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🔒 Security Policy

🛡️ Supported Versions

Version Supported
1.3.x ✅ Yes
1.2.x ✅ Yes
1.1.x ✅ Yes
1.0.x ✅ Yes

🚨 Reporting a Vulnerability

We take security seriously! 🐙 If you discover a security vulnerability in this project, please report it responsibly.

How to Report

  1. DO NOT open a public GitHub issue for security vulnerabilities
  2. Instead, email us at: security@dubsopenhub.com
  3. Or use GitHub's private vulnerability reporting

What to Include

Please provide as much of the following as possible:

  • 📝 Description of the vulnerability
  • 🔄 Steps to reproduce
  • 💥 Potential impact
  • 💡 Suggested fix (if you have one)

What to Expect

  • ⏱️ Acknowledgment within 48 hours
  • 🔍 Assessment within 1 week
  • 🛠️ Fix or mitigation as quickly as possible
  • 🎉 Credit in the release notes (unless you prefer anonymity)

🔐 Security Features

This repository has the following GitHub security features configured:

Feature Status Notes
✅ Dependabot Alerts Enabled Monitors dependencies for known vulnerabilities
✅ Dependabot Security Updates Enabled Auto-creates PRs to fix vulnerable dependencies
✅ Secret Scanning Enabled Detects accidentally committed secrets
✅ Secret Scanning Push Protection Enabled Blocks pushes containing secrets
✅ Code Scanning (CodeQL) Available Static analysis for security bugs

📋 Best Practices

Since this is a Copilot CLI skill (no runtime code, only markdown instructions), the primary security considerations are:

  • 🔑 No secrets in skill files - SKILL.md and agent.md should never contain API keys, tokens, or credentials
  • 📜 Safe instructions - Skill instructions should never instruct the agent to bypass security controls
  • 🔍 Dependency awareness - If dependencies are added in the future, keep them updated

🛡️ Prompt Injection Mitigation

Since this skill orchestrates multiple AI models and processes user-provided task descriptions, prompt injection is a relevant concern:

  • 🔒 Sealed judging - Judge models receive anonymized submissions with model fingerprints stripped, reducing the attack surface for identity-based manipulation
  • 🧹 Input sanitization - The SKILL.md includes anti-gaming protections: calibration anchors, keyword stuffing detection, test tampering scans, and prompt injection scans
  • 🚫 No credential passthrough - User input is used as task descriptions only; it is never interpolated into system-level commands or used to access external services
  • ⚖️ Consensus scoring - Even if one judge model is influenced by injected content, the median-of-3 consensus mechanism limits the impact on final scores

📄 License

This project is licensed under the MIT License.