Translation in progress Some entries still include text in the original language.
Oct 12 · Monday
Another 247 papers do not have category tags yet and are not included in filtered results.
“
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.
提出权威链劫持与轨迹引导策略演化，劫持搜索 Agent 的证据链
Published Aug 5
Evaluated models Qwen3.5-9B , Gemma4-31B , DeepResearch and others7 Abstract 论文揭示了多步搜索智能体在受限中间人操纵下的脆弱性，提出 ACH 策略与 TGSE 演化方法并完成系统验证。
提出多智能体流水线的边界攻击，使对抗内容作为可信输入跨层传播
Published Aug 1
Evaluated models GPT-5-mini , Claude Sonnet 4.5 , Kimi K2.5 Abstract 缺边界校验使对抗内容跨智能体传播，作者称这是架构属性而非模型能力。作者把多智能体 LLM 流水线的对抗问题定义为缺少边界校验，并用生产轨迹和受控实验检查脆弱性来自架构还是骨干模型。中间输出未经验证就传给下游。作者认为，一旦某级接受对抗内容，后续智能体会把它当作可信输入。
对开源视觉购物智能体实施商品图对抗攻击并诱导买错
Published Mar 17
Evaluated models LLaVA-v1.5-7B , Qwen2.5-VL-7B Abstract 作者称 LLaVA 易被三种攻击诱导买错，Qwen 正确购买率仍接近其干净条件。作者在自建电商环境里比较两个开源 VLM 购物智能体对图像对抗扰动的鲁棒性。
You have reached the end