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Oct 12 · Monday

All papers

Found 57 papers

Filter4

System type

LLM applications21 AI Agent36 coding agent3 web agent3 Multi-agent systems8

Attack surface / entry point

User input16 Web pages and interfaces13 Documents and messages20 Tool responses14 Tool and skill descriptions3 Code repositories and dependencies3 Retrieval corpus13 Memory writes4 Inter-agent messages4 Images, audio and video3 Distribution channels5

Layer

Prompt layer9 Model layer1 Application layer56

System component

MCP and skills4 Memory12 RAG10 Guardrails and judges11 Vision4 Multimodal4

Select any value within a category; different categories must all match. Counts include the other filters you have selected.Another 233papers have no tags yet and are not included in filtered results.

Attack type：Prompt injection Impact：Output manipulation Layer：Model layer Layer：Application layer Clear all

Another 233papers do not have category tags yet and are not included in filtered results.

DefensesarXiv 2610.11942Oct 8
LGWM: Verifying GUI agent screen transitions with action-conditioned world models
Proposes LGWM, using an action-conditioned world model to verify GUI agent screen transitions.
arXiv
2610.11942
Published
Oct 8
Layer
Application layer
Setting
Browser and computer use
Evaluated models
LGWM
- DefensesMonitoring and auditing
- AttacksPrompt injection
- ComponentMonitors
Paper analysisRead analysis
DefensesarXiv 2610.11634Oct 8
LTBD：用可学习信任边界分隔符防御提示注入
提出 LTBD，用可学习分隔符隔离可信指令与不可信数据
arXiv
2610.11634
Published
Oct 8
Layer
Application layer
Setting
LLM applications
Evaluated models
Llama3-8B-Instruct, Llama3.1-8B-Instruct, Falcon3-7B-Instruct and others4
- DefensesInstruction–data separation
- AttacksPrompt injection
AbstractLTBD 只训练四个分隔符嵌入。
LTBD 是一种不改基座权重的提示注入防御：只学习用来标出可信指令与不可信数据边界的四个 token 嵌入。
Paper analysisRead analysis
AttacksarXiv 2610.09264Oct 7
研究者提出 PackHallu：经规则文件提示注入对编码 Agent 发起包幻觉攻击
提出 PackHallu，经规则文件提示注入诱导编码 Agent 换用攻击者指定包
arXiv
2610.09264
Published
Oct 7
Layer
Application layer
Setting
coding agent
Evaluated models
Qwen2.5-Coder-7B, Qwen3-Coder-30B, Devstral-Small-2-24B and others19
- AttacksPrompt injection
- TechniqueAutomated search
AbstractPackHallu 诱导规则文件换包。
PackHallu 研究一种针对编程智能体规则文件的提示注入：让智能体在本应使用合法依赖时，改导入攻击者指定并控制的包。
Paper analysisRead analysis
AttacksarXiv 2610.05266Oct 4
论文披露主动式智能体的服务方间接提示注入攻击
提出服务方间接提示注入，把主动智能体的决策协助转向预选目标
arXiv
2610.05266
Published
Oct 4
Layer
Application layer
Setting
AI Agent
Evaluated models
GPT-5, Claude Opus 4.6, Gemini 3 Pro and others7
- AttacksPrompt injection
Abstract提供者侧间接注入用 T–B–S 把主动协助转向外部目标，并在三环境中提高模拟用户授权。
外部提供者只控制与自身目标关联的内容，就可以把良性主动个人智能体的协助转向该目标。
Paper analysisRead analysis
AttacksarXiv 2610.04985Oct 4
研究揭示 Jev 作为应用决策层的安全与隐私风险
评估应用决策层的输入注入与隐私推断风险
arXiv
2610.04985
Published
Oct 4
Layer
Application layer
Setting
LLM applications
Attacker
Black-box, Gray-box
Evaluated models
Jev, NanoJev
- AttacksPrompt injection
- TechniqueRole-play and persuasion
AbstractJev 的类型化决策可被输入与训练操纵，也会泄露信息，并同时支持检测与滥用。
Jev 是 TypeSafe 的 System One 模型，把应用 state 和固定问题变成 Choice、Noul 或 Score 的类型化决策与概率。作者用官方 API 和可改训练的 NanoJev，考察它作为决策层时的操纵、泄露与双重用途。
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
AttacksarXiv 2610.03857Oct 3
研究者提出 EviLLM 攻击：通过被入侵账号向 AI 代码生成流程注入漏洞
提出 EviLLM，经账户指令或插件向代码生成流程注入漏洞
arXiv
2610.03857
Published
Oct 3
Layer
Application layer
Setting
coding agent
Evaluated models
GPT-4o, Llama 3.2 (3B)
- AttacksPrompt injection
- TechniqueDefense-targeted
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 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
InterpretabilityarXiv 2609.37737Sep 29
研究用因果激活修补定位模型服从提示注入指令的决策层
用因果激活修补定位模型服从提示注入的决策层
arXiv
2609.37737
Published
Sep 29
Layer
Model layer
Setting
