agent/papers/items/2026-2605-07830-cybiasbench-benchmarking-bias-in-llm-agents-for-cyber-attack-scenarios.md

Paper: CyBiasBench: Benchmarking Bias in LLM Agents for Cyber-Attack Scenarios

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type: paper title: "CyBiasBench: Benchmarking Bias in LLM Agents for Cyber-Attack Scenarios" authors: Taein Lim, Seongyong Ju, Munhyeok Kim, Hyunjun Kim, Hoki Kim year: 2026 venue: arXiv url: https://arxiv.org/abs/2605.07830 code_url: source: arxiv collected_at: 2026-07-08 published_at: 2026-05-08 updated_at: 2026-05-08 status: queued relevance: high topics:

• agent-evaluation
• agent-safety methods:

benchmarks:

models:

datasets:

• cs.CR
• cs.AI related_concepts:

related_jobs:

related_experiments:

related_projects:

collection_score: 16 collection_queries: autonomous-agent-llm

One-line Takeaway

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

Why Collected

• matched queries: autonomous-agent-llm
• inferred topics: agent-evaluation, agent-safety
• arXiv categories: cs.CR, cs.AI
• collection score: 16

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/2605.07830