We actively support security updates for the following versions of CodeCodePrism:

Note: As this is an AI-generated project, security patches are implemented by our AI developer with human oversight for critical vulnerabilities.

We take security seriously! If you discover a security vulnerability in CodeCodePrism, please help us resolve it responsibly.

For security vulnerabilities, please do NOT create public GitHub issues.

Instead, report security issues through one of these channels:

• Use GitHub's [Private Vulnerability Reporting](https://github.com/rustic-ai /codeprism/security/advisories/new)
• This provides a secure channel for disclosure and coordinated response

Please provide as much detail as possible:

**Vulnerability Type:**
- [ ] Code injection
- [ ] Path traversal
- [ ] Privilege escalation
- [ ] Information disclosure
- [ ] Denial of service
- [ ] Other: ___________

**Affected Component:**
- [ ] codeprism-mcp-server
- [ ] codeprism-core parser
- [ ] Language parsers (JS/Python)
- [ ] MCP protocol implementation
- [ ] Other: ___________

**Severity Assessment:**
- [ ] Critical (Remote code execution, data breach)
- [ ] High (Privilege escalation, authentication bypass)
- [ ] Medium (Information disclosure, DoS)
- [ ] Low (Minor information leak)

**Environment:**
- CodeCodePrism version: [e.g., 0.1.0]
- OS: [Linux/macOS/Windows]
- Rust version: [e.g., 1.82.0]
- Usage context: [MCP server, CLI, library]

**Description:**
[Detailed description of the vulnerability]

**Steps to Reproduce:**
1. [Step 1]
2. [Step 2]
3. [Step 3]

**Impact:**
[What could an attacker accomplish?]

**Suggested Fix:**
[If you have ideas for remediation]

**Additional Context:**
[Any other relevant information]

We aim to respond to security reports promptly:

Complex vulnerabilities may require additional time with regular updates provided.

Unique Security Approach:

Since CodeCodePrism is AI-generated, our security process involves:

1. AI Developer Analysis: Initial vulnerability assessment and fix generation
2. Human Security Review: Critical review of AI-generated security fixes
3. Combined Testing: Both automated and human-verified security testing
4. Coordinated Response: Human oversight ensures proper disclosure timing

This hybrid approach ensures the speed of AI development with the rigor of human security expertise.

We maintain a public acknowledgment of security researchers who responsibly disclose vulnerabilities:

• Hall of Fame: Public recognition in our security documentation
• GitHub Profile: Special contributor badge (when available)
• Project Credits: Acknowledgment in release notes and project documentation

We're exploring a bug bounty program for:

• Critical vulnerabilities: Significant rewards for severe issues
• Novel attack vectors: Extra recognition for creative discoveries
• AI-specific vulnerabilities: Special focus on AI-generated code security

We're interested in vulnerabilities affecting:

• MCP Server: JSON-RPC implementation, protocol handling
• Code Parsers: JavaScript, TypeScript, Python parsing vulnerabilities
• Graph Engine: AST processing and graph construction
• File System Access: Repository scanning and file reading
• Memory Safety: Rust memory safety violations

• Malicious Repositories: Crafted code that exploits parsing
• MCP Protocol Abuse: JSON-RPC injection or manipulation
• Resource Exhaustion: DoS through excessive resource consumption
• Path Traversal: Unauthorized file system access
• Code Injection: Through analysis of malicious code

The following are generally not considered security vulnerabilities:

• Feature Requests: Suggestions for new functionality
• Performance Issues: Unless they enable DoS attacks
• Analysis Accuracy: Incorrect code analysis results
• Documentation Issues: Errors in documentation
• Third-party Dependencies: Issues in upstream libraries (report to them directly)
• Social Engineering: Attacks against users, not the software

• Run CodeCodePrism in isolated environments when analyzing untrusted code
• Use appropriate firewall rules for MCP server deployments
• Monitor resource usage to detect potential DoS attacks

• Limit repository access to necessary directories only
• Use least-privilege principles for service accounts
• Regular security audits of deployment configurations

• Ensure sensitive repositories are not exposed through MCP
• Regular backup of analysis data if stored persistently
• Encrypt sensitive configuration and communication channels

• Validate repository paths and file inputs
• Sanitize data passed to CodeCodePrism APIs
• Implement appropriate error handling

• Set reasonable timeouts for analysis operations
• Monitor memory and CPU usage
• Implement graceful degradation for large repositories

We maintain transparency about our security posture:

• Open Vulnerabilities: Currently disclosed but unpatched
• Patched Vulnerabilities: Historical security fixes
• Response Times: Average time to fix security issues
• Security Releases: Dedicated security patch releases

• Dependency Scanning: Regular checks for vulnerable dependencies
• Static Analysis: Automated security code review
• Fuzzing: Automated testing with malformed inputs
• CI/CD Security: Security checks in our build pipeline

• Periodic Security Audits: Regular manual security reviews
• Threat Modeling: Analysis of potential attack vectors
• Penetration Testing: External security testing (when resources allow)

We classify security incidents as:

1. Critical: Active exploitation, data breach, RCE
2. High: Privilege escalation, authentication bypass
3. Medium: Information disclosure, DoS
4. Low: Minor security improvements

1. Detection: Automated monitoring or manual reporting
2. Assessment: Rapid classification and impact assessment
3. Containment: Immediate measures to limit damage
4. Investigation: Root cause analysis by AI + human team
5. Remediation: Fix development and testing
6. Recovery: System restoration and monitoring
7. Communication: Coordinated disclosure and user notification

• Response Team: AI Developer + Human Security Oversight
• GitHub Security: [Private Vulnerability Reporting](https://github.com/rustic-ai /codeprism/security)

We appreciate the security research community's efforts to keep CodeCodePrism secure. Responsible disclosure helps us maintain a secure project for all users.

Together, we're proving that AI-generated code can meet the highest security standards through collaboration between artificial and human intelligence.

"Security is not just about protecting code—it's about protecting the trust users place in AI-generated software." - CodeCodePrism Security Team, 2024