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Oct 12 · Monday

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Attack technique：Gradient and representation optimization Attack technique：Automated search Attack surface / entry point：Memory writes Attack surface / entry point：Training data Clear all

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AttacksarXiv 2610.01367Oct 1
研究：高质量数据筛选挡不住投毒
提出 Bi-QSTO，优化能穿过质量筛选并在微调中削弱安全对齐的投毒样本
arXiv
2610.01367
Published
Oct 1
Layer
Training and data layer
Setting
Models and APIs
Evaluated models
Llama2-7B-Chat, Llama3-8B-Instruct, Qwen2-7B-Instruct and others5
- AttacksPoisoning and backdoors
- TechniqueGradient and representation optimization
- ComponentFine-tuning and open weights
Abstract质量筛选挡不住部分高分样本的安全削弱。
Bi-QSTO 研究一个实际训练缺口：基于质量的数据选择会先于微调决定样本去留，而以往微调投毒多假设毒样本直接进入训练集。已对齐 LLM 的拒答会被少量有害或看似无害的微调样本削弱。作者用三个问题串起实验：选择能否排除毒样本。
Paper analysisRead analysis
AttacksarXiv 2609.39788Sep 30
研究：多智能体系统潜在通信链路可被训练用于提升有害顺从
提出只训练或篡改多智能体潜在通信链路来提高有害遵从
arXiv
2609.39788
Published
Sep 30
Layer
Application layer
Setting
Multi-agent systems
Evaluated models
Llama-3.2-3B-Instruct, Qwen2.5-3B-Instruct, Gemma-3-1B-IT and others7
- AttacksModel tampering
- TechniqueGradient and representation optimization
Abstract潜在链路本身即可改变冻结智能体系统的安全，奖励既能攻击也能修复。
学习得到的潜在通信链路会改变由冻结、对齐智能体组成的系统是否遵从有害请求。。作者研究三种拓扑：双智能体、三级顺序协作，以及双专家加汇总。
Paper analysisRead analysis
AttacksarXiv 2609.35576Sep 29
研究提出跨 LLM 助手的记忆跳跃攻击
提出跨独立 Agent 的记忆跳跃攻击，经共享工件在助手间自传播
arXiv
2609.35576
Published
Sep 29
Layer
Application layer
Setting
AI Agent
Evaluated models
GPT-5.6 Luna, Kimi-K2.6, GPT-OSS-120B and others6
- AttacksPoisoning and backdoors
- TechniqueDormant triggers
- ComponentMemory
Abstract论文证实持久工件与内存可形成跨智能体传播链，揭示了将共享工件纳入安全防线的重要意义。
该论文系统研究并量化了通过共享持久工件在具有独立私有内存的 LLM 智能体之间发生自传播的“工件介导传播”攻击风险。
Paper analysisRead analysis
AttacksarXiv 2609.27422Sep 23
RAMP：逆转对抗扰动以增强针对恶意软件检测器的干净标签后门攻击
提出 RAMP，逆转对抗扰动加强恶意软件检测器的干净标签后门
arXiv
2609.27422
Published
Sep 23
Layer
Training and data layer
Setting
Models and APIs
Evaluated models
MalConv, MalConvGCG
- AttacksPoisoning and backdoors
- TechniqueAutomated search
- ComponentFine-tuning and open weights
AbstractRAMP 用反转扰动做 clean-label 投毒。
RAMP 是 score-based black-box 下的 clean-label 后门增强，对象是会微调的 Windows PE 原始字节恶意软件检测器。
Paper analysisRead analysis
AttacksarXiv 2608.03642Aug 4
规避与投毒攻击对恶意软件数据漂移检测器的实证分析
用通用扰动对恶意软件分类器及漂移检测器做规避与后门投毒
arXiv
2608.03642
Published
Aug 4
Layer
Model layer
Setting
Models and APIs
