Xinference is an inference API for running open-source, speech, and multimodal models. In 2.5.0 and earlier, Xinference passes attacker-influenced Llama3 tool-call output to eval() in xinference/model/llm/tool_parsers/llama3_tool_parser.py and xinference/model/llm/utils.py. Requests to /v1/chat/completions with a tools field flow through xinference/api/restful_api.py, xinference/model/llm/transformers/core.py, handle_chat_result_non_streaming(), and _post_process_completion() before extract_tool_calls() or _eval_llama3_chat_arguments() evaluates the model-generated Python expression. An unauthenticated remote attacker can influence that output through a crafted prompt and execute commands in the Xinference server process context. This issue is fixed in version 2.7.0.

CVE-2026-61539 affects Xinference versions 2.5.0 and earlier, an inference API for open-source and multimodal models. The vulnerability arises from passing attacker-influenced Llama3 tool-call output directly to Python's eval() function in llama3_tool_parser.py and utils.py. Requests to the /v1/chat/completions endpoint with a tools field traverse multiple processing layers before the unsafe eval() call is made. An unauthenticated remote attacker can craft a malicious prompt to influence the model-generated Python expression passed to eval(). This allows arbitrary command execution within the Xinference server process context, representing a critical remote code execution risk. No authentication is required to exploit this vulnerability, significantly broadening the attack surface. The flaw was fixed in Xinference version 2.7.0 via a patch committed to the xorbitsai/inference GitHub repository.