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Oct 12 · Monday

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Found 141 papers

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MCP and skills4 RAG9 Guardrails and judges5 Monitors7 Reasoning chains7 Permissions and sandboxing3 Fine-tuning and open weights24 Vision7 Audio4 Image generation13 Embeddings5 Multimodal24

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Attack type：Model tampering Attack type：Extraction and theft Attack type：Malicious use Clear all

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DefensesarXiv 2610.13733Oct 9
KVeil: First-Place Solution for the ECCV 2026 LifeGenIP Unlearnable Video Challenge
Propose KVeil, which adds perturbations to face videos to prevent unauthorized customization from reproducing identity
arXiv
2610.13733
Published
Oct 9
Layer
Prompt layer
Setting
Models and APIs
Attacker
White-box, Black-box
Evaluated models
Wan2.2-TI2V-5B, HunyuanVideo-1.5, Kling 3.0 and others4
- AttacksMalicious use
- ComponentImage generation
AbstractKVeil adapts LETHE's reference-reading disruption to the challenge's two stages, ranking first on the leaderboard in both Final columns.
Paper analysisRead analysis
DefensesarXiv 2610.13333Oct 9
LETHE: Defending Against Unauthorized Image-to-Video Generation via Value Diversity Collapse
Proposes LETHE, which perturbs reference images to block unauthorized image-to-video
arXiv
2610.13333
Published
Oct 9
Layer
Prompt layer
Setting
Models and APIs
Evaluated models
Wan2.1-I2V-14B, CogVideoX-5B-I2V, LightX2V and others6
- AttacksMalicious use
- ComponentImage generation
AbstractLETHE 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.
Paper analysisRead analysis
DefensesarXiv 2610.10655Oct 8
Nullify：面向 LLM 遗忘的零空间激活引导免训练方法
提出零空间激活引导 Nullify，免训练擦除指定记忆并保持效用
arXiv
2610.10655
Published
Oct 8
Layer
Model layer
Setting
Models and APIs
Evaluated models
Llama-2-7B-chat-hf, Qwen3-8B
- DefensesInference-time intervention
- AttacksExtraction and theft
Paper analysisRead analysis
DefensesarXiv 2610.10345Oct 8
SLDR：通过选择性层恢复与动态路由防御恶意微调
提出 SLDR，用选择性层恢复与动态路由防御恶意微调
arXiv
2610.10345
Published
Oct 8
Layer
Training and data layer
Setting
Models and APIs
Attacker
Black-box
Evaluated models
Llama3.1-8B-Instruct, Llama3-8B-Instruct, Qwen2.5-7B-Instruct and others4
- DefensesModel hardening
- AttacksModel tampering
- ComponentFine-tuning and open weights
Abstract作者称 SLDR 能压低有害输出并保持下游效用。
SLDR 是针对恶意微调的微调后防御：在已对齐模型上做有符号层探测，只在敏感分数最大与最小的两层训练 LoRA，再用 lmax 的最后 token 表示决定是否启用适配器。
Paper analysisRead analysis
AttacksarXiv 2610.09981Oct 7
研究：LLM 路由决策日志可推断用户医疗、自伤等敏感话题
提出利用路由元数据推断敏感话题的侧信道攻击
arXiv
2610.09981
Published
Oct 7
Layer
Infrastructure layer
Setting
LLM applications
Evaluated models
RouteLLM (BERT router), notdiamond-0001, Vela (vLLM Semantic Router)
- AttacksExtraction and theft
- TechniqueSide channels
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。
Paper analysisRead analysis
AttacksarXiv 2610.09920Oct 7
研究：多向量视觉文档索引可被反演还原页面内容
提出流匹配逆向框架，从多向量视觉文档索引重构页面
arXiv
2610.09920
Published
Oct 7
Layer
Application layer
Setting
LLM applications
Evaluated models
Tomoro-ColQwen3-8B, ColQwen3.5-4.5B, Tomoro-colqwen3-embed-4b and others13
- AttacksExtraction and theft
- ComponentEmbeddings
Paper analysisRead analysis
Analysis and theoryarXiv 2610.09004Oct 7
研究：去除有害信息不足以证明开源权重模型的抗篡改能力
论证发布时互信息不能认证开源权重抗微调恢复
arXiv
2610.09004
Published
Oct 7
Layer
Training and data layer
Setting
Models and APIs
Evaluated models
EleutherAI/deep-ignorance-e2e-strong-filter, EleutherAI/deep-ignorance-unfiltered, Google BERT-Tiny and others6
- DefensesModel hardening
- AttacksModel tampering
- ComponentFine-tuning and open weights
Abstract作者称零信息仍可一步恢复，表观抗微调可来自参数化。
作者研究开源权重模型的篡改抵抗：发布时的互信息是否足以保证微调恢复很慢。
Paper analysisRead analysis
InterpretabilityarXiv 2610.09000Oct 7
研究定位 LLaMA-2-7B-Chat 安全敏感参数
定位安全敏感参数并稀疏改动权重以削弱安全对齐
arXiv
2610.09000
Published
Oct 7
Layer
Model layer
Setting
Models and APIs
Evaluated models
LLaMA-2-7B-Chat
- AttacksModel tampering
- RiskInappropriate refusal
Paper analysisRead analysis
DefensesarXiv 2610.08316Oct 6
MARCO：面向蛋白质生成模型的放射性水印框架
提出 MARCO 放射性水印，冻结蛋白质生成模型并在去噪时嵌入签名
arXiv
2610.08316
Published
Oct 6
Layer
Model layer
Setting
Models and APIs
Attacker
Black-box
Evaluated models
RFdiffusion, ESMFold, Chroma and others6
- DefensesWatermarking and provenance
- AttacksExtraction and theft
AbstractMARCO 在冻结的 PGM 上去噪时嵌构象水印，作者称能兼顾保真与抗扰动，并使盗版模型继承水印。
MARCO 是加在蛋白质生成模型推理过程上的构象水印，用来标记知识产权，并为可能的生物安全滥用保留可追溯信号。作者还把它说成数字权利管理工具，以及符合生物武器公约的数字监管链。问题在于：专有 PGM 的三维输出可能被拿去训练盗版模型，也可能在来源被抹掉后绕过筛查。
Paper analysisRead analysis
