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AgentHarm

Safety / Agents

AgentHarm measures how far a tool-using agent will go in carrying out a harmful request. It pairs 110 harmful behaviours with 110 matched benign ones, so over-refusal is measured alongside compliance.

agentharm

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Official source

Overview only: problem text has not been published on this site; the catalogue records the benchmark introduction, category and official source.

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Reference links

Issues

Dataset discussions

Paper

arXiv:2410.09024

Dataset

Source dataset

At a glance

Metric: harm score and refusal rate, with the benign pairs measuring over-refusal
Size: 110 harmful and 110 benign behaviours in the source; none imported.
License: other (declared on the Hugging Face dataset card)
Released: Dataset published on Hugging Face 2024-10
Maintainer: ai-safety-institute (source organisation)
Answer access: No problem text is imported, so there is no answer storage for this entry. The source distributes the answers under its own terms.

What a problem looks like

Multi-step agent tasks with a set of synthetic tools, each paired with a benign counterpart.

How it is scored

Scored by a grading function per behaviour against the agent transcript; nothing is imported here.

Metric: harm score and refusal rate, with the benign pairs measuring over-refusal

Why it is hard

A single refusal is not enough: the agent has to keep refusing across a multi-step trajectory, and a jailbreak that survives one step often carries through the rest.

Cite

@article{andriushchenko2024agentharm,
  title={AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents},
  author={Maksym Andriushchenko and Alexandra Souly and Mateusz Dziemian and Derek Duenas and Maxwell Lin and Justin Wang and Dan Hendrycks and Andy Zou and Zico Kolter and Matt Fredrikson and Eric Winsor and Jerome Wynne and Yarin Gal and Xander Davies},
  journal={arXiv preprint arXiv:2410.09024},
  year={2024}
}

Discussion

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