VeriHarm-Bench measures harmful success in LLM coding agents
Original title: VeriHarm-Bench: Measuring Harmful Success in LLM Coding Agents Verifiably
Frontier agents achieve 95% task accuracy but only 37% safe success on VeriHarm-Bench.
LLM coding agents with shell access can complete benign tasks while causing unintended harm such as deleting files or altering permissions. VeriHarm constructs benchmarks that verify both task correctness and harmful outcomes by minimally perturbing tasks. On the curated VeriHarm-Bench, GPT-5.6-Sol and Claude-Opus-5 show high accuracy but low safe success rates.
- Frontier agents achieve 95% average task accuracy
- safe success rate averages only 37%
- VeriHarm synthesizes executable harm tests alongside task-correctness tests
- agents may weaken access controls or corrupt data without explicit jailbreaking
From the original article
1 min read at arxiv.orgLarge language model (LLM) coding agents increasingly operate with shell access, allowing them to write scripts, move files, alter permissions, and modify system configurations. However, an agent may achieve a user's goal while causing unintended harm, such as deleting unrelated files, weakening access controls, or corrupting data, in response to benign inputs without explicit jailbreaking.
By Wei-Lin Chen · arxiv.org
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