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Oct 13 · Tuesday
All papers Found 11 papers Another 227 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.
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。
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 LLMLeak，借合法网页抓取把 URL 中的秘密隐蔽外传
Published Oct 1
Evaluated models Meta-Llama-3.3-70B-Instruct , Meta-Llama-4-Scout-17B-16E-Instruct , Meta-Llama-3.1-8B-Instruct and others18 Abstract 作者称第三方外传可发生于正常网页获取，防护需考虑工具请求而不只考虑提示注入。LLMLeak 是利用网页获取请求携带预先编码秘密的隐蔽通道攻击。 它关注恶意软件已能读取数据、却因网络限制或监控无法直接外传的情形。
提出 PMA，用流形对齐从 E2EO 混淆向量恢复明文
Published Sep 7
Evaluated models BERT-base , RoBERTa-base , T5-base and others6 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。
提出 AgentHazard 基准，评测计算机使用智能体的执行层有害行为
Published Apr 3
Evaluated models Qwen2.5-72B-Instruct , Qwen2.5-Coder-32B-Instruct , Qwen3-32B and others12 Abstract AgentHazard 评估了计算机使用智能体的执行安全，作者认为模型层对齐无法可靠保障智能体层面的安全性。AgentHazard 是针对计算机使用智能体执行层安全构建的评估基准，重点探究多步工具调用下局部合理动作累积引发的危害。
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.
Proposing OPTILEAK, reconstructing others' prompts token-by-token via a KV cache side channel
Published Feb 24
Evaluated models Qwen-2.5-3B-Instruct , Qwen-2.5-7B-Instruct , Qwen-2.5-14B-Instruct and others4 Abstract OPTILEAK uses SFT and hard-token DPO to reduce the number of requests per token; the authors claim the risk of cache prompt leakage is higher than previously reported.
提出面向渗出的权重压缩，评估推理服务器权重窃取可行性
Published Jan 4
Evaluated models Qwen2-1.5B , Qwen2-7B , Qwen2.5-1.5B and others8 Abstract 作者用残差量化加微调压缩权重，并称法医水印便宜且微调后仍可解码。这篇工作把 LLM 权重渗出看成 APT：攻击者已有未加密权重，但必须把完整权重藏进推理 API 的合法输出。作者认为可压缩性是被忽略的因素，因为推理量化要求解压后仍能低开销前向，而偷权重的人可以接受事后微调。
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