InterpretabilityarXiv 2610.07518Oct 6
TRACE：仅凭 checkpoint 更新审计大语言模型有害目标
提出 TRACE，仅凭检查点更新审计有害合规微调目标
arXiv
2610.07518
Published
Oct 6
Layer
Training and data layer
Setting
Models and APIs
Evaluated models
Llama-3.1-8B-Instruct, Mistral-7B-Instruct-v0.3, Qwen3-8B and others6
- AttacksModel tampering
- ComponentFine-tuning and open weights
Abstract作者称有害合规 SFT 在检查点更新中留下可排序坐标，并据此做只读权重审计。
作者在检查点更新里寻找有害合规 SFT 的目标组成，并把读数做成不查询模型、不看未知训练数据的审计分 TRACE。
Paper analysisRead analysis
AttacksarXiv 2610.06848Oct 6
TranScope：利用 CPU 性能计数器对 Transformer 做成员推断攻击
提出 TRANSCOPE，用 CPU 硬件计数器推断训练集成员
arXiv
2610.06848
Published
Oct 6
Layer
Infrastructure layer
Setting
Models and APIs
Attacker
Black-box
Evaluated models
BERT (4.4M-340M), ViT (22M-307M), Pythia (70M-2.8B) and others5
- AttacksExtraction and theft
- TechniqueSide channels
AbstractTRANSCOPE 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。
Paper analysisRead analysis
DefensesarXiv 2610.06636Oct 6
差分隐私混合公共数据集提升隐私学习效果
提出 DP-MixMin，私有学习公开数据混合并做差分隐私微调
arXiv
2610.06636
Published
Oct 6
Layer
Training and data layer
Setting
Models and APIs
Evaluated models
ResNet-18, GPT-2 Medium
- AttacksExtraction and theft
AbstractDP-MixMin 私下学公开混合再 DP 微调，胸片 macro AUC 与邮件困惑度优于均匀混合。
DP-MixMin 是一条端到端的差分隐私训练流程：先私下决定多个公开数据集的预训练混合，再预训练并在敏感数据上 DP 微调。作者称这是首个这样的流程。
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2610.06503Oct 5
研究评测五款开源文生图模型的有害内容生成与审核缺口
评测文生图模型的有害内容生成能力与审核检测缺口
arXiv
2610.06503
Published
Oct 5
Layer
Model layer
Setting
Models and APIs
Evaluated models
SDXL, Stable Diffusion 3.5 Large, FLUX.1-dev and others12
- DefensesDetection and filtering
- AttacksMalicious use
- ComponentImage generation
Paper analysisRead analysis
AttacksarXiv 2610.06093Oct 5
研究评测训练数据提取风险的跨语言迁移
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
arXiv
2610.06093
Published
Oct 5
Layer
Prompt layer
Setting
Models and APIs
Evaluated models
GPT-Neo 1.3B, GPT-Neo 2.7B, GPT-J 6B and others7
- AttacksExtraction and theft
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。
Paper analysisRead analysis
AttacksarXiv 2610.05894Oct 5
研究者提出闭环偏见注入攻击
提出闭环激活引导，在扩散语言模型去噪过程中注入定向偏见
arXiv
2610.05894
Published
Oct 5
Layer
Model layer
Setting
Models and APIs
Attacker
Gray-box
Evaluated models
LLaDA-8B-Instruct, Dream-v0-Instruct-7B, LLaDA-MoE-7B-A1B-Instruct
- AttacksModel tampering
- TechniqueGradient and representation optimization
Abstract通过去噪中间概率反馈动态调节残差流转向，证明扩散语言模型在推理服务栈中存在闭环偏见注入脆弱性。
Paper analysisRead analysis
AttacksarXiv 2610.05601Oct 5
Tracer 框架可从遗忘后的扩散模型中识别被擦除概念
提出 TRACER，从权重差识别扩散模型被擦除概念
arXiv
2610.05601
Published
Oct 5
Layer
Model layer
Setting
Models and APIs
Attacker
White-box
Evaluated models
Stable Diffusion v1.5, Stable Diffusion 3, FLUX.1 and others5
- AttacksExtraction and theft
- ComponentImage generation
AbstractTRACER 用权重足迹识别未披露擦除概念并估计个数，作者称数秒内完成且准确率高于对照。
TRACER 针对的是遗忘扩散模型上的目标识别：提供者从公开基座 W 用已发表方法擦掉未知集合 S⋆，只发布 W′。攻击者还有假定包含这些概念的词表，但不知擦了谁、擦了几个、用了什么算法。
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2610.05586Oct 5
AgentDoxx：联网搜索 Agent 对匿名访谈文本的再识别评测
构建 AGENTDOXX 基准，评测联网搜索 Agent 对匿名访谈的再识别
arXiv
2610.05586
Published
Oct 5
Layer
Application layer
Setting
web agent
Evaluated models
GPT-5.6 Terra, GPT-5 Mini, GPT-4.1 Mini and others10
- AttacksMalicious use
- ComponentRAG
Abstract论文提出了已知身份的重识别基准 AGENTDOXX，揭示搜索与记忆双重风险及现有防御在检索轨迹中的泄露漏洞。
Paper analysisRead analysis
AttacksarXiv 2610.04985Oct 4
研究揭示 Jev 作为应用决策层的安全与隐私风险
评估应用决策层的输入注入与隐私推断风险
arXiv
2610.04985
Published
Oct 4
Layer
Application layer
Setting
LLM applications
Attacker
Black-box, Gray-box
Evaluated models
Jev, NanoJev
- AttacksPrompt injection
- TechniqueRole-play and persuasion
AbstractJev 的类型化决策可被输入与训练操纵，也会泄露信息，并同时支持检测与滥用。
Jev 是 TypeSafe 的 System One 模型，把应用 state 和固定问题变成 Choice、Noul 或 Score 的类型化决策与概率。作者用官方 API 和可改训练的 NanoJev，考察它作为决策层时的操纵、泄露与双重用途。
Paper analysisRead analysis
DefensesarXiv 2610.04426Oct 3
UnAct：无需梯度的定向激活干预实现类别遗忘
提出 UnAct，仅以前向激活干预从分类器中遗忘指定类别
arXiv
2610.04426
Published
Oct 3
Layer
Model layer
Setting
Models and APIs
Evaluated models
ResNet-18, ViT-B/16
- DefensesModel hardening
- AttacksExtraction and theft
AbstractUnAct 用前向激活做类别遗忘。
UnAct 是一种事后类别遗忘：删除请求可以只有少量该类图像，且没有标签和保留数据。它不用梯度，只对遗忘图像做前向。编辑对象是分类器所读的后期单元。
Paper analysisRead analysis
AttacksarXiv 2610.04128Oct 3
研究显示拆分学习中的梯度可提升客户端文本重建率
衡量分裂学习中梯度相对激活对客户文本重建的额外贡献
arXiv
2610.04128
Published
Oct 3
Layer
Training and data layer
Setting
Models and APIs
Evaluated models
GPT-2 (openai-community/gpt2, 124M), Qwen3-0.6B
- AttacksExtraction and theft
Abstract激活已恢复大部分 token。
这篇工作测量 split learning 分裂点上的文本泄漏：激活单独能恢复多少，训练回传的梯度再增加多少，以及按 token 还是按整篇计分会不会改写结论。
Paper analysisRead analysis

