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VeriHarm-Bench: Measuring Harmful Success in LLM Coding Agents Verifiably

Paper recorded by Signals 4 on 2026-11-02 in cs.CL. Abstract reproduced from arXiv; link to the original below.

Published 2026-11-02 on arXiv · recorded by Signals 4 on 2026-11-03

Category: cs.CL · 自然语言处理 · first seen 2026-11-03

Abstract

Large 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. This f

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#13 most recent of 628 cs.CL papers we have recorded · ↑ newer: NitiBench+: A Temporal Thai Legal Question Answering Benchmark Across · ↓ older: VIAS: Value-Isolating Activation Steering for Multi-Value Alignment an
Cite this page: VeriHarm-Bench: Measuring Harmful Success in LLM Coding Agents Verifiably: the #13 most recent of 628 cs.CL papers we have recorded (as of 2026-11-02). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/veriharm-bench-measuring-harmful-success-in-llm-coding-agents-verifiably.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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