Translation in progress Some entries still include text in the original language.
Oct 12 · Monday
Another 232 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 S³，用 guard agent 编排阶段安全技能防御智能体多阶段风险
Published Aug 3
Evaluated models DeepSeek-V4-Pro , DeepSeek-V4-Flash Proposes DAERT, which uses simulated failures as reward to generate diverse adversarial instructions, exposing VLA language vulnerabilities.
Published Apr 7
Evaluated models π0 , OpenVLA , OpenVLA-7B and others5 Propose SABER, which uses a reinforcement learning attacker to rewrite instructions within a budget to cause VLA task failure.
Published Mar 26
Evaluated models π0-LIBERO , π0.5 , X-VLA and others6 Propose Blindfold, jailbreaking embodied LLMs with action-level manipulation
Published Mar 2
Evaluated models Llama-3.1-8B , DeepSeek-R1-14B , Gemma-3-27B and others8 Abstract Blindfold jailbreaks embodied LLMs with action-level proxy planning, and the authors claim it can bypass semantic checks and is effective in both simulation and real-world settings.Blindfold is a no-box attack against embodied LLM planning modules, rewriting malicious intent into seemingly benign action sequences.
提出 PINA，迭代优化针对导航智能体的提示注入
Published Jan 20
Evaluated models GPT-3.5-turbo , GPT-4 , Balcı et al. [2] Abstract PINA 在黑盒下优化导航提示注入。PINA 是面向基于 LLM 的导航智能体、在黑盒条件下优化提示注入的框架。
You have reached the end