This is a research / demonstration system. It is NOT approved for clinical use and must NOT be used to make real medical decisions. All clinical decisions must be made by qualified healthcare professionals.

If you discover a security vulnerability in this project, please report it responsibly:

1. Do NOT open a public GitHub issue for security vulnerabilities
2. Email the maintainer directly (see GitHub profile for contact info)
3. Include a description of the vulnerability, steps to reproduce, and potential impact

We will acknowledge receipt within 48 hours and aim to provide a fix or mitigation within 7 days for critical issues.

• Authentication/authorization bypasses (if auth is added in the future)
• Injection vulnerabilities (prompt injection, SQL injection, command injection)
• Sensitive data exposure (API keys, patient data leakage)
• Dependency vulnerabilities in requirements.txt or package.json
• CORS misconfigurations that could enable data exfiltration

• Clinical accuracy of AI-generated recommendations (this is a known limitation, not a vulnerability)
• Denial of service via expensive LLM calls (known limitation of the architecture)
• Issues in third-party services (Google AI Studio, OpenFDA, RxNorm)

• This system processes clinical text that could contain protected health information (PHI)
• No real patient data should ever be used with this demonstration system
• In a production deployment, HIPAA compliance would require: encrypted storage, audit logging, access controls, and BAAs with all third-party services
• The MedGemma model can be self-hosted on-premises to avoid sending data to external APIs

• API keys/tokens (HuggingFace token, Google AI Studio key) are stored in .env (gitignored)
• Never commit .env or any file containing API keys or tokens
• The .env.template file shows required variables without actual values

• Prompt injection: The system processes untrusted user input (patient text) that is sent to the LLM. Adversarial inputs could potentially manipulate LLM behavior.
• Hallucination: The LLM may generate plausible but incorrect medical information. The conflict detection step and RAG grounding mitigate but do not eliminate this risk.
• Over-reliance: The system is designed as decision support, not decision making. UI disclaimers and caveats are included to reinforce this.