Papers with AgentHarm

2 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.
SafeMCP: Proactive Power Regulation for LLM Agent Defense via Environment-Grounded Look-Ahead Reasoning (2026.acl-long)

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Challenge: Large Language Model (LLM) agents are expanding their action spaces to operate in complex environments.
Approach: They propose a server-side defense plugin that constrains tool acquisition via predictive reasoning regarding future safety risks.
Outcome: Experiments on PowerSeeking Bench, ToolEmu, and AgentHarm show that SafeMCP achieves a safe equilibrium, effectively mitigating risks while preserving agent utility.

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