82 lines
3.3 KiB
Markdown
82 lines
3.3 KiB
Markdown
# Security Policy
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## Supported Versions
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| Version | Supported |
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| ------- | ------------------ |
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| 9.x.x | :white_check_mark: |
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| < 9.0 | :x: |
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## Important Disclaimer
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PAL MCP is an open-source Model Context Protocol (MCP) server that acts as middleware between AI clients (Claude Code, Codex CLI, Cursor, etc.) and various AI model providers.
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**Please understand the following:**
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- **No Warranty**: This software is provided "AS IS" under the Apache 2.0 License, without warranties of any kind. See the [LICENSE](LICENSE) file for full terms.
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- **User Responsibility**: The AI client (not PAL MCP) controls tool invocations and workflows. Users are responsible for reviewing AI-generated outputs and actions.
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- **API Key Security**: You are responsible for securing your own API keys. Never commit keys to version control or share them publicly.
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- **Third-Party Services**: PAL MCP connects to external AI providers (Google, OpenAI, Azure, etc.). Their terms of service and privacy policies apply to data sent through this server.
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## Reporting a Vulnerability
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**Please do not report security vulnerabilities through public GitHub issues.**
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### Preferred Method
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Use [GitHub Security Advisories](https://github.com/BeehiveInnovations/pal-mcp-server/security/advisories/new) to report vulnerabilities privately.
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### What to Include
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- Description of the vulnerability
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- Steps to reproduce
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- Affected versions
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- Potential impact
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- Suggested fix (optional)
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### What to Expect
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- We will acknowledge your report and assess the issue
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- Critical issues will be prioritized
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- We'll keep you informed of progress as work proceeds
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We cannot commit to specific response timelines, but we take security seriously.
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### After Resolution
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We welcome security researchers to submit a pull request with the fix. This is an open-source project and we appreciate community contributions to improve security.
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## Disclosure Policy
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We practice coordinated disclosure. Please allow reasonable time to address issues before public disclosure. We'll work with you on timing.
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## Scope
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### In Scope
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- Authentication/authorization bypasses
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- Injection vulnerabilities (command injection, prompt injection with security impact)
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- Information disclosure (API keys, sensitive data leakage)
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- Denial of service vulnerabilities in the MCP server itself
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- Dependency vulnerabilities with exploitable impact
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### Out of Scope
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- Issues in upstream AI providers (report to Google, OpenAI, etc. directly)
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- Issues in AI client software (report to Anthropic, OpenAI, Cursor, etc.)
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- AI model behavior or outputs (this is controlled by the AI client and model providers)
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- Social engineering attacks
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- Rate limiting or resource exhaustion on third-party APIs
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## Security Best Practices for Users
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1. **Protect API Keys**: Store keys in `.env` files (gitignored) or environment variables
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2. **Review AI Actions**: Always review AI-suggested code changes before applying
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3. **Use Local Models**: For sensitive codebases, consider using Ollama with local models
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4. **Network Security**: When self-hosting, ensure appropriate network controls
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5. **Keep Updated**: Regularly update to the latest version for security fixes
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## Recognition
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We appreciate responsible disclosure and will credit security researchers in release notes (unless you prefer anonymity).
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