Evaluated models
MLP 100-100, CADE (CAE 512-384-256-128), CADE (CAE 512-128-32-8) and others6
- AttacksDetection evasion
- TechniqueGradient and representation optimization
Paper analysisRead analysis
AttacksarXiv 2608.01759Aug 3
EvoBreak 利用自我演化 Agent 的无害经验组合实施攻击
提出 EvoBreak，以良性任务组合诱导自进化 Agent 的持久经验并削弱安全边界
arXiv
2608.01759
Published
Aug 3
Layer
Application layer
Setting
AI Agent
Evaluated models
GPT-5-mini, Llama-3.1-8B-Instruct
- AttacksPoisoning and backdoors
- TechniqueAutomated search
- ComponentMemory
Paper analysisRead analysis
AttacksarXiv 2606.09499Jun 8
研究者提出针对世界模型的机器人学习管线投毒攻击
提出 VPH 与 VTH，只改视频帧劫持世界模型并投毒下游策略
arXiv
2606.09499
Published
Jun 8
Layer
Training and data layer
Setting
Embodied systems and robotics
Evaluated models
Cosmos-Predict 2.5, Cosmos-Predict 2.5 AC, Dino WM and others4
- AttacksPoisoning and backdoors
- TechniqueGradient and representation optimization
- ComponentVideo
Abstract只改视频帧经世界模型投毒。
只改看似安全的视频帧，经世界模型之后才改变下游机器人策略。 问题在于世界模型进入数据生成后，数据集检查看到的仍是安全演示，合成轨迹却可以变危险。
Paper analysisRead analysis
AttacksarXiv 2605.11047May 11
DeepTrap 框架在 OpenClaw 上自动挖掘智能体执行上下文漏洞
提出 DEEPTRAP 以搜索可诱导不安全轨迹的执行上下文载荷
arXiv
2605.11047
Published
May 11
Layer
Application layer
Setting
AI Agent
Evaluated models
GPT-5.4, Claude Sonnet 4.6, GLM-5 and others9
- AttacksPrompt injection
- TechniqueAutomated search
- ComponentMemory
AbstractDEEPTRAP 用轨迹级搜索发现 OpenClaw 上下文风险，作者称不能只看最终回复。
DEEPTRAP 是针对 OpenClaw 的自动化红队框架，搜索执行前被改写的文件、记忆、技能、工具元数据或配置，而不是改用户指令。
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2605.01970May 4
Trojan Hippo Bench: A Dynamic Benchmark for Persistent Memory Attacks and Defenses on LLM Agents
构建 Trojan Hippo Bench，评测 Agent 持久记忆投毒与外泄
arXiv
2605.01970
Published
May 4
Layer
Application layer
Setting
AI Agent
Evaluated models
Gemini 3.1 Pro, GPT-5-mini, GPT-5
- AttacksPoisoning and backdoors
- TechniqueDormant triggers
- ComponentMemory
Abstract论文提出了评测智能体持久化记忆攻击与防御的 Trojan Hippo Bench，揭示了潜伏攻击威胁及内存防御的安全与效用权衡。
Paper analysisRead analysis

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Attack technique

Impact

System type

Attack surface / entry point

Layer

System component

研究：高质量数据筛选挡不住投毒

研究：多智能体系统潜在通信链路可被训练用于提升有害顺从

研究提出跨 LLM 助手的记忆跳跃攻击

RAMP：逆转对抗扰动以增强针对恶意软件检测器的干净标签后门攻击

规避与投毒攻击对恶意软件数据漂移检测器的实证分析

EvoBreak 利用自我演化 Agent 的无害经验组合实施攻击

研究者提出针对世界模型的机器人学习管线投毒攻击

DeepTrap 框架在 OpenClaw 上自动挖掘智能体执行上下文漏洞

Trojan Hippo Bench: A Dynamic Benchmark for Persistent Memory Attacks and Defenses on LLM Agents