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Oct 13 · Tuesday

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Found 40 papers

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Attack surface / entry point：Web pages and interfaces Attack surface / entry point：Tool responses Layer：Application layer System component：Memory System component：Guardrails and judges Clear all

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AttacksarXiv 2610.12292Oct 9
The paper proposes an option channel attack: changing one option label can make an agent guardrail allow a prohibited operation
Proposing an option channel attack that renames option labels so that agent guardrails allow prohibited operations
arXiv
2610.12292
Published
Oct 9
Layer
Application layer
Setting
LLM applications
Attacker
Black-box
Evaluated models
LAYA-TD, LAYA-EN, LAYA-ML and others5
- AttacksDetection evasion
- ComponentGuardrails and judges
AbstractThe authors' evaluation indicates that typed models should not serve as final safety arbiters; they are susceptible to off-policy text and option name manipulation, and confidence filtering cannot resist attacks.
Paper analysisRead analysis
DefensesarXiv 2610.07657Oct 6
DEFER1 多智能体防御研究报告受控测试中的攻击成功率下降
实现 DEFER，以确定性检查优先拦截多智能体流水线的内容攻击
arXiv
2610.07657
Published
Oct 6
Layer
Application layer
Setting
Multi-agent systems
Evaluated models
Qwen3-235B-A22B-Instruct-2507, gpt-oss-120b, Llama-4-Scout-17B-16E-Instruct and others5
- DefensesPermissions and approval
- AttacksPrompt injection
- ComponentGuardrails and judges
AbstractDEFER 把确定性检查放在 judge 之前。
DEFER 用来测量多智能体授权流水线里，确定性规则在何处结束、LLM judge 从何处开始。
Paper analysisRead analysis
DefensesarXiv 2610.02861Oct 2
论文提出在 Kubernetes 上保护 AI Agent 的七层纵深防御框架与参考架构
提出 Kubernetes 运维智能体的七层纵深防御框架与参考架构
arXiv
2610.02861
Published
Oct 2
Layer
Application layer
Setting
AI Agent
- DefensesPermissions and approval
- AttacksPrompt injection
- ComponentPermissions and sandboxing
Abstract把运维智能体当不可信租户，用七层控制在完全攻破假设下约束行动。
把 Kubernetes 上的运维智能体当作不可信租户，在假定提示注入已完全控制其工具选择的前提下，用基础设施重建数据面与控制面的边界。要解决的是：智能体读日志、注解、工单或 MCP 工具描述时，这些内容可以变成它带着自身凭据执行的命令。模型对齐和分类器只降低概率。
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2610.03448Oct 2
Study retests 15 prompt injection detectors: benchmark scores poorly predict agent deployment performance
重测提示注入检测器在 Agent 工具输出上的检出与任务阻断
arXiv
2610.03448
Published
Oct 2
Layer
Application layer
Setting
AI Agent
Evaluated models
Horizon-Labs, Wolf Defender, Prismor-1.5B and others15
- DefensesDetection and filtering
- AttacksPrompt injection
- ComponentGuardrails and judges
Abstract论文揭示公开基准无法反映智能体检测器表现，检测能力源于输入格式相似而非通用防御，建议基于工具输出与低误报评测。
Paper analysisRead analysis
DefensesarXiv 2610.00450Oct 1
ZoneClaw 用记忆分区防御 OpenClaw 式计算机使用 Agent 的持久记忆攻击
提出 ZoneClaw，用记忆分区防御计算机使用 Agent 的持久记忆攻击
arXiv
2610.00450
Published
Oct 1
Layer
Application layer
Setting
Browser and computer use
Evaluated models
Claude Sonnet 4.6, GPT-5.5, GLM-5.2 and others4
- DefensesInstruction–data separation
- AttacksPoisoning and backdoors
- ComponentMemory
Paper analysisRead analysis
DefensesarXiv 2609.36603Sep 29
SED: Training-Free Continuous Safety Policy Learning Framework for LLM Agents
提出免训练框架 SED，将有害智能体轨迹蒸馏为可检索安全策略
arXiv
2609.36603
Published
Sep 29
Layer
Application layer
Setting
AI Agent
Evaluated models
DeepSeek-V4-Flash, GLM-5.2, Kimi K3
- DefensesInstruction–data separation
- AttacksPrompt injection
- ComponentMemory
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2609.35886Sep 27
Agentic Commerce Bench：衡量会花钱的智能体的欺诈检测能力
构建支付欺诈基准 ACB，评测花钱智能体的欺诈检测
arXiv
2609.35886
Published
Sep 27
Layer
Application layer
Setting
AI Agent
Evaluated models
gordonguard, pattern judge
- DefensesDetection and filtering
- RiskDangerous agent actions
- ComponentGuardrails and judges
AbstractACB 把支付欺诈与智能体操纵分开测量，作者强调观察权限、误报预算和微支付复核成本。
ACB 是一套智能体支付欺诈基准与检测参考栈，目标是衡量钱是否被不当提取，而不仅是智能体是否接受了恶意指令。
Paper analysisRead analysis