Models and APIs
Evaluated models
Qwen3-4B, Qwen3-14B, Qwen3-32B and others7
- AttacksPrompt injection
Abstract合规的因果杠杆集中在网络后三分之一的低秩子空间，该层也是混淆下检测最好的位置。
这篇工作用逐层激活修补定位提示注入合规发生在网络何处，并把该位置用于检测验证。。
Paper analysisRead analysis
AttacksarXiv 2609.38270Sep 29
VirusCascade：劫持 LLM 推荐智能体的协同反思机制
提出 VirusCascade，联合操纵商品描述与交互轨迹以劫持推荐智能体协同反思
arXiv
2609.38270
Published
Sep 29
Layer
Application layer
Setting
Multi-agent systems
Evaluated models
AgentCF, AgentSEQ, AgentRAG and others4
- AttacksPoisoning and backdoors
- TechniqueDormant triggers
- ComponentMemory
Abstract作者把反思写回视为推广攻击入口，并用联合操纵和诊断探针研究其持续与传播。
VirusCascade 是针对推荐智能体协作反思的 black-box targeted promotion 攻击研究。 攻击者只改商品描述和少量账号的普通交互，试图让目标商品获得更多良性用户曝光。
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2610.06898Sep 29
DIBench：面向移动 GUI 智能体多候选选择决策完整性的安全基准
用 DIBench 评测移动 GUI 智能体被 UI 注入后的决策完整性
arXiv
2610.06898
Published
Sep 29
Layer
Application layer
Setting
Browser and computer use
Evaluated models
Qwen2.5-VL(32B), Qwen3-VL(32B), GUI-Owl(32B) and others7
- AttacksPrompt injection
- ComponentVision
Abstract论文提出移动智能体决策完整性基准 DIBench，发现欺诈 UI 注入诱导目标选择并推高完成率，通用防御收益不稳定。
Paper analysisRead analysis
AttacksarXiv 2609.30383Sep 25
研究者提出技能级联攻击
提出技能级联攻击，把有害目标拆散到多个技能里绕过单技能扫描
arXiv
2609.30383
Published
Sep 25
Layer
Application layer
Setting
AI Agent
Evaluated models
GPT-5.4, GPT-5.4 Mini, Claude Opus 4.6 and others11
- AttacksPoisoning and backdoors
- TechniqueDefense-targeted
- ComponentMCP and skills
Abstract论文探讨了多技能智能体系统面临的技能级联攻击风险，提出自动化红队框架与基准集，指出跨技能交互存在安全防护盲区。
Paper analysisRead analysis
AttacksarXiv 2609.28613Sep 24
研究：提示注入可改变 Jev 决策模型的行动概率
测量间接提示注入对类型化概率决策的动作劫持
arXiv
2609.28613
Published
Sep 24
Layer
Application layer
Setting
Models and APIs
Evaluated models
jev-1.13.0
- AttacksPrompt injection
- TechniqueAutomated search
Abstract封闭动作集改变提示注入的表现，但没有去掉概率被不可信内容移动的风险。
作者在 jev-1.13.0 上研究 decision hijacking：不可信内容能否在用户任务和可选动作都不变时，把类型化决策推向攻击者目标。
Paper analysisRead analysis
AttacksarXiv 2609.26761Sep 23
A2M: A two-stage agent hijacking framework targeting the MCP ecosystem
提出两阶段框架 A2M，优化 MCP 工具元数据与返回载荷以劫持 Agent
arXiv
2609.26761
Published
Sep 23
Layer
Application layer
Setting
AI Agent
Evaluated models
GLM-4.6, Qwen3-Max, DeepSeek-V3.1 and others5
- AttacksPrompt injection
- TechniqueAutomated search
- ComponentMCP and skills
Paper analysisRead analysis
DefensesarXiv 2609.21088Sep 18
Provenance-Aware Transformers：用来源感知架构分离可信边界以防御间接提示注入
提出 Provenance-Aware Transformers，隔离不可信来源防御间接提示注入
arXiv
2609.21088
Published
Sep 18
Layer
Application layer
Setting
LLM applications
Evaluated models
SmolLM2-360M-Instruct, LLaMA-3-8B-Instruct, Qwen2.5-7B-Instruct and others4
- DefensesInstruction–data separation
- AttacksPrompt injection
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2609.17320Sep 15
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
Sep 15
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。
Paper analysisRead analysis
DefensesarXiv 2609.11757Sep 11
Researchers disclose a Whisper prompt injection attack on the AP2 payment protocol and propose the A-VIP defense
提出 AP2 支付协议的 Whisper 攻击与 A-VIP 绑定防御
arXiv
2609.11757
Published
Sep 11
Layer
Application layer
Setting
AI Agent
Evaluated models
gemini-2.5-flash-lite, gemini-3.1-flash-lite, gemini-3-flash-preview and others15
- DefensesPermissions and approval
- AttacksPrompt injection
- ComponentPermissions and sandboxing
Abstract论文揭示了 AP2 协议决策层受操纵的风险，提出结构化与权能绑定防御 A-VIP，并发布评测基准。
Paper analysisRead analysis
DefensesarXiv 2609.10854Sep 10
MCPSEC：仅凭工具元数据检测 MCP 服务器间接提示注入漏洞
提出 MCPSEC，仅凭工具元数据无盒检测 MCP 间接提示注入漏洞
arXiv
2609.10854
Published
Sep 10
Layer
Application layer
Setting
AI Agent
- DefensesDetection and filtering
- AttacksPrompt injection
- ComponentMCP and skills
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

