Richie Lee - LLM Security 101 - An Introduction to AI Red Teaming | PyData Amsterdam 2024

LLM security involves three main risk categories: misalignment (model functionality issues), identity security (authorization/authentication), and LLM-specific security risks

Key security concerns include:

• Information leakage of sensitive data
• Prompt injection attacks
• Data exfiltration
• Jailbreaking attempts
• Indirect fault injections
• Authorization and access control issues

AI Red Teaming is crucial for evaluating LLM security posture:

• Systematic testing approach
• Simulates malicious actor behavior
• Can be automated using tools like Microsoft’s Pirate
• Tests both security and harmful content risks
• Helps identify vulnerabilities iteratively

Risk assessment should consider:

• Likelihood of security incidents
• Severity of potential impact
• Data security implications
• Reputation damage potential
• Compliance requirements

Practical security measures include:

• Implementing private endpoints
• Data encryption and isolation
• Rate limiting
• Access controls
• Monitoring and logging
• Regular security testing

LLM security requires cross-disciplinary collaboration between:

• Security teams
• Data/AI practitioners
• Compliance teams
• External vendors and security partners

The threat landscape is evolving rapidly as:

• Attack techniques become more sophisticated
• New vulnerabilities are discovered
• Use cases expand through plugins and integrations
• Attackers leverage automation and other LLMs