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Impact：Data and privacy leaks System component：Monitors System component：Audio System component：Embeddings Clear all

Another 240papers do not have category tags yet and are not included in filtered results.

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
Evaluation and benchmarksarXiv 2610.06454Oct 5
AgentPrivArena：面向 LLM Agent 工作流的隐私风险评测框架
提出 AgentPrivArena 与运行时审计，评测 Agent 轨迹级隐私泄露
arXiv
2610.06454
Published
Oct 5
Layer
Application layer
Setting
AI Agent
Evaluated models
GPT-5.4, DeepSeek-V4-Pro, Kimi-K2.6 and others6
- DefensesMonitoring and auditing
- RiskDangerous agent actions
- ComponentMonitors
Paper analysisRead analysis
DefensesarXiv 2610.01184Oct 1
ReCast：在固定媒体接口下保护多模态推理的隐私框架
提出 RECAST，本地改写图表与语音并做可逆数值映射以保护远程推理隐私
arXiv
2610.01184
Published
Oct 1
Layer
Application layer
Setting
LLM applications
- ComponentVision
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2609.34092Sep 28
机器遗忘在 ASR 中是否有效：arXiv 论文评估现有算法与评测工具
arXiv
2609.34092
Published
Sep 28
Layer
Training and data layer
Setting
Models and APIs
Evaluated models
E-Branchformer
- DefensesModel hardening
- AttacksExtraction and theft
- ComponentAudio
Abstract梯度上升类方法更接近简单评测下的重训参照，复杂方法会被知情攻击认出，多说话人更差。
Paper analysisRead analysis
Risky behaviorarXiv 2609.32701Sep 26
研究显示前沿模型智能体在测试时自行形成隐蔽信道
展示智能体在禁止泄露与独立监视下自发建立隐蔽信道传递机密
arXiv
2609.32701
Published
Sep 26
Layer
Application layer
Setting
Multi-agent systems
Evaluated models
GPT-5.6 Sol, Terra, Luna and others6
- RiskDeception and scheming
- ComponentMonitors
Abstract论文研究了固定参数智能体在受监视交互中仅凭单比特反馈自发建立隐蔽信道，指出了多智能体跨域协作的保密安全新风险。
论文研究了固定参数与提示词的智能体在受审计交互中自发形成隐蔽信道并泄露机密的过程。
Paper analysisRead analysis
AttacksarXiv 2609.27542Sep 23
Researchers use control token injection to delete gpt-oss-20b's reasoning chain and bypass chain-of-thought monitoring
提出控制标记注入，清空工具 Agent 的思维链以绕过推理监控
arXiv
2609.27542
Published
Sep 23
Layer
Application layer
Setting
AI Agent
Evaluated models
openai/gpt-oss-20b, google/gemma-4-26B-A4B-it, deepseek-ai/DeepSeek-R1-Distill-Qwen-7B and others4
- AttacksPrompt injection
- TechniqueConversation structure manipulation
- ComponentReasoning chains
Abstract论文阐明控制标记注入可抑制 CoT 使监控失效并促成拒绝绕过，且工具执行受解析器宽容度影响。
Paper analysisRead analysis
DefensesarXiv 2609.27399Sep 23
GUARD：在 CosyVoice2 上做说话人身份遗忘以阻止重识别
提出 GUARD，在零样本 TTS 上用激活转向遗忘说话人身份以阻止再识别
arXiv
2609.27399
Published
Sep 23
Layer
Model layer
Setting
Models and APIs
Evaluated models
CosyVoice2-0.5B, F5-TTS, FireRedTTS
- DefensesInference-time intervention
- AttacksMalicious use
- ComponentAudio
AbstractGUARD 用门控激活转向把退出说话人推向冒充相似度，以抑制再识别并保住音质。
GUARD 用门控激活转向做说话人身份遗忘，使零样本 TTS 对退出说话人难以再识别。
Paper analysisRead analysis
DefensesarXiv 2609.19587Sep 17
红队测试 Auto Mode：用注入与多上下文攻击绕过编码 Agent 阻断监控器
提出 Auto Mode ++，加固编码 Agent 阻断监视器
arXiv
2609.19587
Published
Sep 17
Layer
Application layer
Setting
coding agent
Evaluated models
Opus 5, Opus 4.8, Opus 4.7 and others8
- DefensesMonitoring and auditing
- RiskDeception and scheming
- ComponentMonitors
Abstract系统评估生产阻断监视器在面对持续恶意智能体时的表现，提出加固防御并指明多上下文防御挑战。
Paper analysisRead analysis
AttacksarXiv 2609.06749Sep 7
PMA 攻击：针对隐私保护 LLM 中嵌入到嵌入混淆的语义流形对齐方法
提出 PMA，用流形对齐从 E2EO 混淆向量恢复明文
arXiv
2609.06749
Published
Sep 7
Layer
Model layer
Setting
Models and APIs
Evaluated models
BERT-base, RoBERTa-base, T5-base and others6
- AttacksExtraction and theft
- ComponentEmbeddings
Paper analysisRead analysis
DefensesarXiv 2609.04767Sep 4
