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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.
提出利用路由元数据推断敏感话题的侧信道攻击
Published Oct 7
Evaluated models RouteLLM (BERT router) , notdiamond-0001 , Vela (vLLM Semantic Router) Abstract 论文揭示并测量了 LLM 路由决策在无文本日志中的敏感话题泄露，验证了按类别公平性防御的有效性与边界。
提出利用共享冷却的跨租户干扰攻击，并设计租户隔离机制
Published Oct 4 Abstract 揭示网关共享冷却漏洞，提出来源检查与租户隔离防御。
提出 MIRROR，用多路径摘要多数表决保障多智能体通信完整性
Published Oct 2
Evaluated models Gemma-4-31B-it , Gemini 2.5 Pro Abstract MIRROR 靠多数路径。MIRROR 是 LLM 多智能体通信层上的多路径法定人数完整性原语，只处理不透明字节，不解释消息内容。要处理的是 Agent-in-the-Middle：中介合法终结 TLS 后可改在途消息，而语义校验有推理开销和误拦，签名又要求私钥管理。做法是生成结束后复制同一规范化载荷。
提出只训练或篡改多智能体潜在通信链路来提高有害遵从
Published Sep 30
Evaluated models Llama-3.2-3B-Instruct , Qwen2.5-3B-Instruct , Gemma-3-1B-IT and others7 Abstract 潜在链路本身即可改变冻结智能体系统的安全，奖励既能攻击也能修复。学习得到的潜在通信链路会改变由冻结、对齐智能体组成的系统是否遵从有害请求。 。作者研究三种拓扑：双智能体、三级顺序协作，以及双专家加汇总。
提出 SYNCHAIN，诱导计算机使用 Agent 自建跨任务持久攻击链
Published Aug 7
Evaluated models Qwen3.5-9B , Llama-3.1-8B , Ministral-3-8B Abstract 论文揭示了 CUA 自合成工件带来的内部供应链威胁，验证了潜伏载荷的跨任务传播能力与防御局限。
Propose a structured JSON backdoor that triggers malicious actions in robot control via a poisoned LoRA
Published Apr 5
Evaluated models Llama-3.1-8B-Instruct , Gemma-2-9B-IT , DeepSeek-R1-Distill-Llama-8B and others4 为去中心化 LLM 推理网络提出稳健成本感知 PoQ，抵御恶意评估者操纵分数
Published Jan 29
Evaluated models llama 3 2 3b , gemma 2 2b it , tinyllama 1 1b and others10 Abstract 成本感知 PoQ 加入稳健共识。这篇扩展工作把成本感知 Proof of Quality 的共识层改成能承受恶意评估分数的形式，奖励仍同时看输出质量与延迟成本。
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