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System type：LLM applications System type：coding agent System type：Browser and computer use System type：Multi-agent systems System component：RAG System component：Permissions and sandboxing Clear all

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AttacksarXiv 2610.11932Oct 8
A Fudan team measures the citation fragility of AI search: ordinary posts can enter citations and answer text
Measures citation fragility in AI search, demonstrating that ordinary public posts can enter citations and answers.
arXiv
2610.11932
Published
Oct 8
Layer
Application layer
Setting
LLM applications
Evaluated models
Grok, Doubao, ChatGPT and others10
- AttacksPoisoning and backdoors
- ComponentRAG
AbstractThe authors claim the paper quantifies vulnerabilities in AI search caused by citation preferences and low posting thresholds; tests show public web posts can penetrate AI citations, and they call for auditing the citation supply chain.
Paper analysisRead analysis
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 2610.08571Oct 6
RAG-PIBench：可信 RAG 系统中提示注入检测的泄漏感知基准
提出 RAG-PIBench，评测 RAG 上下文中提示注入检测器
arXiv
2610.08571
Published
Oct 6
Layer
Application layer
Setting
LLM applications
Evaluated models
DistilBERT-base-uncased, DeBERTa-v3-base, MiniLM
- DefensesDetection and filtering
- AttacksPrompt injection
- ComponentRAG
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2610.07639Oct 6
HarnessSecurity-Bench horizontally evaluates the security mechanisms of six coding agent frameworks.
评测编码智能体 harness 安全机制在对抗任务中的防护效果
arXiv
2610.07639
Published
Oct 6
Layer
Application layer
Setting
coding agent
- AttacksPrompt injection
- ComponentPermissions and sandboxing
Paper analysisRead analysis
DefensesarXiv 2610.07276Oct 6
SAFESHIELD：小语言模型部署时安全的决策组织框架
提出 SAFESHIELD，组织小语言模型部署时的安全决策
arXiv
2610.07276
Published
Oct 6
Layer
Application layer
Setting
LLM applications
Evaluated models
Qwen3-4B-Instruct
- AttacksJailbreaks
- RiskHallucination and fabrication
- ComponentRAG
AbstractSAFESHIELD 用可审计决策轨迹组织 SLM 部署时的四项安全决策，并报告协调依赖影响端到端结果。
SAFESHIELD 是面向小语言模型的部署时安全框架，把运行时安全从单项审核改写成可检查的决策过程。
Paper analysisRead analysis
DefensesarXiv 2610.05826Oct 5
研究者提出临时可见性协议，限制持续摄入 RAG 向量库的投毒暴露
提出临时可见性协议，限制持续摄入 RAG 向量库的投毒暴露
arXiv
2610.05826
Published
Oct 5
Layer
Application layer
Setting
LLM applications
- DefensesInstruction–data separation
- AttacksPoisoning and backdoors
- ComponentRAG
Abstract用截止期限制未核验可见时长。
这篇工作给持续摄入的 RAG 向量库一个失败关闭的临时可见性协议，把协议能限制的未核验暴露和依赖检测器的中毒暴露分开。
Paper analysisRead analysis
DefensesarXiv 2610.05246Oct 4
研究测量策略网关跨平台命令裁决的能力边界
提出策略门裁决已实现动作，并测量跨平台命令拦截边界
arXiv
2610.05246
Published
Oct 4
Layer
Application layer
Setting
coding agent
Evaluated models
claude-opus-5, deepseek-v4.1-flash, gpt-6-astra
- DefensesPermissions and approval
- RiskDangerous agent actions
- ComponentPermissions and sandboxing
Paper analysisRead analysis
Analysis and theoryarXiv 2610.04888Oct 4
Research: fact-checking evaluations may mix in future evidence unknowable at the time of the claim
测量事实核查评测混入的时序证据与参数泄漏
arXiv
2610.04888
Published
Oct 4
Layer
Application layer
Setting
LLM applications
Evaluated models
Qwen3-14B, phi-4
- ComponentRAG
Abstract两条后见通道会抬高事实核查分数。
作者量的是 LLM 事实核查分数里，有多少来自 claim 当时还不可知的信息。通道有两条：预训练记住的结局，以及检索到的、写于 claim 之后的文档。
Paper analysisRead analysis
Analysis and theoryarXiv 2610.04860Oct 4
Research reveals that text watermarks induce model hallucinations and proposes two mitigation interventions
分析文本水印在 RAG 中诱发事实幻觉的机制并提出干预
arXiv
2610.04860
Published
Oct 4
Layer
Model layer
Setting
LLM applications
Evaluated models
LLaMA 3.1 8B Instruct, Mistral-7B-Instruct-v0.3, Qwen2.5-14B-Instruct
- DefensesInference-time intervention
- RiskHallucination and fabrication
- ComponentRAG
Abstract论文揭示了生产环境文本水印导致模型事实退化的失效模式，分析了解码机制并提出针对性干预。
论文研究生产级文本水印对大语言模型事实准确性的影响，分析了水印诱发幻觉的失效模式并评估了针对性缓解方案。
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2610.04691Oct 4
RAGStress：评测知识库退化下 RAG 鲁棒性的受控基准
提出 RAGStress 受控基准，评测知识库退化下的 RAG
arXiv
2610.04691
Published
Oct 4
Layer
Application layer
Setting
LLM applications
