The widespread adoption of Large Language Models (LLMs) like GPT-4, Claude, and Gemini has introduced unprecedented capabilities and equally unprecedented risks. Organizations are increasingly deploying LLMs to handle sensitive tasks, from processing medical records to analyzing financial documents. This talk examines the evolving landscape of LLM security and privacy, combining theoretical foundations with a walkthrough of example implementations.

Through real-world case studies of both attacks and defenses and practical implementation guidance using popular security tools, we’ll explore critical vulnerabilities and proven defensive techniques. Special attention will be given to securing fine-tuned and domain-specific LLMs, with live examples using NVIDIA’s NeMo Guardrails, LangChain’s security tools, and Microsoft’s guidance library.

Introduction & Context (8 minutes)

• The shifting security paradigm in the LLM era
  • From traditional ML security to language model threats
  • Why traditional security measures fall short
  • High-impact case studies:
    • Samsung code leak analysis
    • Training data extraction incident breakdown
    • Recent jailbreaking techniques in production

Critical Attack Vectors (12 minutes)

• Modern LLM Attack Surface
  • Prompt injection taxonomy and examples
  • Training data extraction techniques
  • Supply chain vulnerabilities in fine-tuning
  • Real attack scenario walkthroughs
• Advanced Threats
  • Model inversion methodologies
  • Social engineering through LLM manipulation
  • Example attack chains from recent incidents
  • Latest research findings (2023-2024)

Defense Strategies and Implementation (12 minutes)

• Comprehensive Security Architecture
  • NVIDIA NeMo Guardrails for input validation and model output control
  • LangChain security tools for securing fine-tuned models
  • Azure AI Safety for output filtering and ensuring model reliability
  • Reference architecture walkthrough: Secure LLM deployment and monitoring
• Runtime Security
  • Monitoring implementation patterns
  • Access control frameworks
  • Example monitoring dashboard review
• Validation Framework
  • Red teaming methodology walkthrough
  • Automated testing approaches
  • Example test suite review

Privacy and Compliance (8 minutes)

• Privacy Architecture
  • Differential privacy implementation patterns
  • Confidential computing via trusted execution engines and secure enclaves
  • Data minimization strategies
  • Reference architecture for regulated industries
• Regulatory Guidelines
  • GDPR & HIPAA requirements for LLMs
  • Documentation requirements
  • Compliance checklist walkthrough

Future Directions and Recommendations (5 minutes)

• Emerging security standards and best practices
  • Key companies and regulatory bodies shaping the future of LLM security
  • Building security-first LLM architectures
  • Key recommendations for secure deployment