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Oct 12 · Monday

All papers

Found 9 papers

Filter3

Attack type

Prompt injection9 Poisoning and backdoors4

Defense type

Permissions and approval4

Impact

Unauthorized actions4

System type

AI Agent9

Attack surface / entry point

User input9 Web pages and interfaces7 Documents and messages8 Tool responses15 Tool and skill descriptions38 Memory writes5 Inter-agent messages5 Distribution channels24

Layer

Application layer9

System component

MCP and skills9 Memory10 RAG3 Guardrails and judges17 Monitors11 Permissions and sandboxing10

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Attack type：Prompt injection Attack surface / entry point：User input System component：MCP and skills Clear all

Another 265papers do not have category tags yet and are not included in filtered results.

AttacksarXiv 2610.12524Oct 8
An exploration of attacks on AI agent systems
重测并扩展对 AI Agent 的提示注入，并用 Agent Card 欺骗操纵 A2A 选择
arXiv
2610.12524
Published
Oct 8
Layer
Application layer
Setting
AI Agent
Evaluated models
Ollama Mistral 0.9.5, llama3-8b-8192, gemini-2.0-flash-lite and others5
- AttacksPrompt injection
- TechniqueEncoding and obfuscation
- ComponentMCP and skills
Abstract单智能体过滤与分类可被格式或措辞绕过，A2A 选卡还会偏向名称和描述。
作者在自建的 LangChain、AutoGen 和 A2A 系统上重测提示注入，并改 Agent Card 看选择是否偏移。摘要称试验主要凸显单智能体弱点，同时把攻击扩到多智能体。
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2610.04378Oct 3
COPEX：面向 MCP 智能体的受控攻击评测基准发布
提出 COPEX 基准，评测 MCP 智能体对多入口攻击的抵抗力
arXiv
2610.04378
Published
Oct 3
Layer
Application layer
Setting
AI Agent
Evaluated models
Claude Opus 4.7, Claude Sonnet 4.6, GPT-5.5 and others9
- AttacksPrompt injection
- ComponentMCP and skills
Paper analysisRead analysis
DefensesarXiv 2610.02432Oct 2
论文提出 LLM 输入扰动鲁棒性度量，并发布 ASA 攻击与 AttestMCP 防护
提出 ASA 与 AttestMCP，度量并降低 LLM 对输入序列扰动的敏感
arXiv
2610.02432
Published
Oct 2
Layer
Application layer
Setting
LLM applications, AI Agent
Attacker
Black-box
Evaluated models
Gemma-3-27B-Instruct, Gemma-3-4B-Instruct, Llama-3.2-3B-Instruct and others12
- DefensesDetection and filtering
- AttacksDetection evasion
- ComponentGuardrails and judges
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 2609.22573Sep 19
研究提出面向企业 MCP 的零信任授权与工具发现方案
提出企业 MCP 零信任授权与发现扩展，在架构上拒绝提示注入下的越权调用
arXiv
2609.22573
Published
Sep 19
Layer
Application layer
Setting
AI Agent
Evaluated models
gpt-5, claude-sonnet-4-6, claude-opus-4-7 and others6
- DefensesPermissions and approval
- AttacksPrompt injection
- ComponentMCP and skills
Abstract同一份按工具声明同时管 MCP 的发现、列表和调用，使注入不能扩大已有凭证的权限。
作者把零信任放在 MCP 工具调用边界：智能体即使被提示注入，也不能调用其凭证范围之外的工具。问题来自三点部署性质。调用意图会随不可信上下文改变。
Paper analysisRead analysis
AttacksarXiv 2608.30441Aug 31
ECLIPSE：针对长程 Agent 系统的自演化隐蔽提示注入攻击
提出 ECLIPSE 双通道提示注入，劫持长程 Agent 的动作轨迹
arXiv
2608.30441
Published
Aug 31
Layer
Application layer
Setting
AI Agent
Evaluated models
DeepSeek, GPT-4o, Claude 4.8 Opus and others7
- AttacksPrompt injection
- TechniqueAutomated search
- ComponentMCP and skills
Paper analysisRead analysis
DefensesarXiv 2609.29647Aug 29
AgentKernel：面向 Agent 的可信原生操作系统内核
提出 AgentKernel，用四支柱强制中介 Agent 的输入、工具与记忆
arXiv
2609.29647
Published
Aug 29
Layer
Application layer
Setting
AI Agent
- DefensesInstruction–data separation
- AttacksPrompt injection
- ComponentPermissions and sandboxing
AbstractAgentKernel 是四支柱智能体 OS 设计，安全论证未附带实测数字。
AgentKernel 是把安全做成组织原则的智能体 OS 架构，不是带跑分的攻防评测。
Paper analysisRead analysis
DefensesarXiv 2608.21101Aug 21
ClawSentry：面向自主 LLM Agent 的渐进式多层安全监控网关
提出 ClawSentry 多层监控网关，在技能准入和运行时拦截恶意技能与隐藏指令
arXiv
2608.21101
Published
Aug 21
Layer
Application layer
Setting
coding agent
Evaluated models
Codex/GPT-5.4, Codex/GPT-5.5, Claude Code/GLM-5.1 and others6
- DefensesMonitoring and auditing
- AttacksPoisoning and backdoors
- ComponentMCP and skills
AbstractClawSentry 用包准入、三级运行时门控和反绕过，压低 skill 攻击并保住干净任务。
ClawSentry 是挂在智能体运行时之外的安全监督网关，用来同时盖住 skill 执行的多个生命周期边界，并把已被拒绝的效果记到会话里。
Paper analysisRead analysis
AttacksarXiv 2608.16246Aug 17
CompoSkill 框架利用逐个通过扫描的 Agent 技能组合发起攻击
提出 CompoSkill，将逐个通过扫描的技能编排为攻击链
arXiv
2608.16246
Published
Aug 17
Layer
Application layer
Setting
AI Agent
Evaluated models
GPT-5.4, Gemini-3.1-flash, DeepSeek-V4 and others4
- AttacksPrompt injection
- TechniqueDefense-targeted
- ComponentMCP and skills
Abstract论文分析了智能体技能组合风险，指出单包安全认证状态不具备可组合性，需转向路径级防护。
Paper analysisRead analysis

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