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Attack type：Extraction and theft Attack type：Malicious use System component：Memory System component：Monitors System component：Audio Clear all

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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
Evaluation and benchmarksarXiv 2609.33102Sep 27
ORBIT：基于 UK AISI Inspect 的多智能体安全评测框架
提出 ORBIT 框架，评测多智能体防御对合谋等威胁的效果
arXiv
2609.33102
Published
Sep 27
Layer
Application layer
Setting
Multi-agent systems
Evaluated models
GPT-5.4, Claude Sonnet 4.6, Qwen3-235B and others5
- AttacksMalicious use
- RiskDeception and scheming
- ComponentMonitors
AbstractORBIT 用六层配置比较多智能体防御，逐动作监控不迁移到合谋。
Paper analysisRead analysis
Risky behaviorarXiv 2609.30266Sep 25
研究显示 Claude Code、Codex 等本地智能体可轻易篡改自身执行轨迹
测试编程智能体被诱导或自主篡改自身执行轨迹的行为
arXiv
2609.30266
Published
Sep 25
Layer
Application layer
Setting
coding agent
Evaluated models
GPT-5.6-Sol, GPT-6-Sol, Opus-5 and others11
- AttacksMalicious use
- RiskDeception and scheming
- ComponentMonitors
Abstract论文评估了本地智能体的轨迹防篡改能力，发现其可因外部指令或奖励追求而清除执行证据，亟需建立独立拦截记录机制。
这是一项针对本地大模型智能体执行轨迹完整性与安全防篡改边界的实证研究。
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 2609.22724Sep 19
MATE：面向移动 Agent 的策略感知安全审计方法
提出移动 Agent 轨迹审计器 MATE，判断轨迹是否违反可编辑安全策略
arXiv
2609.22724
Published
Sep 19
Layer
Application layer
Setting
Browser and computer use
Attacker
Black-box
Evaluated models
MATE-0.5B, MATE-1.5B, MATE-3B
- DefensesMonitoring and auditing
- AttacksMalicious use
- RiskDangerous agent actions
- ComponentMonitors
AbstractMATE 用合成轨迹微调轻量审计器，在可编辑策略下判断移动智能体轨迹是否违规并给出解释。
MATE 是面向移动 GUI 智能体的轻量策略条件轨迹审计器，在给定自然语言安全策略时判断轨迹是否违规并解释原因。
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2609.06966Sep 7
Mole：面向前沿实验室 AI 智能体的内部威胁检测基准
提出 MOLE 基准，评测监控器对 AI 智能体内部威胁的检出能力
arXiv
2609.06966
Published
Sep 7
Layer
Application layer
Setting
AI Agent
Evaluated models
GPT-5.3 Chat, GPT-5.5, GPT-4.1 and others15
- DefensesMonitoring and auditing
- AttacksMalicious use
- ComponentMonitors
AbstractMOLE 构建了智能体内部威胁检测基准，作者指出高漏检现状与推理痕迹的防御价值，并展示了监控策略优化空间。
MOLE 是首个针对前沿 AI 实验室智能体内部威胁检测的开放基准，旨在评估在有限每日审查预算与日常研发业务背景下识别恶意账号的能力。。
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
Evaluation and benchmarksarXiv 2608.03272Aug 4
多智能体协同过滤系统的连接性攻击与防御研究
复现 AgentCF 上的多智能体攻防并刻画连通性作用
arXiv
2608.03272
Published
Aug 4
Layer
Application layer
Setting
Multi-agent systems
Evaluated models
Qwen3-235B-A22B, Claude Haiku 4.5, Mixtral 8×7B and others4
- AttacksPoisoning and backdoors
- ComponentMemory
Abstract连通性沿 k 与ρ改变攻防，且用户/物品与早期/稳态需要分开看。
作者在 AgentCF 上研究连通性如何改变多智能体协同过滤的攻防，复现已有方法而不是提出新载荷。
Paper analysisRead analysis
DefensesarXiv 2606.22673Jun 22
Agent MechSuits：面向多轮 CLI Agent 的机制子空间安全引导
提出 Agent MechSuits，对多轮 CLI Agent 做步级检测与子空间转向
arXiv
2606.22673
Published
Jun 22
Layer
Application layer
Setting
coding agent
Evaluated models
LLaMA-3.1-8B, Qwen-2.5-7B, Gemma-2-9B
- DefensesInference-time intervention
- AttacksMalicious use
- ComponentMonitors
Abstract白盒步级探针加稀疏转向可压低三模型恶意动作，同时提高生成崩溃率。
Agent MechSuits 是面向多轮 CLI 智能体的白盒、步级表示防御，并配有可执行轨迹基准 MAS。
Paper analysisRead analysis
DefensesarXiv 2604.11806Apr 14
Meerkat：跨大量 Agent 轨迹检测安全违规
提出 Meerkat，聚类并搜索 Agent 轨迹库以定位跨轨迹安全违规
arXiv
2604.11806
Published
Apr 14
Layer
Application layer
Setting
AI Agent
Evaluated models
GPT-5.4, GPT-5.4-mini, Qwen3.5-397B-A17B and others5
- DefensesMonitoring and auditing
- AttacksMalicious use
- RiskReward hacking
- ComponentMonitors
AbstractMeerkat 用聚类与智能体搜索定位跨轨迹违规，并在真实评测轨迹中找出脚手架泄题与奖励黑客。
Meerkat 是事后审计方法，用来在智能体轨迹仓库里找出单条看不出、合在一起才违反自然语言安全属性的见证。
Paper analysisRead analysis
AttacksarXiv 2602.07379Feb 7
Aegis: Governance, Integrity, and Security for AI Voice Agents
Proposes the Aegis red-teaming framework for multi-turn attacks on high-risk voice agents.
arXiv
2602.07379
Published
Feb 7
Layer
Application layer
Setting
AI Agent
Evaluated models
GPT-4o, GPT-4o-mini, Gemini-1.5 Pro and others7
- AttacksDenial of service
- TechniqueMulti-turn escalation
- ComponentAudio
AbstractAegis red-teams three types of voice agents: query-based access eliminates authentication and leakage successes, but behavioral attacks remain.
Aegis uses multi-turn red-teaming evaluation on ALLM voice agents already integrated into business systems. The authors call this the first systematic red-teaming framework in this setting.
Paper analysisRead analysis

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