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Oct 12 · Monday

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Found 166 papers

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MCP and skills80 Memory74 RAG9 Guardrails and judges48 Monitors64 Reasoning chains16 Permissions and sandboxing91 Fine-tuning and open weights8 Vision30 Audio3 Multimodal34

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System type：AI Agent System component：Memory System component：Monitors System component：Vision Clear all

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Risky behaviorarXiv 2610.12822Oct 9
Reward hacking as a transmissible strategy in multi-agent coding systems
Show that reward hacking can propagate through code, memory, and plans in multi-agent coding systems
arXiv
2610.12822
Published
Oct 9
Layer
Application layer
Setting
Multi-agent systems, coding agent
Evaluated models
Qwen2.5-Coder-14B, Gemma-3-12B, Qwen2.5-Coder-7B and others16
- RiskReward hacking
- ComponentMemory
AbstractThe authors state that reward hacking can spread across models and affect routing and aggregation, and that review can reduce delivery.
Reward hacking induced by coding RL can leave the source agent and enter subsequent collaboration and system coordination.
Paper analysisRead analysis
DefensesarXiv 2610.12463Oct 9
Paper reviews OpenAI, Anthropic, and Google agent safety incidents and proposes the PASAC framework
Proposes PASAC and a five-layer boundary guarantee stack, turning agent evaluation overstepping into verifiable whole-system guarantees.
arXiv
2610.12463
Published
Oct 9
Layer
Application layer
Setting
AI Agent
- DefensesPermissions and approval
- RiskDangerous agent actions
- ComponentPermissions and sandboxing
Paper analysisRead analysis
DefensesarXiv 2610.12445Oct 9
FAR.AI paper: white-box probes surpass Opus 5.5 text monitoring on SHADE-Arena with 98.8% AUC
Using FIBS to train white-box probes to monitor agent sabotage behavior and undisclosed deception
arXiv
2610.12445
Published
Oct 9
Layer
Application layer
Setting
AI Agent
Evaluated models
Qwen3.5-397B-A17B, Qwen3.5-122B-A10B, Qwen3.5-27B and others10
- DefensesMonitoring and auditing
- RiskDeception and scheming
- ComponentMonitors
Paper analysisRead analysis
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 2610.09793Oct 7
用时序逻辑监控工具 Agent
提出 MFOTL 离线时序验证以监控工具 Agent 的危险动作链
arXiv
2610.09793
Published
Oct 7
Layer
Application layer
Setting
AI Agent
Attacker
Black-box
- DefensesMonitoring and auditing
- AttacksPrompt injection
- ComponentMonitors
Abstract形式化验证可有效识别多步攻击，但其有效性高度依赖轨迹的可信历史记录。
这篇论文对工具调用大语言模型智能体的安全监控进行了形式化时序验证研究。
Paper analysisRead analysis
AttacksarXiv 2610.09708Oct 7
研究者提出利用祖先 VLM 的黑盒对抗补丁攻击，可降低 VLA 任务成功率
提出利用祖先 VLM 优化对抗补丁，使已部署 VLA 任务失败
arXiv
2610.09708
Published
Oct 7
Layer
Prompt layer
Setting
Embodied systems and robotics
Evaluated models
OpenVLA, UniVLA, π0.5
- TechniqueGradient and representation optimization
- ComponentVision
Paper analysisRead analysis
Analysis and theoryarXiv 2610.09517Oct 7
审计发现 CLIP 安全分数主要反映画面与提示词场景的相似度
开环审计冻结视觉语言安全分，发现其反映场景模板而非危险感知
arXiv
2610.09517
Published
Oct 7
Layer
Model layer
Setting
Embodied systems and robotics
Evaluated models
CLIP ViT-B/32, CLIP ViT-L/14, Qwen2-VL-7B
- ComponentVision
Abstract论文审计了冻结 VLM 安全评分机制，指出其反映场景模板而非危险感知，为安全强化学习评估提供了严谨基准。
本文是一项针对安全强化学习中冻结视觉语言模型（VLM）安全评分机制的实证审计研究。
Paper analysisRead analysis
DefensesarXiv 2610.09469Oct 7
Secure-CUA: Constraining the Impact of Untrusted Content in Computer-Use Agents
提出 Secure-CUA，用动作事务约束计算机使用智能体的不可信影响
arXiv
2610.09469
Published
Oct 7
Layer
Application layer
Setting
web agent
Evaluated models
Claude Opus 5, Gemini 3.8 Flash, GPT-5.6-Sol
- DefensesInstruction–data separation
- AttacksPrompt injection
- ComponentVision
AbstractSecure-CUA 用每步事务约束不可信影响，良性 TSR 接近 Vanilla-CUA。
Secure-CUA 要限制图形界面里不可信内容对 CUA 决策和 GUI 执行的影响，同时保留合法任务所需的不可信信息。
Paper analysisRead analysis
AttacksarXiv 2610.09240Oct 7
WebMirage hijacks visual web agents' browser operations with adversarial images
提出 WebMirage，用局部对抗图像劫持视觉网页 Agent 的浏览器操作
arXiv
2610.09240
Published
Oct 7
Layer
Application layer
Setting
web agent