DefensesarXiv 2609.33039Sep 27
TACIT: detecting agent trajectory harm from LLM internal states
提出 TACIT，从冻结内部状态检测智能体有害轨迹
arXiv
2609.33039
Published
Sep 27
Layer
Application layer
Setting
AI Agent
Evaluated models
Qwen3-4B, Llama-3.1-8B, Qwen3-4B-Instruct and others4
- DefensesDetection and filtering
- AttacksPrompt injection
- ComponentGuardrails and judges
Abstract提出基于内部表征的智能体轨迹安全检测方法 TACIT，揭示工具风险表征特性并在多项基准上提升检测表现与效率。
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
Evaluation and benchmarksarXiv 2609.19705Sep 17
SoK 论文提出 FARSIGHT 框架，评测 15 个学术金融 LLM 交易 Agent 的鲁棒性与安全失败
用 FARSIGHT 方案级评测金融 LLM 交易 Agent 的稳健性与安全失败
arXiv
2609.19705
Published
Sep 17
Layer
Application layer
Setting
AI Agent
Evaluated models
ChatGPT Deep Research
- AttacksPrompt injection
- RiskDangerous agent actions
- ComponentMemory
Paper analysisRead analysis
OtherarXiv 2609.12413Sep 11
SoK 综述：智能体 AI 时代的越狱攻击、防御与实践考量
系统梳理智能体执行链路中的越狱攻击与防御并比较推理时防御
arXiv
2609.12413
Published
Sep 11
Layer
Application layer
Setting
AI Agent
Evaluated models
Qwen3.5-4B, Llama-3.1-8B-Instruct
- AttacksJailbreaks
- ComponentMemory
AbstractSoK 将越狱重放到智能体流水线，并用两种模型检验回复层防御的边际与代价。
Mia 等人把越狱安全从单轮回复扩展到智能体的规划、记忆、工具和智能体间通信，并问既有结论在更强原生对齐下是否还成立。
Paper analysisRead analysis
DefensesarXiv 2609.10707Sep 10
面向 AI-SOC 的神经符号防御框架：用 SIEM 解码器与 NeMo Guardrails 阻断日志投毒提示注入
提出神经符号纵深架构，阻断 AI-SOC 的日志投毒与间接提示注入
arXiv
2609.10707
Published
Sep 10
Layer
Application layer
Setting
LLM applications
Evaluated models
Gemma 3 (12B)
- DefensesDetection and filtering
- AttacksPrompt injection
- ComponentGuardrails and judges
Paper analysisRead analysis
DefensesarXiv 2609.04875Sep 4
Forgetting without restart: execution-state forgetting for stateful LLM agents
Proposes execution-state forgetting, using provenance-guided selective replay to purify stateful agents
arXiv
2609.04875
Published
Sep 4
Layer
Application layer
Setting
AI Agent
Evaluated models
Llama-3.1-8B-Instruct, Qwen2.5-7B-Instruct, Mistral-7B-Instruct-v0.3
- AttacksPrompt injection
- ComponentMemory
AbstractSelective replay concatenates the clean prefix with the sanitized suffix, and main-table metrics match full reset.
This work defines forgetting in long-horizon agents as runtime counterfactual equivalence, rather than deleting a plaintext memory.
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
AttacksarXiv 2608.23471Aug 25
InjecMEM: Memory Injection Attacks against LLM Agent Memory Systems
提出 InjecMEM，一次注入 Agent 记忆使后续查询输出预设内容
arXiv
2608.23471
Published
Aug 25
Layer
Application layer
Setting
AI Agent
Evaluated models
Qwen2.5-7B-Instruct, Llama-3.1-8B-Instruct, Mistral-7B-Instruct-v0.3 and others6
- AttacksPoisoning and backdoors
- TechniqueGradient and representation optimization
- ComponentMemory
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2608.16824Aug 18
GEO-Flag：检测与测量面向生成式引擎优化的网页内容
提出 GEOFlagBench 与 IPT，检测并测量生成式引擎优化网页
arXiv
2608.16824
Published
Aug 18
Layer
Application layer
Setting
LLM applications
Evaluated models
ModernBERT-base, Qwen3-0.6B, Gemini-3.5-flash and others11
- DefensesDetection and filtering
- ComponentGuardrails and judges
AbstractGEOFlagBench 与 IPT 用于检测 GEO 页，并估计真实检索中的占比与引用可核验性。
GEO-Flag 把页面级 GEO 检测做成可评测任务，并在发布的检索结果上估计被判为 GEO 的页面占比。
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2608.09476Aug 10
ActBench: A Self-Evolving Benchmark for Behavioral Safety of Collaborative Agents
构建自演化基准 ActBench，评测协作智能体的行为安全
arXiv
2608.09476
Published
Aug 10
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。
Paper analysisRead analysis
DefensesarXiv 2608.02683Aug 3
S³：用多阶段防御提升 LLM 智能体安全
提出 S³，用 guard agent 编排阶段安全技能防御智能体多阶段风险
arXiv
2608.02683
Published
Aug 3
Layer
Application layer
Setting
Embodied systems and robotics
Evaluated models
DeepSeek-V4-Pro, DeepSeek-V4-Flash
- DefensesMonitoring and auditing
- AttacksPoisoning and backdoors
- RiskDangerous agent actions
- ComponentMonitors
Paper analysisRead analysis

