Papers with AgentHarm-Gen

1 papers
Agent vs. Agent: Automated Data Generation and Red-Teaming for Custom Agentic Workflows (2025.emnlp-industry)

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Challenge: Existing red-teaming frameworks like AgentHarm use static prompts and hardcoded toolsets .
Approach: They propose a red-teaming framework that generates adversarial tasks and evaluation functions tailored to arbitrary toolsets and uses iterative prompt refinement with self-reflection to develop more effective attacks.
Outcome: The proposed approach achieves 162% increase in attack success rate on o4-mini and 86% success on gemini 2.5 Pro.

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