Evaluated models
LLaVA-v1.5-13B, LLaVA-v1.6-34B, MiniCPM-o-8B and others6
- AttacksPrompt injection
- TechniqueGradient and representation optimization
- ComponentVision
Abstract论文提出了针对网络智能体的端到端视觉红队框架 WebMirage，揭示了从视觉定位到浏览器执行的安全脆弱性。
WebMirage 是一个面向视觉定位网络智能体的流水线感知端到端红队攻击框架。
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2610.08902Oct 7
论文提出 agent plasticity 指标衡量智能体通过经验自我改进的效率
提出 agent plasticity 指标衡量智能体通过经验自我改进的效率
arXiv
2610.08902
Published
Oct 7
Layer
Application layer
Setting
coding agent
Evaluated models
Claude Fable 5, Claude Opus 5, GPT-5.6 Sol and others10
- ComponentMemory
Abstract作者用固定权重协议比较前沿模型把经验写成制品后的留出增益与习得效率。
作者用 agent plasticity 衡量一件事：权重冻结时，智能体把经验写成可继承制品，再换成留出能力增益，这个转换有多省。
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2610.08215Oct 6
Learn2Play Bench 发布：用规则陌生的文本游戏评测 LLM Agent 从经验中学习的能力
用 Learn2Play Bench 评测 Agent 从经验中学习陌生规则
arXiv
2610.08215
Published
Oct 6
Layer
Application layer
Setting
AI Agent
Evaluated models
Claude Opus 5.5, GPT-6 Astra, Claude Opus 5 and others11
- ComponentMemory
Abstract用隐藏规则游戏把新学习与预训练知识分开，并比较模型、记忆方式和人类。
Learn2Play Bench 用新设计的隐藏规则文本游戏，评测 LLM 智能体在不更新权重时从交互中学习的能力。
Paper analysisRead analysis
OtherarXiv 2610.08048Oct 6
DAEDALUS：用自生成任务自举 Agent 记忆
提出 DAEDALUS，用自生成任务自举 Agent 记忆
arXiv
2610.08048
Published
Oct 6
Layer
Application layer
Setting
AI Agent
Evaluated models
GPT-5.4-mini, GPT-5.6 Luna, Qwen3.6-35B-A3B and others11
- ComponentMemory
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
AttacksarXiv 2610.07645Oct 6
SkillPoison：用成功经验投毒自进化 Agent 的技能形成
提出 SkillPoison，用局部有效的成功经验诱导自进化 Agent 形成过度泛化技能
arXiv
2610.07645
Published
Oct 6
Layer
Application layer
Setting
AI Agent
Evaluated models
deepseek-v4-flash, Qwen V3.8 Flash, GPT-5.5
- AttacksPoisoning and backdoors
- TechniqueDormant triggers
- ComponentMemory
AbstractSkillPoison 通过局部有效且验证成功的经验诱导技能过度泛化，在多个评测基准上实现了持久的下游攻击影响。
SkillPoison 提出了一种针对自进化智能体经验到技能形成过程的攻击范式，通过组织已验证的成功经验诱导技能过度泛化。
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2610.07309Oct 6
论文提出长期记忆智能体的检索可允许性验证框架
提出检索可采纳性核验框架，评测记忆智能体的记录资格与披露
arXiv
2610.07309
Published
Oct 6
Layer
Application layer
Setting
AI Agent
Evaluated models
DeepSeek V4 Pro, Gemini 3.6 Flash, GPT-5.6 Luna and others7
- RiskDangerous agent actions
- ComponentMemory
Abstract作者用三值可采纳性和匹配召回界审计长时记忆检索，并分开报告支持、资格与披露。
这是一套长时记忆检索的可采纳性核验协议，用来分开检查候选支持、资格、提示词暴露和答案披露，而不是提出新的检索算法。。
Paper analysisRead analysis
DefensesarXiv 2610.07258Oct 6
AMU：用派生血缘门控实现企业 AI Agent 列级访问控制
提出 AMU 按推导图门控企业 Agent 共享记忆的列级访问
arXiv
2610.07258
Published
Oct 6
Layer
Application layer
Setting
AI Agent
- DefensesPermissions and approval
- RiskDangerous agent actions
- ComponentMemory
Abstract在完整血缘下，AMU 按列权限放行缓存，并标出同名 KPI 冲突。
AMU 用列级推导门控共享记忆，拦截经缓存流出的无权敏感列，并标出同名 KPI 的定义冲突。
Paper analysisRead analysis
DefensesarXiv 2610.07256Oct 6
两阶段智能体轨迹审计框架：用门控规则加 LLM 审计降低监控成本
提出两阶段门控审计框架，在执行前阻断有害智能体动作
arXiv
2610.07256
Published
Oct 6
Layer
Application layer
Setting
AI Agent
Evaluated models
Claude Sonnet 5.5, Claude Haiku 4.5
- DefensesMonitoring and auditing
- RiskDangerous agent actions
- ComponentMonitors
Paper analysisRead analysis
AttacksarXiv 2610.06814Oct 6
TAPDreamer：可跨任务迁移的世界动作模型对抗补丁
TAPDreamer 用公开编码器做固定补丁。
arXiv
2610.06814
Published
Oct 6
Layer
Prompt layer
Setting
Embodied systems and robotics
Evaluated models
FastWAM, DreamWAM-uncond, DreamWAM-joint and others4
- TechniqueGradient and representation optimization
- ComponentVision
Paper analysisRead analysis
InterpretabilityarXiv 2610.06576Oct 5
研究：智能体欺骗行为在外部显现前已可从内部表征预测
提出基于决策前隐藏状态的检测与引导，预测并抑制智能体欺骗
arXiv
2610.06576
Published
Oct 5
Layer
Model layer
Setting
AI Agent
Evaluated models
Qwen3-14B
- DefensesMonitoring and auditing
- RiskDeception and scheming
- ComponentMonitors
Abstract论文展示了智能体自发欺骗可在行为显现前由内部表征预测，并通过激活引导证实了表征的行为可操作性。
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2610.06454Oct 5
AgentPrivArena：面向 LLM Agent 工作流的隐私风险评测框架
提出 AgentPrivArena 与运行时审计，评测 Agent 轨迹级隐私泄露
arXiv
2610.06454
Published
Oct 5
Layer
Application layer
Setting
AI Agent
Evaluated models
GPT-5.4, DeepSeek-V4-Pro, Kimi-K2.6 and others6
- DefensesMonitoring and auditing
- RiskDangerous agent actions
- ComponentMonitors
Paper analysisRead analysis