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All papers

Attack type

Attack technique

Model risks

Defense type

Impact

System type

Attack surface / entry point

Layer

System component

The paper proposes an option channel attack: changing one option label can make an agent guardrail allow a prohibited operation

DEFER1 多智能体防御研究报告受控测试中的攻击成功率下降

论文提出在 Kubernetes 上保护 AI Agent 的七层纵深防御框架与参考架构

Study retests 15 prompt injection detectors: benchmark scores poorly predict agent deployment performance

ZoneClaw 用记忆分区防御 OpenClaw 式计算机使用 Agent 的持久记忆攻击

SED: Training-Free Continuous Safety Policy Learning Framework for LLM Agents

Agentic Commerce Bench：衡量会花钱的智能体的欺诈检测能力

TACIT: detecting agent trajectory harm from LLM internal states

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

SoK 论文提出 FARSIGHT 框架，评测 15 个学术金融 LLM 交易 Agent 的鲁棒性与安全失败

SoK 综述：智能体 AI 时代的越狱攻击、防御与实践考量

面向 AI-SOC 的神经符号防御框架：用 SIEM 解码器与 NeMo Guardrails 阻断日志投毒提示注入

Forgetting without restart: execution-state forgetting for stateful LLM agents

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

InjecMEM: Memory Injection Attacks against LLM Agent Memory Systems

GEO-Flag：检测与测量面向生成式引擎优化的网页内容

ActBench: A Self-Evolving Benchmark for Behavioral Safety of Collaborative Agents

S³：用多阶段防御提升 LLM 智能体安全