Finding attack models and public datasets for AI agent security tests
Security testing for AI applications and AI agents needs strong attack examples. Many red-team setups use a to create difficult or unsafe inputs on purpose. The needed recommendations cover both and that can produce realistic, challenging attacks.
The attack types include toxicity, , SQL injection, jailbreaks, , prompt leakage, tool misuse, multi-turn attacks, and other agent-specific attacks. A public benchmark dataset is also needed, so teams do not have to generate every attack from scratch. The ideal option is a golden dataset with predefined, high-quality attacks.
Practical recommendations could include models, datasets, papers, or used by people working on AI security and red teaming.
Key points
- The focus is security testing for AI applications and AI agents.
- Both and are being considered.
- The attack list includes , jailbreaks, tool misuse, and multi-turn attacks.
- A public golden dataset would reduce the need to generate every attack from scratch.
- Useful follow-up sources could include datasets, papers, and .