Evaluated models
Claude Sonnet 4, Claude Opus 4.6, DeepSeek R1 and others5
- ComponentRAG
Abstract作者用 RAGStress 比较五系统。
RAGStress 是知识库损坏下的 RAG 受控压力测试。作者明确不把它当作模型排行榜。
Paper analysisRead analysis
DefensesarXiv 2610.02456Oct 2
SideKernel：面向 macOS 上 AI 编码 Agent 的 microVM 沙箱
为 macOS 编程 Agent 构建可用的 microVM 沙箱 SideKernel
arXiv
2610.02456
Published
Oct 2
Layer
Application layer
Setting
coding agent
- DefensesPermissions and approval
- RiskDangerous agent actions
- ComponentPermissions and sandboxing
Paper analysisRead analysis
AttacksarXiv 2610.02373Oct 2
HDI 攻击：篡改 GraphRAG 辅助结构可致 88–94% 攻击成功率
提出 HDI 攻击以篡改 GraphRAG 辅助结构破坏检索
arXiv
2610.02373
Published
Oct 2
Layer
Application layer
Setting
LLM applications
Evaluated models
Microsoft GraphRAG, HippoRAG2
- AttacksPoisoning and backdoors
- ComponentRAG
Abstract论文形式化了 GraphRAG 辅助模式级实体攻击面，提出 HDI 攻击并验证其攻击效果。
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
AttacksarXiv 2610.01871Oct 1
研究者提出 IMMRAG 攻击，可从多模态 RAG 数据存储中提取医学与文档图像
提出 imMRAG 攻击，从多模态 RAG 私有图像库提取内容
arXiv
2610.01871
Published
Oct 1
Layer
Application layer
Setting
LLM applications
Attacker
Black-box
Evaluated models
Lumina-mGPT, Gemini 2.5 Flash Image Preview, CLIP ViT-B/16 and others5
- AttacksExtraction and theft
- TechniqueCross-modal payloads
- ComponentRAG
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2610.01218Oct 1
AGO AI Quality Gate：面向 RAG 的证据优先发布决策框架
提出面向 RAG 的证据优先发布框架 AGO，并评测评审 adherence 判别
arXiv
2610.01218
Published
Oct 1
Layer
Application layer
Setting
LLM applications
Evaluated models
gpt-4o, gpt-4.1-nano
- ComponentGuardrails and judges
AbstractAGO 把缺失证据和评审误差写成显式发布状态，并要求按领域验证评审。
AGO 是部署在企业 RAG 评估业务中的自托管发布门控，用来决定一个版本应放行、人工复核还是拦截。
Paper analysisRead analysis
AttacksarXiv 2610.00392Sep 30
A2A-TIBA：针对 A2A 协议 LLM 智能体的间接提示注入攻击与三层同构攻防模型
提出 A2A-TIBA，经 A2A 对等任务间接注入并植入常驻回调
arXiv
2610.00392
Published
Sep 30
Layer
Application layer
Setting
Multi-agent systems
Evaluated models
MiniMax-M3, deepseek-v4-pro, glm-5.3 and others8
- AttacksPrompt injection
- TechniqueMulti-turn escalation
- ComponentMCP and skills
AbstractA2A-TIBA 一次植入后绕过智能体，GDA 用四类结果定位防线，ELA-ITL 把信封标注列为第三层。
A2A-TIBA 把间接提示注入和绕过放进 A2A：gray-box 攻击者已能投递任务，诱导目标部署常驻回调程序后，下令和外传不再经过智能体。
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2609.39146Sep 30
MADBench：多智能体辩论安全性评测基准发布
提出 MADBench，评测多智能体辩论是吸收还是放大对抗影响
arXiv
2609.39146
Published
Sep 30
Layer
Application layer
Setting
Multi-agent systems
Evaluated models
gpt-4o, GPT-5, GPT-6 Astra
- AttacksPrompt injection
- ComponentRAG
AbstractMADBench 显示辩论能吸收部分准确率攻击，同时放大未授权读写。
MADBench 是一个评测多智能体辩论安全性的基准，用来看攻击是被辩论吸收还是被放大。
Paper analysisRead analysis
DefensesarXiv 2610.00371Sep 30
FlowReview：以授权配对评测控制多智能体组合信息流
提出 FlowReview，用授权配对与确定性提交门控控制多智能体组合信息流
arXiv
2610.00371
Published
Sep 30
Layer
Application layer
Setting
Multi-agent systems
Evaluated models
Sonnet 4.5, Haiku 4.5, Qwen3-32B and others5
- DefensesPermissions and approval
- AttacksPrompt injection
- ComponentPermissions and sandboxing
Abstract配对标准要求拒绝禁止使用并完成授权使用，FlowReview 把三项审查接到执行。
这篇工作把多智能体安全写成一件必须同时成立的事：拦住组合信息所启用的禁止使用，并完成同一场景里任务所需的授权使用。
Paper analysisRead analysis
DefensesarXiv 2609.38983Sep 30
The paper proposes an Approval Laundering taxonomy and empirically shows that Claude Code's approval and execution binding fails
形式化编程 Agent 的审批洗白并提出 HMAC 权能令牌防御
arXiv
2609.38983
Published
Sep 30
Layer
Application layer
Setting
coding agent
Evaluated models
Claude Code (v2.1.197, opus[1m] / claude-sonnet-5-ccmax), Codex CLI (@openai/codex v0.154.0)
- DefensesPermissions and approval
- RiskDangerous agent actions
- ComponentPermissions and sandboxing
Abstract系统揭示编程智能体审批洗白风险，提出七字段权能防御机制，证实其能消除身份时序偏离但难防副作用偏离。
Paper analysisRead analysis
DefensesarXiv 2610.00328Sep 29
ContractRL：面向可审计工具调用修复的屏蔽组相对策略优化
提出契约约束的工具调用修复协议，在执行前拦截非法补丁
arXiv
2610.00328
Published
Sep 29
Layer
Application layer
Setting
LLM applications
Evaluated models
2B rank-8 LoRA, 0.8B rank-4 LoRA
- DefensesPermissions and approval
- RiskDangerous agent actions
- ComponentPermissions and sandboxing
Paper analysisRead analysis

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