# Security Learnings from LLM Production Deployments at NVIDIA ## Overview This case study presents insights from NVIDIA's product security and AI red team's experience in building, breaking, and securing LLM integrations in production environments. The presentation focuses on practical security considerations and real-world challenges encountered when deploying LLMs in production systems. ## Understanding LLM Behavior ### Core LLM Characteristics - LLMs generate probability distributions over tokens rather than direct outputs - Each forward pass produces probabilities that must be sampled to generate text - Random sampling introduces non-deterministic behavior - Generation process is unidirectional and cannot be reversed - Models don't truly "reason" but rather make statistical predictions ### Key Production Challenges - Models don't work as users might expect or wish - Code/data confusion leads to prompt injection vulnerabilities - Limited knowledge based on training data - Fine-grained access control difficulties - Need for external conversation history management ## RAG Systems Security ### Common RAG Vulnerabilities - Data poisoning through malicious document insertion - Incorrect permission boundaries leading to data leakage - Security through obscurity failures when relying on link sharing - Information leakage through guard rail refusals - Logging systems exposing sensitive data ### RAG Security Recommendations - Implement strict access controls on RAG data stores - Properly secure and permission all document sources - Avoid relying solely on guard rails for security - Carefully consider logging strategies and access controls - Educate users about proper data handling in RAG systems ## Plugin Architecture Security ### Plugin Vulnerabilities - SQL injection through prompt injection - Server-Side Request Forgery (SSRF) in URL handling - Remote Code Execution through Python plugins - Excessive plugin permissions - Data exfiltration through active content rendering ### Plugin Security Best Practices - Parameterize plugin interfaces - Implement aggressive input validation - Restrict plugin permissions - Sandbox execution environments - Limit active content rendering capabilities ## Production Deployment Considerations ### Input Vector Management - Consider all potential input sources: ### Data Flow Security - Track data movement through the system - Monitor logging and storage locations - Consider potential exfiltration paths - Implement least privilege access - Minimize output exposure ## Key Security Principles ### Traditional Security Applications - Identify trust and security boundaries - Trace data flows - Apply least privilege principle - Implement output minimization ### LLM-Specific Considerations - Account for probabilistic/fuzzy outputs - Address adversarial control possibilities - Manage tainted data propagation - Isolate sensitive information from LLM access ## Implementation Guidelines ### RAG Implementation Security - Treat all external data as potentially hostile - Implement proper document permission boundaries - Carefully consider logging strategies - Monitor for poisoning attempts - Regular security audits of data stores ### Plugin Architecture Safety - Implement strict sandboxing - Careful API access control - Input validation at multiple levels - Regular security testing - Monitor for abuse patterns ### Monitoring and Logging - Balance operational needs with security - Implement secure logging practices - Consider privacy implications - Regular access review - Audit trail maintenance ## Lessons Learned ### Key Takeaways - Traditional security principles still apply but need adaptation - Attack surface is significantly different from traditional applications - Probabilistic nature of LLMs introduces new security challenges - Need for comprehensive security strategy across all components - Importance of user education and clear security boundaries ### Future Considerations - Evolving threat landscape - Need for better security tools - Importance of continued research - Balance between functionality and security - Regular security assessment and updates