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Oct 13 · Tuesday

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Defense type：Detection and filtering Defense type：Permissions and approval Attack surface / entry point：Web pages and interfaces Attack surface / entry point：Code repositories and dependencies Attack surface / entry point：Training data Attack surface / entry point：Weights and gradients Clear all

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Analysis and theoryarXiv 2610.13051Oct 9
Space beats statistics, retraining beats ranking: auditing training labels under label-only backdoors
Auditing training labels for label-only backdoors, comparing spaces and statistics, and judging by retraining
arXiv
2610.13051
Published
Oct 9
Layer
Training and data layer
Setting
Models and APIs
Evaluated models
ResNet-32
- DefensesDetection and filtering
- AttacksPoisoning and backdoors
AbstractLabel inspection must be done in a space that has not been fitted to these labels, and judged by retrained models rather than detection scores.
The authors ask: can a defender without trusted data decide which training labels to keep, and how much clean data this destroys, and what the final model accuracy is.
Paper analysisRead analysis
DefensesarXiv 2610.03089Oct 2
COBRA: A Dual-LLM Architecture for Defending Computer-Using Agents Against Branch-Jumping Attacks
提出 COBRA 双 LLM 架构，防御计算机使用 Agent 的分支转向攻击
arXiv
2610.03089
Published
Oct 2
Layer
Application layer
Setting
Browser and computer use
Evaluated models
gpt-5, UI-TARS-1.5-7B, claude-sonnet-5 and others6
- DefensesPermissions and approval
- AttacksPrompt injection
- ComponentPermissions and sandboxing
Abstract提出分支转向防御架构 COBRA，通过事前能力约束与边界代理消除分支转向攻击并兼顾效用。
Paper analysisRead analysis
DefensesarXiv 2610.01788Oct 1
SAGE：基于相似度从少量已验证样本中清洗中毒训练数据
提出 SAGE，用少量已核验样本按相似度清洗投毒训练数据
arXiv
2610.01788
Published
Oct 1
Layer
Training and data layer
Setting
Models and APIs
Evaluated models
ResNet-18, VGG-16
- DefensesDetection and filtering
- AttacksPoisoning and backdoors
Paper analysisRead analysis
DefensesarXiv 2610.02569Oct 1
Pincer：用数字分身学习用户偏好并执行动态最小权限
为 Agent 提出资源层授权系统 Pincer，用数字分身执行动态最小权限
arXiv
2610.02569
Published
Oct 1
Layer
Application layer
Setting
AI Agent
Evaluated models
Claude Haiku 4.5, Muse Spark 1.2 Contributor
- DefensesPermissions and approval
- AttacksPrompt injection
- ComponentPermissions and sandboxing
AbstractPincer 在资源层以三票合取做授权。
Pincer 是加在现成 coding agent 上的资源层授权防御：用只受用户侧信息影响的 digital twin 做动态最小权限，并与 tool-call 层防御并存。要处理的是用户维护策略带来的权限蠕变和授权疲劳，以及不学习用户策略的 tool-call 分类器。作者引述工作称 auto mode 会放行多数不安全操作，并认为推理代码或复杂 shell 的效果在这一层不实际。
Paper analysisRead analysis
DefensesarXiv 2609.37196Sep 29
ToolFence: Fine-Grained Authorization for Tool-Calling LLM Agents
提出 ToolFence，以能力蓝图和确定性授权拦截 Agent 未授权工具调用
arXiv
2609.37196
Published
Sep 29
Layer
Application layer
Setting
AI Agent
Evaluated models
Qwen3-max, GPT-4o (gpt-4o-2024-08-06), Mistral-Small-3.1-24B-Instruct-2503
- DefensesPermissions and approval
- AttacksPrompt injection
- ComponentPermissions and sandboxing
Paper analysisRead analysis
DefensesarXiv 2609.35659Sep 29
