Third-party cyber evaluations involving OpenAI models
openai.com·14h ago
AI Research
arXiv CS.AI·Jun 10·5 min read
Toward Secure LLM Agents: Threat Surfaces, Attacks, Defenses, and Evaluation
TL;DR
Large language model (LLM) agents face new security risks as they evolve from chatbots to autonomous software components. A comprehensive review of 247 papers was conducted to identify threat surfaces, attack types, and defense strategies.
✦ Why It Matters
Engineers can enhance LLM security by implementing stronger defenses and realistic evaluation practices based on these findings.
Key Takeaways
- Identify and mitigate prompt injection vulnerabilities in LLM applications.
- Establish clear trust boundaries for LLM agent interactions.
- Implement principled privilege controls to limit agent capabilities.
- Use realistic evaluation practices to assess long-term security risks.
How It Works
The authors propose a lifecycle-based framework that models LLM agent security by examining the interactions between information flow, delegated authority, and persistent state. This approach helps categorize various attack vectors and defense mechanisms, providing a structured way to analyze security risks.