2 min readfrom Machine Learning

Best models for generating red-team attacks? Also looking for public datasets [R]

Hi everyone, I'm currently working on a framework to evaluate the security of LLM applications and AI agents, and I've been stuck on one part for a while.

Most red-teaming frameworks rely on an LLM to generate adversarial prompts. My question is more about which model to use.

  • Which closed-source models would you recommend for generating high-quality attacks?
  • Which open-source models have worked well for you?
  • Have you noticed any models that consistently generate more realistic or challenging attacks than others?

I'm looking for models that can generate attacks such as Toxicity, prompt injection, SQL injection, jailbreaks, indirect prompt injection, prompt leakage, tool misuse, multi-turn attacks, and other agent-specific attacks ect...

I also have another question.

Is there a good public dataset that people use to benchmark or validate the security of AI agents? I'd prefer a "golden" dataset with predefined, high-quality attacks rather than generating everything from scratch.

I'm curious about what people actually use in practice if you've worked on LLM security or red teaming, I'd really appreciate any recommendations, whether it's models, datasets, papers, or GitHub repositories.

Thanks in advance! Any advice or insights would be greatly appreciated.

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