PyPI · xinference
Xinference vulnerable to remote code execution via unsafe `eval()` in Llama3 tool-call parsing
Xinference used Python's unsafe eval() function when parsing Llama3 tool-call output generated by a large language model. Because the model output can be influenced by attacker-controlled prompts sent to the chat completion API, a remote attacker can craft prompts that cause the model to return a Python expression. Xinference then evaluates that expression on the server while post-processing the tool-call result. In the tested default deployment, authentication was not enabled, so the vulnerability was exploitable by an unauthenticated remote attacker through the /v1/chat/completions endpoint.
Users can interact with deployed models through Xinference's OpenAI-compatible /v1/chat/completions API. The request entry point is implemented in xinference/api/restful_api.py; non-streaming requests call the model instance's chat() method and return the inference result.
When the Transformers backend is used, inference results flow through the batching logic in xinference/model/llm/transformers/core.py. Non-streaming chat results are handled by handle_chat_result_non_streaming(). If the request contains a tools field, Xinference calls _post_process_completion() to parse tool-call output from the model response.
The Llama3 tool-call parser is implemented in xinference/model/llm/tool_parsers/llama3_tool_parser.py. In affected versions, extract_tool_calls() parsed model output with eval():
def extract_tool_calls(
self, model_output: str
) -> List[Tuple[Optional[str], Optional[str], Optional[Dict[str, Any]]]]:
try:
data = eval(model_output, {}, {})
return [(None, data["name"], data["parameters"])]
except Exception:
return [(model_output, None, None)]
The intended behavior was to convert a Python dictionary-like string generated by the model into a dictionary object. However, eval() executes the input as a Python expression, and eval(model_output, {}, {}) is not a security sandbox. If an attacker can influence the model output through prompt injection or direct chat input, the attacker can cause the model to return an expression such as:
__import__('os').system('touch /tmp/hacked')
When the expression reaches eval(), it is executed in the Xinference server process context. The harmless touch /tmp/hacked command can be replaced with other payloads, such as a reverse shell, malware download, sensitive file read, or lateral-movement payload.
Severity: Critical
CVSS v3.1: 10.0
Vector: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:H/A:H
Rationale:
This vulnerability was discovered by:
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Check my repoSources: CISA KEV (public domain), OSV.dev & GitHub Advisory Database (CC-BY-4.0), FIRST EPSS, NVD/CWE (public domain). Served live from the Stateward advisory database.