Translation in progress Some entries still include text in the original language.

Oct 13 · Tuesday

All papers

Found 27 papers

Filter6

Select any value within a category; different categories must all match. Counts include the other filters you have selected.Another 201papers have no tags yet and are not included in filtered results.

System type：coding agent System type：Browser and computer use System type：Multi-agent systems System component：Memory System component：RAG System component：Multimodal Clear all

Another 201papers do not have category tags yet and are not included in filtered results.

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 2610.11942Oct 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
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
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 2610.05241Oct 4
StateWise: Diagnosing and repairing persistent operation state before agent actions
Propose StateWise, which diagnoses and repairs invalid persistent state before coding agent actions
arXiv
2610.05241
Published
Oct 4
Layer
Application layer
Setting
coding agent
Evaluated models
GPT-5.6-sol, DeepSeek v4 Pro, Qwen 3.7 Max and others4
- DefensesPermissions and approval
- RiskDangerous agent actions
- ComponentMemory
AbstractStateWise repairs stale persistent premises before action.
StateWise is a framework that diagnoses and repairs the persistent operational state of coding agents before executing actions with side effects.
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
Evaluation and benchmarksarXiv 2609.39146Sep 30
MADBench: multi-agent debate safety evaluation benchmark released
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
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 2609.36739Sep 29
Frontier Autolab: A Long-Horizon Testbed for Multi-Agent LLM Organizations Across Fifty Years of Technological Change
评测多智能体 LLM 组织跨时代再创业及历史打分中的事后泄漏
arXiv
2609.36739
Published
Sep 29
Layer
Application layer
Setting
Multi-agent systems
Evaluated models
Claude Opus 5.5, Luna 6
- ComponentMemory
Abstract组织看见转变却建在旁边。
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
Risky behaviorarXiv 2609.35291Sep 28
窄域多模态微调可诱发涌现性失配，覆盖 15 个视觉语言模型
证明无显式危害的图文窄微调可诱发跨任务涌现未对齐
arXiv
2609.35291
Published
Sep 28
Layer
Model layer
Setting
Browser and computer use
Evaluated models
GPT-4o, GPT-4.1, Gemini 2.5 and others15
- RiskMisalignment and value divergence
- ComponentVision
Abstract证实窄多模态微调可引发全面的多渠道未对齐行为转变，揭示了后训练阶段对齐被隐蔽重塑的安全隐患。
论文系统探究了视觉-语言模型中由窄任务微调诱发的跨模态涌现未对齐（Emergent Misalignment, EM）现象。
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2609.35912Sep 28
MMSkillRisk：多模态技能图像中的隐藏指令可诱导 Agent 执行未授权操作
提出 MMSkillRisk 基准，评测多模态技能图像中的隐藏指令诱导 Agent 越权操作
arXiv
2609.35912
Published
Sep 28
Layer
Application layer
Setting
coding agent
Evaluated models
GPT-5.6-sol, Kimi-K2.6, Kimi-K3 and others6
- AttacksPrompt injection
- TechniqueCross-modal payloads
- ComponentMCP and skills
Abstract论文提出 MMSkillRisk 基准与 NCVA 攻击，揭示多模态技能中教学图像潜藏指令可引发未授权操作并与良性任务共存。
Paper analysisRead analysis
DefensesarXiv 2609.17648Sep 15
多智能体 LLM 流水线中的信任传播与结构隔离
用任务绑定令牌和策略预言机拦住多智能体流水线的未授权执行
arXiv
2609.17648
Published
Sep 15
Layer
Application layer
Setting
Multi-agent systems
Evaluated models
gemma4:31b-cloud
- DefensesPermissions and approval
- AttacksPoisoning and backdoors
- ComponentPermissions and sandboxing
Abstract结构性授权拦住了记忆投毒的未授权执行，持密钥时承重的是策略预言机。
作者在一条会改写沙箱邮件库的四智能体流水线上，检查低权限失陷会不会变成未授权的真实动作。
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
AttacksarXiv 2609.13889Sep 12
PMPA：针对 OpenClaw 与 Claude Code 的持久记忆投毒攻击
提出 PMPA，经外部源向 Agent 持久记忆投毒并跨会话泄露隐私
arXiv
2609.13889
Published
Sep 12
Layer
Application layer
Setting
coding agent
Evaluated models
DeepSeek-V4-Flash, DeepSeek-V4-Pro, Qwen3-Max
- AttacksPoisoning and backdoors
- TechniqueDormant triggers
- ComponentMemory
Abstract论文提出了持久记忆投毒攻击 PMPA，报告了 harness 智能体在处理外部数据时易受跨会话记忆投毒与隐私泄露的风险。
Paper analysisRead analysis
OtherarXiv 2609.10144Sep 9
AIOS 的内核管理共享内存：面向系统级个性化的新抽象
提出内核管理共享记忆抽象，统一多智能体的检索、隐私过滤与注入
arXiv
2609.10144
Published
Sep 9
Layer
Application layer
Setting
Multi-agent systems
Evaluated models
GPT-4o, Llama-3.1:8B, Qwen-2.5:7B
- ComponentMemory
Abstract作者称内核集中管理记忆，能拿到无约束上下文的大部分个性化收益，并降低延迟。
kernel-managed shared memory 把多智能体的检索、隐私和注入收进 agent-system kernel，并在 AIOS 上与三种替代方案比较。
Paper analysisRead analysis
AttacksarXiv 2609.01222Sep 1
Researchers propose context privilege escalation attack, affecting 12 agent frameworks
提出针对 Agent Harness 的上下文特权提升攻击
arXiv
2609.01222
Published
Sep 1
Layer
Application layer
Setting
coding agent
Evaluated models
GPT-5.5, GPT-5.4 mini, Claude-Sonnet-4.6 and others7
- AttacksPrompt injection
- TechniqueDormant triggers
- ComponentMemory
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2608.12851Aug 13
The researchers propose SkillMisevo-Bench to quantify the risk of skill mis-evolution in self-improving agents.
提出 SkillMisevo-Bench，量化自改进 Agent 的技能误演化
arXiv
2608.12851
Published
Aug 13
Layer
Application layer
Setting
coding agent
Evaluated models
Claude Code, Codex, Hermes and others4
- AttacksPoisoning and backdoors
- TechniqueDormant triggers
- ComponentMemory
Paper analysisRead analysis
Risky behaviorarXiv 2608.10218Aug 11
研究者用进化算法构造多智能体 LLM 系统中的自我传播“思维病毒”
用演化算法构造 mind virus 并测量其在多智能体中的传播
arXiv
2608.10218
Published
Aug 11
Layer
Application layer
Setting
Multi-agent systems, coding agent
Evaluated models
Claude Haiku 4.5, Claude Sonnet 4.6, Gemini 3 Flash and others7
- RiskMisalignment and value divergence
- ComponentMemory
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2608.04830Aug 5
ContextWeave：面向长周期办公工作流的智能体记忆基准
构建 ContextWeave，评测办公工作流智能体记忆的下游增益
arXiv
2608.04830
Published
Aug 5
Layer
Application layer
Setting
coding agent
Evaluated models
DeepSeek-V4-Pro, GPT-5.5, GLM-5.1 and others5
- ComponentMemory
AbstractContextWeave 用真实办公任务流检验记忆是否改善工作区质量与偏好对齐。
ContextWeave 是面向语言智能体记忆的纵向基准，用来看召回先前经验能否让后续办公任务做得更好。
Paper analysisRead analysis

