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Oct 12 · Monday
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About this collection Collection in progress We are building a large AI safety paper collection to help researchers find work related to their interests. Papers are collected automatically andfiltered only for relevance to AI safety , without judging quality, novelty or reading value. The default “Annotated first” shows papers with tags or published paper analyses first. Each group is ordered by original publication date, newest first, with undated papers last. “Newest first” uses publication dates only. This order is not a quality ranking.
One-line summaries, tags and paper analyses are prepared by AI from the papers. Language models may have systematic biases and produce hallucinations.We plan to carry out human annotation and review to reduce errors in this content.
提出 Auto Mode ++,加固编码 Agent 阻断监视器
Published Sep 17
Evaluated models Opus 5 , Opus 4.8 , Opus 4.7 and others8 Abstract 系统评估生产阻断监视器在面对持续恶意智能体时的表现,提出加固防御并指明多上下文防御挑战。
评测开发者能否在协作编程中发现编码 Agent 的隐蔽破坏
Published Jun 4
Evaluated models Claude-Opus-4.6 , GPT-5.4 , Gemini-3.1-Pro and others5 提出 Gram 审计框架,测量编程与研究智能体的破坏倾向
Published May 29
Evaluated models Gemini 2.5 Pro , Gemini 3.0 Pro Preview , Gemini 3.1 Pro Preview 构建 SLEIGHT-Bench 评测代码智能体监控对概念盲点规避的捕获
Published May 16
Evaluated models Claude Opus 4.6 , Claude Opus 4.7 , Claude Sonnet 4.6 and others6 Abstract 评估针对监控盲点的规避基准,发现前沿监控器漏报率高,算力与脚手架虽有提升但存在类别局限。
提出针对可信监控器的自适应提示注入,绕过 AI 控制协议
Published Oct 10, 2025
Evaluated models GPT-4o , GPT-4 , GPT-4.1 and others4 Abstract 作者指出当前依赖 LLM 监控器的 AI 控制协议易被自适应提示注入攻破,呼吁引入多监控器集成与数据流隔离机制。
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