We release patches for security vulnerabilities in the following versions:

Please do not report security vulnerabilities through public GitHub issues.

Instead, please report them via email to: security@automotive-claude-code.ai

You should receive a response within 48 hours. If for some reason you do not, please follow up via email to ensure we received your original message.

Please include the following information in your report:

• Type of vulnerability
• Full paths of source file(s) related to the vulnerability
• Location of the affected source code (tag/branch/commit or direct URL)
• Any special configuration required to reproduce the issue
• Step-by-step instructions to reproduce the issue
• Proof-of-concept or exploit code (if possible)
• Impact of the issue, including how an attacker might exploit it

1. Acknowledgment: We will acknowledge receipt of your vulnerability report within 48 hours
2. Assessment: We will assess the vulnerability and determine its impact and severity
3. Fix Development: We will develop a fix for the vulnerability
4. Disclosure: We will coordinate disclosure with you

• Initial Response: Within 48 hours
• Severity Assessment: Within 7 days
• Fix Development: Varies based on complexity
• Public Disclosure: After fix is released (coordinated with reporter)

• Never commit API keys to version control
• Store API keys in .env files (never commit .env)
• Use environment variables for sensitive configuration
• Rotate API keys regularly
• Use separate keys for development, staging, and production

# Good - using environment variables
export ANTHROPIC_API_KEY="your-key-here"

# Bad - hardcoding in code
api_key = "sk-ant-1234..."  # DON'T DO THIS

• Run containers as non-root user (we do this by default)
• Don't expose unnecessary ports
• Use secrets management for sensitive data
• Keep base images updated
• Scan images for vulnerabilities

• Use HTTPS for all API communications
• Validate SSL certificates
• Don't disable certificate verification
• Use VPN for sensitive development work

• All code must go through pull request review
• Security-focused review for authentication/authorization code
• Check for injection vulnerabilities (SQL, command, etc.)
• Validate input/output handling
• Review dependency changes

• Keep dependencies up to date
• Review dependency security advisories
• Use pip audit or safety check regularly
• Pin dependency versions in production
• Avoid dependencies with known vulnerabilities

• Write security tests for sensitive functionality
• Test input validation thoroughly
• Test authentication and authorization
• Perform security scanning in CI/CD

The platform uses LLM APIs which can be susceptible to prompt injection attacks. We mitigate this by:

• Sanitizing user inputs before sending to LLMs
• Using separate system and user message contexts
• Validating LLM outputs before execution
• Implementing rate limiting

• No sensitive data should be sent to LLM APIs
• User code is processed locally when possible
• API keys and credentials are filtered from LLM prompts
• Logs are sanitized to remove sensitive information

• Validate all CAN messages before transmission
• Implement message authentication where supported
• Use network segmentation for security-critical CAN networks
• Monitor for anomalous CAN traffic

• All safety-critical code must undergo additional review
• Use formal verification where applicable
• Implement comprehensive testing
• Follow ISO 26262 guidelines

• All external inputs are validated
• YAML/JSON parsing uses safe loaders
• File path validation prevents directory traversal
• Command execution is sandboxed

• API key validation on startup
• No hardcoded credentials in codebase
• Support for external secret managers

• Sensitive data is redacted from logs
• Audit logging for security-relevant events
• Log rotation to prevent disk exhaustion

• API call rate limiting
• Resource usage limits
• Timeout protections

We use the following security tools:

• Bandit: Python code security scanner
• Safety: Dependency vulnerability scanner
• Trivy: Container image scanner
• Pre-commit hooks: Prevent secret commits
• Detect-secrets: Secret detection

This project aims to support:

• ISO 26262: Functional safety for automotive systems
• ASPICE: Automotive SPICE process model
• MISRA C: Coding standards for safety-critical systems

While the platform itself is not certified, it provides tools to help with:

• Safety analysis and documentation
• Compliance reporting
• Traceability management
• Code quality validation

We will update this security policy as needed. Check back regularly for updates.

Security advisories will be published:

• On GitHub Security Advisories
• In the CHANGELOG.md
• Via email notification (for critical issues)

• Security Issues: security@automotive-claude-code.ai
• General Questions: info@automotive-claude-code.ai
• GitHub Issues: For non-security bugs only

We thank the security research community for their contributions to making this project more secure.

Contributors who responsibly disclose security vulnerabilities will be acknowledged here (with their permission).

Last updated: 2024-03-19