agent/papers/items/2026-2607-01793-safety-testing-llm-agents-at-scale-from-risk-discovery-to-evidence-grounded-veri.md

Paper: Safety Testing LLM Agents at Scale: From Risk Discovery to Evidence-Grounded Verification

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type: paper title: "Safety Testing LLM Agents at Scale: From Risk Discovery to Evidence-Grounded Verification" authors: Yunhao Feng, Ruixiao Lin, Ming Wen, Qinqin He, Yanming Guo, Yifan Ding, Yutao Wu, Jialuo Chen, et al. year: 2026 venue: arXiv url: https://arxiv.org/abs/2607.01793 code_url: source: arxiv collected_at: 2026-07-08 published_at: 2026-07-02 updated_at: 2026-07-04 status: queued relevance: high topics:

• agent-evaluation
• agent-safety
• coding-agent
• rag
• reasoning
• tool-use methods:

benchmarks:

models:

datasets:

• cs.AI related_concepts:

related_jobs:

related_experiments:

related_projects:

collection_score: 18 collection_queries: llm-agent

One-line Takeaway

Auto-collected from arXiv because it matched the Agent collection queries. Needs human skim.

Why Collected

• matched queries: llm-agent
• inferred topics: agent-evaluation, agent-safety, coding-agent, rag, reasoning, tool-use
• arXiv categories: cs.AI
• collection score: 18

Review Checklist

• Does this paper directly inform Agent architecture, evaluation, memory, tools, safety, coding agents, GUI/browser agents, or multi-agent workflows?
• Does it include a benchmark, dataset, code, or reproducible experimental setup?
• Should it be promoted from queued to skimmed or summarized?

Links

• arXiv: https://arxiv.org/abs/2607.01793