multi_mcp

The project demonstrates good security practices in its core codebase, including Pydantic-based input validation, secure path resolution (`Path.resolve().relative_to()`), and checks for file size/type before embedding for LLMs. The prompts explicitly guide the AI towards security-focused analysis (OWASP Top 10, adversarial mindset). The primary risks are inherent to external LLM interactions (hallucinations, misuse of generated code) and the configuration of CLI models. While the `_execute_cli_model` uses `subprocess.Popen` for CLI models, `cli_command`, `cli_args`, and `cli_env` are configured by the server administrator in `config/models.yaml`, not directly from user input. This shifts potential command injection risks to configuration management rather than code vulnerability, assuming the `models.yaml` is trusted and secure. The system is designed to *detect* security issues in target code, showing a strong security awareness.