CredLeak-Bench
提出 CredLeak-Bench，评测 Agent 面对钓鱼的凭据泄露与恢复
- arXiv
- 2610.08871
- Published
- Layer
- Application layer
- Setting
- web agent
- Evaluated models
- Claude Sonnet 5, Claude Opus 4.8, Gemini 3.6 Flash and others7
- AttacksPrompt injection
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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
- Layer
- Application layer
- Setting
- coding agent
- AttacksPrompt injection
- ComponentPermissions and sandboxing
EvoRiskBench: An evolutionary benchmark for runtime security risks of workspace agents
构建 EvoRiskBench 评测工作区 Agent 的运行时安全风险
- arXiv
- 2610.03153
- Published
- Layer
- Application layer
- Setting
- AI Agent
- Evaluated models
- GPT-5.6 Sol, DeepSeek-V4-Pro-0813, Claude Opus 5
- AttacksPrompt injection
- ComponentMCP and skills
Abstract论文构建了工作空间智能体运行时风险基准 EvoRiskBench，发现高执行力智能体在 IPI 下面临较高安全风险。
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
- 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。
pikit：面向间接提示注入研究与评测的可组合工具包
构建可组合工具包 pikit，评测编码智能体的间接提示注入
- arXiv
- 2609.36817
- Published
- Layer
- Application layer
- Setting
- coding agent
- AttacksPrompt injection
Abstractpikit 提供组合式间接注入评测。
pikit 是面向间接提示注入研究的组合式工具包，主要贡献是组织攻击、投递通道、防御和判定流程，而非提出新的攻击优化算法。
CoSec：面向社区场景的 LLM Agent 安全基准
用 CoSec 评测社区 Agent 是否越界披露受保护信息
- arXiv
- 2609.34790
- Published
- Layer
- Application layer
- Setting
- AI Agent
- Evaluated models
- OpenClaw with Gemini 3.1 Pro, OpenClaw with Qwen 3.8 Max, OpenClaw with MiniMax M2.5 and others13
- AttacksPrompt injection
- ComponentMemory
研究审计 Agent 安全评测缺陷：工具调用中介在间接提示注入下的有效评测框架
提出卡点评测框架以审计智能体间接提示注入评测的测量缺陷
- arXiv
- 2609.32691
- Published
- Layer
- Application layer
- Setting
- AI Agent
- Evaluated models
- gpt-5.6-terra, llama3.1:8b, gemma4:12b and others4
- AttacksPrompt injection
Abstract论文揭示了 IPI 评测中导致指标虚高的系统性缺陷，通过构建有效评测框架纠正了多项安全与模型能力结论。
Cave-Bench：虚假指控下 LLM 智能体最高 60.06% 的运行会破坏正确工作
提出 CAVE-BENCH，评测智能体在虚假指责下破坏已完成的正确工作
- arXiv
- 2609.32616
- Published
- Layer
- Application layer
- Setting
- coding agent
- Evaluated models
- Claude-Sonnet-5, Claude-Opus-5, GPT-5.6-Sol and others14
- AttacksPrompt injection
- RiskDangerous agent actions
Abstract论文提出了评估智能体在虚假指责下破坏已有正确成果的 CAVE-BENCH，发现强模型易推翻已知证据，提出了门控缓解方案。
AgentXploit：面向 AI Agent 的仓库到运行时自动化红队系统
提出 AgentXploit，从代码仓库发现攻击路径并在运行时验证利用
- arXiv
- 2609.31318
- Published
- Layer
- Application layer
- Setting
- AI Agent
- Evaluated models
- GPT-4.1
- AttacksPrompt injection
研究提出 ACE 语料库，评估内核级证据对 Agent 安全检测的有效性
构建 ACE 语料，评测内核级证据对 Agent 攻击检测的有效性
- arXiv
- 2609.28915
- Published
- Layer
- Application layer
- Setting
- coding agent
- Evaluated models
- AdaBoost, XGBoost, TabPFN and others10
- AttacksPrompt injection
- ComponentMCP and skills
AbstractACE 用配对的内核与应用层证据表明，syscall 痕迹可判别智能体攻击，两层组合通常更好。
ACE 是一份把智能体会话的内核 syscall 痕迹与应用层清单、转录配对起来的检测语料和基准。。
DUMA-Bench：评估 LLM Agent 安全的双控多智能体基准
提出双控多智能体基准 DUMA-Bench，测量对抗环境下的策略违反
- arXiv
- 2609.24662
- Published
- Layer
- Application layer
- Setting
- Multi-agent systems
- Evaluated models
- GPT-5, GPT-5-mini, GPT-5-nano and others14
- AttacksPrompt injection
AbstractDUMA-Bench 用双控交互评测智能体安全。
DUMA-Bench 把 τ²-bench 的双控交互做成智能体安全评测，用来看策略违反如何随用户、模型和环境一起出现。
Emergence World：对长时程多智能体系统开展对抗压力测试
提出 Emergence World，对长时程多智能体系统开展对抗压力测试
- arXiv
- 2609.17320
- Published