Tracekit: A tamper-proof intent-reasoning-action audit system for autonomous coding agents
提出 Tracekit，审计编码 Agent 的意图、推理与动作并检测账本篡改
arXiv
2609.35659
Published
Sep 29
Layer
Application layer
Setting
coding agent
Evaluated models
Claude Code (claude -p, version 2.1), 独立评审（同一 CLI，无工具）
- DefensesMonitoring and auditing
- AttacksPrompt injection
- RiskDangerous agent actions
- ComponentMonitors
AbstractTracekit 把意图、自述和动作写入可锚定的哈希链。
Tracekit 是无第三方依赖的编码智能体审计层：把人类请求、模型自述和已执行动作并排写入可外部锚定的哈希链，执行前用正则门，事后用规则和独立模型互查。
Paper analysisRead analysis
DefensesarXiv 2609.34804Sep 28
物理认证联邦学习：为关键水务基础设施的协同异常检测提供安全保障
为协同异常检测提出物理准入门，在聚合前拦截不合规训练批次
arXiv
2609.34804
Published
Sep 28
Layer
Training and data layer
Setting
Models and APIs
Evaluated models
deep reconstruction autoencoder
- DefensesDetection and filtering
- AttacksPoisoning and backdoors
Abstract物理准入为联邦检测增加数据约束，但合规证明不等于无投毒或训练来源证明。
这是一项面向水基础设施联邦异常检测的物理准入防御研究。 作者针对隐藏训练遥测带来的投毒检查问题，结合不变量挖掘、聚合前过滤和零知识抽样证明。
Paper analysisRead analysis
DefensesarXiv 2609.31282Sep 25
基于区块链的 Agentic AI 框架：面向安全软件供应链的资源优化与能耗感知
提出 AttestChain，将供应链安全智能体报告签名上链并据此控制发布
arXiv
2609.31282
Published
Sep 25
Layer
Application layer
Setting
Multi-agent systems
Evaluated models
GPT-class LLM
- DefensesWatermarking and provenance
- ComponentPermissions and sandboxing
AbstractAttestChain 将智能体报告与链上发布门控结合，现有证据来自受控合成构建的总体结果。
AttestChain 是一个把 LLM 安全检查、智能体身份验证和许可链发布策略连接起来的软件供应链防御框架。。
Paper analysisRead analysis
AttacksarXiv 2609.27124Sep 23
Fed-ADR：用恶意编排服务器协同客户端梯度操纵攻击联邦学习
提出 Fed-ADR，用编排服务器协同操纵梯度攻破联邦学习并检测恢复
arXiv
2609.27124
Published
Sep 23
Layer
Training and data layer
Setting
Models and APIs
Attacker
Gray-box
Evaluated models
CNN（MNIST：2 Conv + 3 FC）, CNN（Fashion-MNIST：6 Conv + 1 FC）, ResNet18（CIFAR-10）
- DefensesDetection and filtering
- AttacksPoisoning and backdoors
- TechniqueDefense-targeted
AbstractFed-ADR 用 gray-box 编排攻击绕过多种聚合防御，再用 Hessian 一致性检测和窗口内恢复把准确率拉回。
Fed-ADR 是一套面向联邦学习的攻击、检测与恢复框架：编排服务器改写合谋客户端的梯度，使参数服务器的现有防御把投毒更新当成良性，同时在发现后用历史梯度把全局模型原地修回来。
Paper analysisRead analysis
AttacksarXiv 2609.22510Sep 19
特拉维夫大学提出爆炸性提示词：条件触发的间接提示注入可绕过九款生产 Agent
提出条件触发的爆炸提示词，并设计摄入期检测器 DeFuse
arXiv
2609.22510
Published
Sep 19
Layer
Application layer
Setting
AI Agent
Evaluated models
Llama-3.1-8B-Instruct, Llama-3.1-70B-Instruct, Qwen2.5-32B-Instruct and others15
- DefensesDetection and filtering
- AttacksPrompt injection
- TechniqueDormant triggers
- ComponentGuardrails and judges
Abstract论文揭示了条件触发使提示注入绕过防御并在触发时执行工具，提出轻量检测器并在摄入期有效防范。
论文针对大语言模型智能体面临的间接提示注入威胁，研究了利用条件句式实现时间分离的爆炸提示词。
Paper analysisRead analysis
DefensesarXiv 2609.18411Sep 16
可验证行动卡片 VAC：为智能体浏览器提供可信人在回路确认
提出可验证操作卡 VAC，把智能体浏览器确认绑定到真实 DOM 操作
arXiv
2609.18411
Published
Sep 16
Layer
Application layer
Setting
web agent
Evaluated models
gpt-oss-120b, Gemini-2.5-Flash, GPT-4.1 and others6
- DefensesPermissions and approval
- AttacksPrompt injection
- ComponentPermissions and sandboxing
Paper analysisRead analysis
DefensesarXiv 2609.14987Sep 14
ActGuard：面向 LLM Agent 间接提示注入的执行前动作审计框架
提出 ActGuard 执行前动作审计，定位并遮盖间接提示注入的恶意片段
arXiv
2609.14987
Published
Sep 14
Layer
Application layer
Setting
AI Agent
Evaluated models
GPT-4o-mini, Gemini-2.5-Flash, DeepSeek-V4-Flash
- DefensesMonitoring and auditing