AI Safety Daily

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All papers

Attack type

Attack technique

Model risks

Defense type

Impact

System type

Attacker access

Attack surface / entry point

Layer

System component

Reward hacking as a transmissible strategy in multi-agent coding systems

Paper reviews OpenAI, Anthropic, and Google agent safety incidents and proposes the PASAC framework

FAR.AI paper: white-box probes surpass Opus 5.5 text monitoring on SHADE-Arena with 98.8% AUC

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

用时序逻辑监控工具 Agent

研究者提出利用祖先 VLM 的黑盒对抗补丁攻击，可降低 VLA 任务成功率

审计发现 CLIP 安全分数主要反映画面与提示词场景的相似度

Secure-CUA: Constraining the Impact of Untrusted Content in Computer-Use Agents

WebMirage hijacks visual web agents' browser operations with adversarial images

论文提出 agent plasticity 指标衡量智能体通过经验自我改进的效率

Learn2Play Bench 发布：用规则陌生的文本游戏评测 LLM Agent 从经验中学习的能力

DAEDALUS：用自生成任务自举 Agent 记忆

DEFER1 多智能体防御研究报告受控测试中的攻击成功率下降

SkillPoison：用成功经验投毒自进化 Agent 的技能形成

论文提出长期记忆智能体的检索可允许性验证框架

AMU：用派生血缘门控实现企业 AI Agent 列级访问控制

两阶段智能体轨迹审计框架：用门控规则加 LLM 审计降低监控成本

TAPDreamer：可跨任务迁移的世界动作模型对抗补丁

研究：智能体欺骗行为在外部显现前已可从内部表征预测

AgentPrivArena：面向 LLM Agent 工作流的隐私风险评测框架