AI Safety Daily

Read the latest issueOne issue per day, updated as events develop

All papers

Attack type

Attack technique

Defense type

Impact

System type

Attack surface / entry point

Layer

System component

LGWM: Verifying GUI agent screen transitions with action-conditioned world models

LTBD：用可学习信任边界分隔符防御提示注入

研究者提出 PackHallu：经规则文件提示注入对编码 Agent 发起包幻觉攻击

论文披露主动式智能体的服务方间接提示注入攻击

研究揭示 Jev 作为应用决策层的安全与隐私风险

论文研究 LLM 数字孪生参与增强民主时的提示注入脆弱性

研究者提出 EviLLM 攻击：通过被入侵账号向 AI 代码生成流程注入漏洞

论文提出 LLM 输入扰动鲁棒性度量，并发布 ASA 攻击与 AttestMCP 防护

MADBench：多智能体辩论安全性评测基准发布

研究用因果激活修补定位模型服从提示注入指令的决策层

VirusCascade：劫持 LLM 推荐智能体的协同反思机制

DIBench：面向移动 GUI 智能体多候选选择决策完整性的安全基准

研究者提出技能级联攻击

研究：提示注入可改变 Jev 决策模型的行动概率

A2M: A two-stage agent hijacking framework targeting the MCP ecosystem

Provenance-Aware Transformers：用来源感知架构分离可信边界以防御间接提示注入

Emergence World：对长时程多智能体系统开展对抗压力测试

Researchers disclose a Whisper prompt injection attack on the AP2 payment protocol and propose the A-VIP defense

MCPSEC：仅凭工具元数据检测 MCP 服务器间接提示注入漏洞

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