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Oct 12 · Monday
All papers Found 23 papers Another 240 papers do not have category tags yet and are not included in filtered results.
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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.
Propose KVeil, which adds perturbations to face videos to prevent unauthorized customization from reproducing identity
Published Oct 9
Evaluated models Wan2.2-TI2V-5B , HunyuanVideo-1.5 , Kling 3.0 and others4 Abstract KVeil adapts LETHE's reference-reading disruption to the challenge's two stages, ranking first on the leaderboard in both Final columns.
Proposes LETHE, which perturbs reference images to block unauthorized image-to-video
Published Oct 9
Evaluated models Wan2.1-I2V-14B , CogVideoX-5B-I2V , LightX2V and others6 Abstract LETHE suppresses the diversity of reference values and attracts attention, making protected images difficult to animate faithfully.LETHE is a protective perturbation added to personal images before publication, used to counter unauthorized image-to-video generation.
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 TRANSCOPE，用 CPU 硬件计数器推断训练集成员
Published Oct 6
Evaluated models BERT (4.4M-340M) , ViT (22M-307M) , Pythia (70M-2.8B) and others5 Abstract TRANSCOPE 揭示了分词器在嵌入表引发的硬件访问局部性差异，证实微架构计数器可突破恒定时间与置信度遮蔽防御。
用翻译前缀做跨语言训练数据提取，恢复英语训练文本中的 PII
Published Oct 5
Evaluated models GPT-Neo 1.3B , GPT-Neo 2.7B , GPT-J 6B and others7 Abstract 跨语言 TDE 能恢复英语记忆的 PII，转移随多语训练和措辞保真变化。这篇工作检验训练数据抽取是否跨语言：英语段落里记住的 PII，能否被其他语言的前缀逐字抽出。
提出 HXAI 分层隐私解释框架，本地保留细解释并只发布加噪摘要
Published Oct 2
Evaluated models gradient-boosted tree regression models Abstract HXAI 把无噪声局部 SHAP 留在家庭内，只聚合差分隐私摘要。HXAI 是面向分布式能源系统的分层隐私保护解释框架，服务规划、监测与诊断，而不是实时控制。
提出 VEX-Bench 评测 LLM 生成虚假信息的核查复杂度
Published Sep 28
Evaluated models Claude Sonnet 4.5 , Gemini 3.1 Pro , GPT-5.4 and others7 Abstract VEX-Bench 用筛查时的五维复杂度衡量虚假信息对核查容量的占用，并报告生成与核查的成本差。VEX-Bench 把 LLM 虚假信息的风险从“能否生成”改成“筛查时会占用多少核查容量”。
提出 PrivDrift 基准，审计话题漂移后的用户秘密复述
Published Sep 25
Evaluated models GPT-OSS-120B , DeepSeek-R1 , Qwen3-VL-235B 提出扩散潜空间地理扰动防御，抑制视觉语言模型泄露精确地理位置
Published Sep 18
Evaluated models GPT-4.1 , GPT-5 , Gemini 2.5 Pro and others5 攻破混合 FHE 推理的置换式保密，精确恢复线性层排序谱
Published Sep 11
Evaluated models ResNet-20 , ResNet-56 , ResNet-50 and others12 Propose GIFT, isolating user data in shared LLM serving using encryption and GPU information flow tracking
Published Aug 26
Evaluated models Qwen2.5-14B , Qwen2.5-32B , Qwen2.5-72B and others6 Abstract GIFT uses cryptographic isolation plus GPU information flow rules to enforce user data isolation on vLLM and DistServe, and reports throughput overhead.GIFT is the authors' user data isolation system for shared LLM serving, with GIFT-CC enabling confidential computing. In the conclusion, the authors state it is the first IFT design for this scenario.
Proposing KeyPooling to measure where API relays break prompt cache isolation
Published Aug 18
Evaluated models grok-4-fast-non-reasoning , grok-4-1-fast-non-reasoning , grok-3 and others11 Abstract Shared upstream credentials place relay customers in the same cache domain; preserving identity or separate principals can separate them.KeyPooling measures where LLM API relays drop customer identity, causing prompt caches to no longer be isolated per downstream customer.
利用加密思维块的跨模型重放，诱导弱模型逐字转写专有推理链
Published Aug 11
Evaluated models Claude Opus 4.8 , Claude Sonnet 4.6 , Claude Haiku 4.5 and others13 Abstract 论文揭示了客户端加密思维块跨模型互换漏洞，提出了低成本提取方法并给出防御方案。
Extract system instructions with encoding and structured requests, and harden them by rewriting with CoT
Published Apr 1
Evaluated models GPT-4.1-mini , GPT-3.5-turbo , Gemini-2.5-flash and others7 Abstract Encoded requests can elicit system instructions after a direct refusal.This work evaluates and hardens the confidentiality of LLM system instructions under encoded extraction. The authors are concerned with: when direct extraction has been rejected, whether the model still surrenders protected content when the same request is rewritten as a formatting or encoding task.
Proposes PrefixWall, which selectively isolates shared prefixes to mitigate timing side channels
Published Mar 11
Evaluated models gemma-3-4b-it , llava-onevision-qwen2-0.5b-ov-hf , llava-onevision-qwen2-7b-ov-chat-hf and others9 Abstract PrefixWall uses cache metadata to selectively cut off cross-user prefix continuation, preserving APC while limiting the TTFT side channel.PrefixWall is a system-level defense for multi-tenant LLM services that limits the TTFT side channel of Automatic Prefix Caching while preserving prefix reuse.
提出 LFT 多轮框架，评测模型对欺诈与网络犯罪的可执行协助
Published Feb 25
Evaluated models Claude Sonnet 3.5 , Claude Sonnet 3.7 , Claude Sonnet 4 and others14 Abstract 作者称低努力文本诱导下，多轮 LFT 中模型对三类犯罪的信息协助有限。作者给出一套由执法与政策专家校验的多轮评测，用来估计文本模型经由信息提供，对欺诈和网络犯罪有多少可执行协助。
提出 OPTILEAK，经 KV 缓存侧信道逐 token 重建他人提示
Published Feb 24
Evaluated models Qwen-2.5-3B-Instruct , Qwen-2.5-7B-Instruct , Qwen-2.5-14B-Instruct and others4 Abstract OPTILEAK 用 SFT 与 hard-token DPO 降低每 token 请求数，作者称缓存提示泄露的风险高于先前报告。
提出用户侧防御 TRACE-RPS，分享前匿名化文本并追加后缀以抑制属性推断
Published Feb 12
Evaluated models Llama2-7B-Chat , Llama2-13B-Chat , Llama3.1-8B-Instruct and others10 Abstract 用户侧 TRACE 加 RPS/MPS，在分享前降低 LLM 对敏感属性的推断准确率。这是一项用户侧防御：在文本公开前降低 LLM 对敏感属性的推断，而不是改攻击提示或削弱模型推理。
提出 JustAsk，用在线技能选择提取模型系统提示词
Published Jan 29
Evaluated models anthropic/claude-opus-4.5 , openai/gpt-5.2-codex , GPT-5.2 and others19 提出 ChartAttack，用恶意提示驱动代码模型生成误导图表
Published Jan 19
Evaluated models Claude-4.6-Sonnet , GPT-4o , Gemini-2.5-Flash and others20 Abstract ChartAttack 生成不改数据的误导图，降低 MLLM 与人类的图表问答准确率。ChartAttack 研究恶意提示能否让 MLLM 在不改数据的情况下生成误导图表，并量化这些图对其他 MLLM 和人类图表问答的影响。