CVE-2026-61539

### Summary 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. ### Details 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()`: ```python 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: ```python __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. ### Score 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: - AV:N: the vulnerable API is remotely reachable over the network; - AC:L: exploitation only requires a crafted chat-completion request and tool-call parameter; - PR:N: the tested default configuration did not require authentication; - UI:N: no user interaction is required; - S:C: command execution can affect resources beyond the Xinference application boundary; - C:H/I:H/A:H: remote code execution can fully compromise confidentiality, integrity, and availability. ### Credit This vulnerability was discovered by: - XlabAI Team of Tencent Xuanwu Lab ([email protected]) - Atuin Automated Vulnerability Discovery Engine - Guannan Wang ([email protected]), Zhanpeng Liu ([email protected]), Guancheng Li ([email protected])

### Summary 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. ### Details 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()`: ```python 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: ```python __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. ### Score 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: - AV:N: the vulnerable API is remotely reachable over the network; - AC:L: exploitation only requires a crafted chat-completion request and tool-call parameter; - PR:N: the tested default configuration did not require authentication; - UI:N: no user interaction is required; - S:C: command execution can affect resources beyond the Xinference application boundary; - C:H/I:H/A:H: remote code execution can fully compromise confidentiality, integrity, and availability. ### Credit This vulnerability was discovered by: - XlabAI Team of Tencent Xuanwu Lab ([email protected]) - Atuin Automated Vulnerability Discovery Engine - Guannan Wang ([email protected]), Zhanpeng Liu ([email protected]), Guancheng Li ([email protected])

Xinference es una API de inferencia para ejecutar modelos de código abierto, de voz y multimodales. En las versiones 2.5.0 y anteriores, Xinference pasa la salida de llamadas a herramientas de Llama3 influenciada por el atacante a eval() en xinference/model/llm/tool_parsers/llama3_tool_parser.py y xinference/model/llm/utils.py. Las solicitudes a /v1/chat/completions con un campo 'tools' fluyen a través de xinference/api/restful_api.py, xinference/model/llm/transformers/core.py, handle_chat_result_non_streaming() y _post_process_completion() antes de que extract_tool_calls() o _eval_llama3_chat_arguments() evalúen la expresión Python generada por el modelo. Un atacante remoto no autenticado puede influenciar esa salida a través de un prompt manipulado y ejecutar comandos en el contexto del proceso del servidor de Xinference. Este problema se ha solucionado en la versión 2.7.0.