- AttacksPrompt injection
Paper analysisRead analysis
DefensesarXiv 2609.07063Sep 7
VERITAS：抗投毒的本地差分隐私图学习协议
提出 VERITAS，用双边背书剪除本地隐私图学习中的投毒节点
arXiv
2609.07063
Published
Sep 7
Layer
Training and data layer
Setting
Models and APIs
Evaluated models
GCN, GraphSAGE, GAT
- DefensesDetection and filtering
- AttacksPoisoning and backdoors
AbstractVERITAS 用分级信任的双边背书剪除 LDP 图学习中的投毒节点，并恢复分类准确率。
VERITAS 是面向本地差分隐私图学习的投毒防御协议，按 trust-but-verify 在保持 ϵ-LDP 的同时剪除伪造节点。
Paper analysisRead analysis
DefensesarXiv 2609.06346Sep 6
面向后门攻击下持续学习的鲁棒动态扩展：净化与选择性恢复
提出持续学习下的净化与选择性恢复框架，抵御逐任务训练投毒后门
arXiv
2609.06346
Published
Sep 6
Layer
Training and data layer
Setting
Models and APIs
Evaluated models
ViT-B/16
- DefensesModel hardening
- AttacksPoisoning and backdoors
- ComponentVision
Abstract作者报告任务内净化与恢复可配合动态专家防御后门，但保证依赖分离条件。
这是一项面向持续图像分类的后门防御研究：模型只使用当前任务数据，同时需要保留过去知识并避免学习恶意监督。
Paper analysisRead analysis
DefensesarXiv 2609.05901Sep 5
KITA：为 LLM Agent 设计密钥隔离的阈值签名授权架构
提出 KITA，把 Agent 批准绑定到规范动作的密钥隔离阈值签名
arXiv
2609.05901
Published
Sep 5
Layer
Application layer
Setting
AI Agent
- DefensesPermissions and approval
- AttacksPrompt injection
- ComponentPermissions and sandboxing
AbstractKITA 把隔离的阈值签名当作精确动作的执行条件，并测量门控、相关性与签名开销。
KITA 是一套密钥隔离的阈值授权架构，用来把模型评审变成执行外部动作的条件。
Paper analysisRead analysis
DefensesarXiv 2609.27760Sep 4
FedMAST：用多轴结构痕迹检测并遏制联邦学习后门
提出 FedMAST，用多轴结构痕迹检测并遏制联邦学习后门
arXiv
2609.27760
Published
Sep 4
Layer
Training and data layer
Setting
Models and APIs
Evaluated models
Tiny ResNet-18
- DefensesDetection and filtering
- AttacksPoisoning and backdoors
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2609.02316Sep 2
Counter-GEO-Bench：现有护栏难挡 GEO 虚假信息
构建 COUNTER-GEO-BENCH，评测生成式搜索中的 GEO 虚假信息防御
arXiv
2609.02316
Published
Sep 2
Layer
Application layer
Setting
LLM applications
Evaluated models
Gemma-4-31B-IT, Qwen-3.5-35B-A3B, Llama-4-Scout-17B-16E and others8
- DefensesDetection and filtering
- AttacksPrompt injection
- ComponentRAG
Abstract论文构建了针对 GEO 虚假信息的首个防御基准，揭示通用护栏的失效与反作用，并提出了有效的轻量对比检测基线。
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2609.05535Sep 2
Mind2Web-Injection：面向网页 Agent 视觉提示注入护栏的证据对齐评测
提出 Mind2Web-Injection 评测网页 Agent 视觉注入护栏
arXiv
2609.05535
Published
Sep 2
Layer
Application layer
Setting
LLM applications
Evaluated models
GPT-5.6-luna, Qwen3-VL-32B, Qwen3-VL-8B and others6
- DefensesDetection and filtering
- AttacksPrompt injection
- ComponentGuardrails and judges
Abstract作者称检测、定位和反事实响应在六个 VLM 上可分开。
Mind2Web-Injection 用来检查网页截图护栏的判决是否对齐到该用的画面证据。。
Paper analysisRead analysis
DefensesarXiv 2609.02127Sep 2
论文提出带类型溯源与断言护栏，防止持久化 AI Agent 把不可信输入当作自身历史
提出类型化溯源与断言护栏，约束持久 Agent 释放自传断言
arXiv
2609.02127
Published
Sep 2
Layer
Application layer
Setting
AI Agent
- DefensesWatermarking and provenance
- AttacksPoisoning and backdoors
- ComponentMemory
Paper analysisRead analysis
DefensesarXiv 2608.30041Aug 31
SkillGuard：面向间接提示注入的 LLM Agent 能力限制机制
提出 SkillGuard，用可达性限制污染后的 Agent 能力
arXiv
2608.30041
Published
Aug 31
Layer
Application layer
Setting
AI Agent
Evaluated models
Gemini 2.5 Flash, Llama3.3-70B
- DefensesPermissions and approval
- AttacksPrompt injection
- ComponentPermissions and sandboxing
Paper analysisRead analysis

