用众包竞赛评估AI黑客能力，发现其表现远超预期。
Evaluating AI cyber capabilities with crowdsourced elicitation
- 通过开放竞赛众包提取AI的攻防能力
- AI在两场赛事中分别进入前5%和前10%，获7500美元奖励
- 适合安全机构追踪AI新兴威胁，也可收集人类表现数据
随着人工智能系统能力不断提升，理解其潜在的网络攻击能力对制定明智治理政策至关重要。然而，准确界定其能力边界十分困难，以往评估常严重低估。所谓‘AI诱引’（AI elicitation）即挖掘AI在特定任务中的最大性能，当前多由安全组织内部完成。本文探索将诱引工作外包给众包模式：在两场网络安全竞赛（CTF）中开放AI赛道——AI vs. Humans（400支队伍）与Cyber Apocalypse（8000支队伍）。结果显示，AI团队分别位列前5%和前10%，共赢得7500美元奖金。这一优异表现表明，开放市场诱引可作为内部诱引的有效补充。本文提出设立诱引悬赏机制，以实现对新兴AI能力的及时、低成本态势感知。另一优势是能大规模采集人类表现数据。采用METR方法论分析后发现，AI代理可稳定解决需人类平均一小时内完成的网络安全挑战。
原文摘要 · Abstract (English)
As AI systems become increasingly capable, understanding their offensive cyber potential is critical for informed governance and responsible deployment. However, it's hard to accurately bound their capabilities, and some prior evaluations dramatically underestimated them. The art of extracting maximum task-specific performance from AIs is called "AI elicitation", and today's safety organizations typically conduct it in-house. In this paper, we explore crowdsourcing elicitation efforts as an alternative to in-house elicitation work. We host open-access AI tracks at two Capture The Flag (CTF) competitions: AI vs. Humans (400 teams) and Cyber Apocalypse (8000 teams). The AI teams achieve outstanding performance at both events, ranking top-5% and top-10% respectively for a total of \$7500 in bounties. This impressive performance suggests that open-market elicitation may offer an effective complement to in-house elicitation. We propose elicitation bounties as a practical mechanism for maintaining timely, cost-effective situational awareness of emerging AI capabilities. Another advantage of open elicitations is the option to collect human performance data at scale. Applying METR's methodology, we found that AI agents can reliably solve cyber challenges requiring one hour or less of effort from a median human CTF participant.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。