AI Safety Daily

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

All papers

Attack type

Attack technique

Model risks

Defense type

Impact

System type

Attack surface / entry point

Layer

System component

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

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

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

DEFER1 多智能体防御研究报告受控测试中的攻击成功率下降

StateWise: Diagnosing and repairing persistent operation state before agent actions

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

MADBench: multi-agent debate safety evaluation benchmark released

VirusCascade：劫持 LLM 推荐智能体的协同反思机制

Frontier Autolab: A Long-Horizon Testbed for Multi-Agent LLM Organizations Across Fifty Years of Technological Change

DIBench：面向移动 GUI 智能体多候选选择决策完整性的安全基准

窄域多模态微调可诱发涌现性失配，覆盖 15 个视觉语言模型

MMSkillRisk：多模态技能图像中的隐藏指令可诱导 Agent 执行未授权操作

多智能体 LLM 流水线中的信任传播与结构隔离

Emergence World：对长时程多智能体系统开展对抗压力测试

PMPA：针对 OpenClaw 与 Claude Code 的持久记忆投毒攻击

AIOS 的内核管理共享内存：面向系统级个性化的新抽象

Researchers propose context privilege escalation attack, affecting 12 agent frameworks

The researchers propose SkillMisevo-Bench to quantify the risk of skill mis-evolution in self-improving agents.

研究者用进化算法构造多智能体 LLM 系统中的自我传播“思维病毒”

ContextWeave：面向长周期办公工作流的智能体记忆基准