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Attack type

Defense type

Impact

System type

Attack surface / entry point

Layer

System component

Space beats statistics, retraining beats ranking: auditing training labels under label-only backdoors

COBRA: A Dual-LLM Architecture for Defending Computer-Using Agents Against Branch-Jumping Attacks

SAGE：基于相似度从少量已验证样本中清洗中毒训练数据

Pincer：用数字分身学习用户偏好并执行动态最小权限

ToolFence: Fine-Grained Authorization for Tool-Calling LLM Agents

Tracekit: A tamper-proof intent-reasoning-action audit system for autonomous coding agents

物理认证联邦学习：为关键水务基础设施的协同异常检测提供安全保障

基于区块链的 Agentic AI 框架：面向安全软件供应链的资源优化与能耗感知

Fed-ADR：用恶意编排服务器协同客户端梯度操纵攻击联邦学习

特拉维夫大学提出爆炸性提示词：条件触发的间接提示注入可绕过九款生产 Agent

可验证行动卡片 VAC：为智能体浏览器提供可信人在回路确认

ActGuard：面向 LLM Agent 间接提示注入的执行前动作审计框架

VERITAS：抗投毒的本地差分隐私图学习协议

面向后门攻击下持续学习的鲁棒动态扩展：净化与选择性恢复

KITA：为 LLM Agent 设计密钥隔离的阈值签名授权架构

FedMAST：用多轴结构痕迹检测并遏制联邦学习后门

Counter-GEO-Bench：现有护栏难挡 GEO 虚假信息

Mind2Web-Injection：面向网页 Agent 视觉提示注入护栏的证据对齐评测

论文提出带类型溯源与断言护栏，防止持久化 AI Agent 把不可信输入当作自身历史

SkillGuard：面向间接提示注入的 LLM Agent 能力限制机制