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Layer

System component

KVeil: First-Place Solution for the ECCV 2026 LifeGenIP Unlearnable Video Challenge

LETHE: Defending Against Unauthorized Image-to-Video Generation via Value Diversity Collapse

Nullify：面向 LLM 遗忘的零空间激活引导免训练方法

SLDR：通过选择性层恢复与动态路由防御恶意微调

研究：LLM 路由决策日志可推断用户医疗、自伤等敏感话题

研究：多向量视觉文档索引可被反演还原页面内容

研究：去除有害信息不足以证明开源权重模型的抗篡改能力

研究定位 LLaMA-2-7B-Chat 安全敏感参数

MARCO：面向蛋白质生成模型的放射性水印框架

TRACE：仅凭 checkpoint 更新审计大语言模型有害目标

TranScope：利用 CPU 性能计数器对 Transformer 做成员推断攻击

差分隐私混合公共数据集提升隐私学习效果

研究评测五款开源文生图模型的有害内容生成与审核缺口

研究评测训练数据提取风险的跨语言迁移

研究者提出闭环偏见注入攻击

Tracer 框架可从遗忘后的扩散模型中识别被擦除概念

AgentDoxx：联网搜索 Agent 对匿名访谈文本的再识别评测

研究揭示 Jev 作为应用决策层的安全与隐私风险

UnAct：无需梯度的定向激活干预实现类别遗忘

研究显示拆分学习中的梯度可提升客户端文本重建率