- Layer
- Application layer
- Setting
- Multi-agent systems
- Evaluated models
- Claude Opus 4.8, DeepSeek v4 Pro, Gemini 3.5 Flash and others9
- AttacksPrompt injection
- RiskMisalignment and value divergence
- ComponentMemory
Abstract作者通过长期平行世界实验指出模型对齐不具备可组合性，单模型群体会产生社会奉承、协同退缩及语言不透明等失效。
MMPIBench：多模态提示注入对六种 Agent 框架的跨框架评测
用 MMPIBench 评测多模态提示注入对 Agent 框架的影响
- arXiv
- 2609.09404
- Published
- Layer
- Application layer
- Setting
- AI Agent
- Evaluated models
- Claude Opus 4.8, GPT-5.4, Gemini 3.1 Pro and others6
- AttacksPrompt injection
- TechniqueCross-modal payloads
- ComponentVision
Abstract论文提出 MMPIBench 评测多模态提示注入对智能体的影响，发现模型主导行为、尝试远超完成，音频通道缺乏防护。
ActBench：面向协作智能体行为安全的自演化基准
构建自演化基准 ActBench，评测协作智能体的行为安全
- arXiv
- 2608.09476
- Published
- Layer
- Application layer
- Setting
- AI Agent
- Evaluated models
- Claude-Opus-4.8, Claude-Sonnet-4.6, GPT-5.5 and others15
- AttacksPrompt injection
- ComponentPermissions and sandboxing
AbstractActBench 通过自演化搜索与双证据重构评测轨迹行为安全，揭示基础模型决策差异主导了协同智能体的操作风险。
OpenART 提出环境演化红队框架
构建 OpenART 竞技场，以 EMHA 演化持久环境评测智能体安全
- arXiv
- 2608.00677
- Published
- Layer
- Application layer
- Setting
- AI Agent
- Evaluated models
- GPT-5.5, Claude-Opus-4.8, GLM-5.2 and others5
- AttacksPrompt injection
- TechniqueAutomated search
IssueTrojanBench: evaluating AI coding agents against malicious issue requests
Proposes IssueTrojanBench to evaluate coding agents' execution of malicious issues
- arXiv
- 2607.20759
- Published
- Layer
- Application layer
- Setting
- coding agent
- Evaluated models
- GPT-5.3 Codex, GPT-5.4, Sonnet 4.6
- AttacksPrompt injection
论文评测 Claude Code 与 Codex 记忆文件中的提示注入风险
评测编码 Agent 记忆文件中提示注入的跨会话影响
- arXiv
- 2607.14611
- Published
- Layer
- Application layer
- Setting
- coding agent
- Evaluated models
- Claude Haiku 4.5, Claude Opus 4.7, GPT-5.2 and others4
- AttacksPoisoning and backdoors
- ComponentMemory
Abstract已植入记忆文件的载荷可以跨会话影响 Claude Code 与 Codex，攻击成功与载荷是否留下是两种不同的失败。
Bad Memory 在沙箱合成工作区里评测文件型持久记忆上的提示注入，比较载荷能否改变当前会话，以及能否在后续会话中留下或叠加。
Researchers propose MemGhost, achieving covert memory injection into persistent personal agents
提出 MEMGHOST，用单封邮件对持久个人 Agent 做跨会话隐蔽记忆注入
- arXiv
- 2607.05189
- Published
- Layer
- Application layer
- Setting
- AI Agent
- Evaluated models
- GPT-5.4, Claude Sonnet 4.6, Claude Sonnet 4.5 and others6
- AttacksPoisoning and backdoors
- TechniqueAutomated search
- ComponentMemory
Abstract总结了论文的研究背景、双代理优化方法 MEMGHOST、全周期实验结果以及作者对记忆信任边界的启示。
本文研究了持久化个人智能体面临的隐蔽记忆注入威胁，提出了一套兼顾写入、隐蔽与跨会话生效的自动化生成框架与全周期评测基准。
研究者系统研究 LLM 智能体记忆投毒攻击并提出 MPBench 基准
系统研究 LLM 智能体记忆投毒并构建基准 MPBench
- arXiv
- 2606.04329
- Published
- Layer
- Application layer
- Setting
- AI Agent
- Evaluated models
- Meta-Llama-3.1-70B-Instruct, Deberta-v3-base, Llama-3.1-8B-Instruct and others5
- AttacksPoisoning and backdoors
- TechniqueDormant triggers
- ComponentMemory
Abstract系统研究了智能体记忆投毒风险，揭示了写入渠道漏洞，构建了 MPBench 基准并指出提示注入防御的局限。
Uber proposes ADR enterprise-level agent detection system, deployed on 7,200 hosts for ten months
提出企业级检测系统 ADR，识别编程 Agent 的提示注入与恶意 MCP
- arXiv
- 2605.17380
- Published
- Layer
- Application layer
- Setting
- coding agent
- DefensesDetection and filtering
- AttacksPrompt injection
- ComponentMCP and skills
Abstract提出了端点因果遥测、两级在线分流与离线红队闭环的 ADR 系统，并在生产环境中验证了有效性。