This is a curated list repository and doesn't contain executable code. However, we take the security and accuracy of the information we provide seriously.

If you discover a security vulnerability in any of the tools listed in this repository, please report it to the respective project maintainers directly.

If you find security-related issues with this repository itself (such as malicious links, phishing attempts, or compromised resources), please report them to us:

1. Email: pavan4devops@gmail.com
2. Subject Line: [SECURITY] Brief description of the issue
3. Include:
   • Description of the security issue
   • Steps to reproduce (if applicable)
   • Potential impact
   • Any suggested fixes

• Acknowledgment: We'll acknowledge your report within 48 hours
• Investigation: We'll investigate and validate the issue
• Action: We'll take appropriate action (remove malicious links, update information, etc.)
• Credit: We'll credit you in our security acknowledgments (unless you prefer to remain anonymous)

When contributing to this repository:

1. Verify Links: Ensure all links point to legitimate, official sources
2. Check HTTPS: Prefer HTTPS links over HTTP
3. Avoid Shortened URLs: Use full URLs for transparency
4. Verify GitHub Repos: Ensure GitHub links point to official repositories
5. Check for Typosquatting: Be careful of similar-looking domain names
6. Review Badges: Ensure badge URLs are from trusted sources

When using tools from this list:

1. Do Your Research: Always research tools before using them in production
2. Check Dependencies: Review dependencies for security vulnerabilities
3. Read Documentation: Understand security implications of each tool
4. Use Official Sources: Download/install from official sources only
5. Keep Updated: Use the latest stable versions
6. Review Permissions: Understand what permissions tools require
7. Audit Code: For critical applications, audit open-source code
8. Follow Best Practices: Implement security best practices for LLMOps:
   • Secure API keys and credentials
   • Implement rate limiting
   • Use encryption for sensitive data
   • Monitor for prompt injection attacks
   • Validate and sanitize inputs
   • Implement access controls

Be aware of these common security issues when working with LLMs:

• Malicious prompts that manipulate model behavior
• Mitigation: Use input validation and guardrails

• Models may expose training data or sensitive information
• Mitigation: Implement data filtering and access controls

• Compromised training data affecting model behavior
• Mitigation: Validate training data sources

• Leaked API keys leading to unauthorized access
• Mitigation: Use environment variables and secrets management

• Resource exhaustion through excessive requests
• Mitigation: Implement rate limiting and monitoring

• Vulnerable packages in the dependency chain
• Mitigation: Regular security audits and updates

Refer to our Security & Safety section for tools that help secure LLM applications.

• OWASP Top 10 for LLM Applications
• NIST AI Risk Management Framework
• Microsoft Responsible AI Guidelines

We follow responsible disclosure practices:

1. Private Disclosure: Report security issues privately first
2. Investigation Period: Allow time for investigation and fixes
3. Public Disclosure: Announce fixes after they're implemented
4. Credit: Acknowledge security researchers who report issues

We'll post security-related updates in:

• GitHub Security Advisories
• Repository Issues (tagged with security)
• README updates

For security concerns: pavan4devops@gmail.com

For general questions: Open an issue on GitHub

Last Updated: January 2026

Thank you for helping keep Awesome LLMOps and the community safe! 🔒