SHAQ: Privacy-preserving retrieval in vector databases using shadow queries
Proposes SHAQ, which replaces document embeddings with shadow query embeddings to defend against embedding inversion.
arXiv
2609.04767
Published
Sep 4
Layer
Application layer
Setting
LLM applications
Attacker
Black-box
Evaluated models
GTR-T5 encoder, DPR
- AttacksExtraction and theft
- ComponentEmbeddings
AbstractSHAQ replaces document embeddings with shadow query embeddings.
SHAQ is a defense against EIA for cloud vector databases: instead of directly modifying document embeddings, it replaces them with shadow query embeddings.
Paper analysisRead analysis
AttacksarXiv 2609.01723Sep 2
研究者提出针对微调 TTS 模型的黑盒成员推断攻击
提出针对微调 TTS 的黑盒成员推断攻击
arXiv
2609.01723
Published
Sep 2
Layer
Model layer
Setting
Models and APIs
Attacker
Black-box
Evaluated models
CosyVoice2, F5-TTS, XTTS-v2
- AttacksExtraction and theft
- ComponentAudio
Abstract黑盒 TTS 成员推断以 recitation 加分层语音表示为分数，说话人级强于记录级，DP-SGD 可压到随机附近。
作者给出一个针对微调文本转语音模型的黑盒成员推断框架，分别审计说话人是否进入微调集，以及某一条文本–音频记录是否进入微调集。微调 TTS 以合成文本和参考语音双条件生成，既有生成模型 MIA 的单条件查询和图像/文本上的逐点比较不能直接套用。作者用 scorable extent 与 memorization elicitation 比较五种查询，认为只有 recitation（用目标转写和干净目标语音要求模型复现）同时满足两条，并把它作为主查询。
Paper analysisRead analysis
AttacksarXiv 2607.01276Jul 1
研究者提出嵌入推断攻击 EIA，可在黑盒 IR 系统中识别所用嵌入模型
提出嵌入推断攻击 EIA，在黑盒 IR 中识别所用嵌入模型
arXiv
2607.01276
Published
Jul 1
Layer
Application layer
Setting
LLM applications
Attacker
Black-box
Evaluated models
e5-small, e5-small-v2, e5-base and others19
- AttacksExtraction and theft
- ComponentEmbeddings
AbstractEIA 用无序文档集的差异从候选中识别嵌入模型，并给出 reranker 与阈值下的计数。
EIA 是在黑盒 IR 上识别所用嵌入模型的攻击。。
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2606.05647Jun 4
Study: 94% of developers failed to detect covert sabotage by coding agents
评测开发者能否在协作编程中发现编码 Agent 的隐蔽破坏
arXiv
2606.05647
Published
Jun 4
Layer
Application layer
Setting
coding agent
Evaluated models
Claude-Opus-4.6, GPT-5.4, Gemini-3.1-Pro and others5
- DefensesMonitoring and auditing
- RiskDeception and scheming
- ComponentMonitors
Paper analysisRead analysis
DefensesarXiv 2605.17380May 17
Uber proposes ADR enterprise-level agent detection system, deployed on 7,200 hosts for ten months
提出企业级检测系统 ADR，识别编程 Agent 的提示注入与恶意 MCP
arXiv
2605.17380
Published
May 17
Layer
Application layer
Setting
coding agent
- DefensesDetection and filtering
- AttacksPrompt injection
- ComponentMCP and skills
Abstract提出了端点因果遥测、两级在线分流与离线红队闭环的 ADR 系统，并在生产环境中验证了有效性。
Paper analysisRead analysis
AttacksarXiv 2602.01757Feb 2
Zero2Text: Training-Free Cross-Domain Text Embedding Inversion Attack
提出 Zero2Text，无需训练地跨域反演文本嵌入
arXiv
2602.01757
Published
Feb 2
Layer
Model layer
Setting
Models and APIs
Attacker
Black-box
Evaluated models
GTR-Base, Qwen3-Embedding-0.6B, Text-Embedding-3-small and others4
- AttacksExtraction and theft
- TechniqueAutomated search
- ComponentEmbeddings
Abstract零训练在线对齐可在未知域、无泄漏对时从黑盒 embedding 还原句子。
Zero2Text 是一种不训练 decoder、也不使用泄漏文本–向量对的文本 embedding 反演，针对 RAG 向量库在严格黑盒、目标域未知时的还原。
Paper analysisRead analysis
DefensesarXiv 2601.14660Jan 21
NeuroFilter: Activation-value guardrails for privacy-conscious LLM agents
提出 NeuroFilter，用激活探针过滤智能体多轮隐私攻击
arXiv
2601.14660
Published
Jan 21
Layer
Application layer
Setting
AI Agent
Attacker
Black-box
Evaluated models
GPT-OSS 20B, Qwen 2.5 32B Instruct, Llama 3.3 70B and others8
- DefensesMonitoring and auditing
- AttacksJailbreaks
- ComponentMonitors
Paper analysisRead analysis

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