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Oct 12 · Monday

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Attack technique：Gradient and representation optimization Attack technique：Role-play and persuasion Attack technique：Conversation structure manipulation Attack surface / entry point：User input Layer：Application layer Clear all

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AttacksarXiv 2610.09973Oct 7
从期望危害到似然：LLM 智能体越狱的概率重构
提出 OPUR 攻击，用似然梯度优化对抗后缀越狱 LLM 智能体
arXiv
2610.09973
Published
Oct 7
Layer
Application layer
Setting
AI Agent
Attacker
White-box
Evaluated models
Llama-3.1-8B-Instruct, Ministral-8B-Instruct-2410
- AttacksJailbreaks
- TechniqueGradient and representation optimization
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
AttacksarXiv 2608.23471Aug 25
InjecMEM: Memory Injection Attacks against LLM Agent Memory Systems
提出 InjecMEM，一次注入 Agent 记忆使后续查询输出预设内容
arXiv
2608.23471
Published
Aug 25
Layer
Application layer
Setting
AI Agent
Evaluated models
Qwen2.5-7B-Instruct, Llama-3.1-8B-Instruct, Mistral-7B-Instruct-v0.3 and others6
- AttacksPoisoning and backdoors
- TechniqueGradient and representation optimization
- ComponentMemory
Paper analysisRead analysis
AttacksarXiv 2602.07379Feb 7
Aegis: Governance, Integrity, and Security for AI Voice Agents
提出 Aegis 红队框架，多轮攻击高风险语音 Agent
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
AttacksarXiv 2602.00979Feb 1
GradingAttack: Revealing Security Vulnerabilities in LLM-Based Educational Grading Agents
提出 GradingAttack，用梯度后缀与角色扮演操纵教育评分智能体判定
arXiv
2602.00979
Published
Feb 1
Layer
Application layer
Setting
LLM applications
Evaluated models
Qwen2.5-7B, Qwen2.5-7B-Instruct, Qwen2.5-14B-Instruct and others8
- AttacksDetection evasion
- TechniqueGradient and representation optimization
Paper analysisRead analysis
AttacksarXiv 2509.25624Sep 30, 2025
STAC：看似无害的工具调用链如何攻破 LLM Agent
提出 STAC 框架，把恶意目标拆成多轮看似无害的工具调用链
arXiv
2509.25624
Published
Sep 30, 2025
Layer
Application layer
Setting
AI Agent
Attacker
Black-box
Evaluated models
GPT-4.1-2025-04-14, GPT-4.1-mini-2025-04-14, Qwen3-32B and others8
- AttacksJailbreaks
- TechniqueMulti-turn escalation
Abstract作者提出并验证了智能体在序列化工具攻击下的普遍脆弱性，指出针对累积动作序列防御的必要性。
Paper analysisRead analysis

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All papers

Attack type

Attack technique

Impact

System type

Attack surface / entry point

Layer

从期望危害到似然：LLM 智能体越狱的概率重构

研究揭示 Jev 作为应用决策层的安全与隐私风险

InjecMEM: Memory Injection Attacks against LLM Agent Memory Systems

Aegis: Governance, Integrity, and Security for AI Voice Agents

GradingAttack: Revealing Security Vulnerabilities in LLM-Based Educational Grading Agents

STAC：看似无害的工具调用链如何攻破 LLM Agent