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Attack type：Prompt injection Attack type：Poisoning and backdoors Defense type：Detection and filtering 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
AttacksarXiv 2610.10612Oct 7
PyCache Trap 利用 Python 缓存替换绕过七款 Agent Skill 扫描器
提出 PyCache Trap，用字节码缓存替换绕过 Agent 技能扫描器
arXiv
2610.10612
Published
Oct 7
Layer
Application layer
Setting
coding agent
Evaluated models
CISCO, HSS, AUDITOR and others8
- DefensesDetection and filtering
- AttacksDetection evasion
- TechniqueDefense-targeted
- ComponentMCP and skills
Abstract论文剖析智能体技能审查与执行脱节盲区，提出 PyCache Trap 攻击并构建基于可信复现的 EAV 防御。
这篇论文研究了智能体技能（Agent Skills）安全扫描中的“审查–执行脱节”问题，并提出了对应的攻击验证与防御方案。
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
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
Analysis and theoryarXiv 2610.06001Oct 5
AgentSpy: Observing AI agent behavior at the system call layer and detecting skill injection attacks
提出 AgentSpy，在系统调用层观测 Agent 行为并检测技能注入
arXiv
2610.06001
Published
Oct 5
Layer
Application layer
Setting
coding agent
Evaluated models
gpt-5.6-terra, deepseek-v4-flash, glm-5.3-flash and others4
- DefensesDetection and filtering
- AttacksPoisoning and backdoors
- ComponentMCP and skills
Abstract提出了系统级智能体监控框架 AgentSpy，揭示出通过测试背后的非预期行为与注入风险。
本研究提出了面向 AI 智能体的系统级外部监控与行为分析框架 AgentSpy。
Paper analysisRead analysis
DefensesarXiv 2610.04412Oct 3
论文研究 LLM 数字孪生参与增强民主时的提示注入脆弱性
提出检测、结构化与 GSPO 流程，降低共识生成的提示注入错位
arXiv
2610.04412
Published
Oct 3
Layer
Application layer
Setting
LLM applications, Multi-agent systems
Evaluated models
LLaMA-2 7B, LLaMA-3 8B, GPT 3.5 Turbo and others14
- DefensesDetection and filtering
- AttacksPrompt injection
- ComponentGuardrails and judges
Abstract三章依次做个体偏好预测、政党知识图谱审议，以及共识生成对提示注入的脆弱性与一条防御流程。
这是一篇把 LLM 数字孪生放进增强民主的博士论文，分个体表示、政党聚合和共识生成的提示注入三章。
Paper analysisRead analysis
DefensesarXiv 2610.03073Oct 2
SecJev：为 System One 决策模型引入安全专业知识
提出 SecJev，用场景加权训练把决策模型适配为安全类型化判断
arXiv
2610.03073
Published
Oct 2
Layer
Training and data layer
Setting
Models and APIs
Evaluated models
SecJev-0.8B, SecJev-2B, SecJev-4B and others4
- DefensesDetection and filtering
- AttacksPrompt injection
- ComponentGuardrails and judges
AbstractSecJev 用共享打分器做安全决策，0.8B 的 task-macro 超过 Kev-9B。
SecJev 是 0.8B 到 9B 的安全决策模型族，作者称据其所知是首个面向安全的 Jev-like 家族。
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
Evaluation and benchmarksarXiv 2610.03448Oct 2
研究重测 15 个提示注入检测器：基准分数难以预测 Agent 部署表现
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 2610.01788Oct 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 2609.36879Sep 29
SKILLLITE：用小模型做证据引导的恶意 Agent Skill 审计
提出证据引导的 SKILLLITE，审计部署前恶意 Agent Skill
arXiv
2609.36879
Published
Sep 29
Layer
Application layer
Setting
AI Agent
Evaluated models
Gemma4:e4b, Qwen3.5:9B, DeepSeek-R1:8B and others4
- DefensesDetection and filtering
- AttacksPoisoning and backdoors
- ComponentMCP and skills
AbstractSKILLLITE 把证据抽取外置，让紧凑 LLM 只做意图条件下的风险裁决，三个基准上 F1 高于所列基线。
SKILLLITE是面向紧凑、可本地部署 LLM 的恶意 Agent Skill 预执行审计框架，作者单位为南洋理工大学。
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 2609.33039Sep 27
TACIT：从 LLM 内部状态检测 Agent 轨迹危害
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
DefensesarXiv 2609.30657Sep 25
研究者提出面向邮件 Agent 的攻击链建模提示注入检测框架
提出面向邮件 Agent 的攻击链提示注入检测框架
arXiv
2609.30657
Published
Sep 25
Layer
Application layer
Setting
AI Agent
Evaluated models
Proposed framework
- DefensesDetection and filtering
- AttacksPrompt injection
- ComponentGuardrails and judges
Abstract攻击链与动作一致性融合改善了严格检测的平均 F1，但迁移、误报和攻击召回仍需分别看。
这是一项邮件智能体提示注入防御的离线建模与评测研究：作者把攻击文本是否恶意、攻击是否推进到工具操作、最终参数是否达标分开处理。
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2609.29429Sep 24
Jev 被用于零样本检测十类对齐失败，中位 AUROC 达 0.886
提出 RLCDAlignBench，零样本检测多种对齐失败
arXiv
2609.29429
Published
Sep 24
Layer
Prompt layer
Setting
Models and APIs
Evaluated models
Jev (jev-1.13.0)
- DefensesDetection and filtering
- AttacksJailbreaks
- RiskMisalignment and value divergence
- ComponentGuardrails and judges
Abstract一次通用问题已能给多数对齐失败排序，大的误差来自状态和标签。
RLCDAlignBench 测量用强化学习做校准决策的 Jev，能否当作对齐失败的零样本检测器。
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 2609.16098Sep 14
研究者提出面向工具集成 LLM Agent 的通用对抗攻击防御方法
提出 ATF 与 NTR，过滤或恢复工具集以阻止 Agent 调用攻击工具
arXiv
2609.16098
Published
Sep 14
Layer
Application layer
Setting
AI Agent
Evaluated models
Gemma2-9B, Qwen2-7B, LLaMA3-8B and others7
- DefensesPermissions and approval
- AttacksPrompt injection
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2609.15017Sep 14
PIDS-Bench：在过度防御、混淆与分布偏移下评测提示注入检测器
提出 PIDS-Bench，评测提示注入检测器在过度防御与分布偏移下的表现
arXiv
2609.15017
Published
Sep 14
Layer
Application layer
Setting
LLM applications
Evaluated models
TF-IDF + logistic regression, DistilBERT, DeBERTa-v3-FT and others7
- DefensesDetection and filtering
- AttacksPrompt injection
- ComponentGuardrails and judges
AbstractPIDS-Bench 显示高分布内 F1 仍会过度拦截外部来源的安全相关良性输入。
PIDS-Bench 是一个冻结的检测器级基准，用来在固定阈值下同时看提示注入检测和良性误报。
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

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