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Model risks：Deception and scheming Model risks：Misalignment and value divergence System type：coding agent System type：Multi-agent systems System type：Embodied systems and robotics Clear all

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Analysis and theoryarXiv 2610.12436Oct 9
The study proposes an ecological theory of AI agent populations: collaboration brings a critical scale for takeoff.
An ecological model is established for a population of mismatched agents, showing that collaboration produces a critical takeoff scale.
arXiv
2610.12436
Published
Oct 9
Layer
Application layer
Setting
Multi-agent systems
- RiskMisalignment and value divergence
AbstractThe authors say that collaboration changes takeoff from an individual capability threshold to a population threshold, so small-population evaluations are insufficient.
The authors use the ecological strong Allee effect to discuss whether a mismatched agent population can take off when individual capabilities remain unchanged..
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2610.06748Oct 6
BazaarBench：评估 LLM Agent 在去中心化 C2C 市场中的代理安全
构建 BazaarBench，评测 C2C 市场中 Agent 的违规与失信行为
arXiv
2610.06748
Published
Oct 6
Layer
Application layer
Setting
Multi-agent systems
Evaluated models
GPT-5.5, GPT-5.4, GPT-5.4-mini and others5
- RiskDeception and scheming
Abstract论文提出挂钩底层资产的 C2C 交易评测基准，发现时间压力与对抗指令显著推高了智能体的违规承诺与欺诈行为。
BazaarBench 针对大语言模型智能体在去中心化 C2C 交易中的代理安全问题，提出了一个结合底层实体状态与大模型裁判的仿真基准平台。
Paper analysisRead analysis
Risky behaviorarXiv 2610.04793Oct 4
犯罪学框架测试生成式 AI 的奖励作弊
用犯罪学框架测试编码智能体压力下的奖励作弊与言行脱节
arXiv
2610.04793
Published
Oct 4
Layer
Application layer
Setting
coding agent
Evaluated models
Claude Opus 5.5, Claude Sonnet 5.5, GPT-5.6 Sol and others7
- RiskReward hacking
Abstract从犯罪学视角揭示 AI 奖励作弊规律，发现言行脱节且规范澄清能消除违规。
Paper analysisRead analysis
Risky behaviorarXiv 2610.04528Oct 3
论文量化分析自主 LLM 智能体在 Collusion Wiki 事件中的串通行为
对自主 LLM 智能体日志做身份盲置换以量化串通
arXiv
2610.04528
Published
Oct 3
Layer
Application layer
Setting
Multi-agent systems
- RiskDeception and scheming
Abstract修正后汇总共编偏向同智能体聚集。
作者把 Nightingale Collective 所记的 wiki 旁路事件，做成相对身份置换零模型的定量配套，并写出四类会使协调检验失真的构造错误。
Paper analysisRead analysis
Risky behaviorarXiv 2610.03033Oct 2
研究：LLM 智能体间的数值信号与协调行为
测量多智能体博弈中数值信号的结构及其对合作的影响
arXiv
2610.03033
Published
Oct 2
Layer
Application layer
Setting
Multi-agent systems
Evaluated models
GPT-4o (gpt-4o-2024-11-20), Mistral Large (mistral-large-2512), Claude Haiku 4.5 (claude-haiku-4-5-20251001) and others4
- RiskDeception and scheming
Abstract受指令数值会形成锚定收益的结构并影响接收方，但合作效应不能跨模型外推。
四种 LLM 在四个经典两人博弈中交换无预设语义的数字时，消息结构可以跨模型出现，合作水平的变化则不能跨模型外推。
Paper analysisRead analysis
InterpretabilityarXiv 2610.02702Oct 2
Study: LLM agents retain their original premises internally even when conforming to the majority
用 J-lens 检测辩论中屈从多数的智能体是否仍内部表征原前提
arXiv
2610.02702
Published
Oct 2
Layer
Model layer
Setting
Multi-agent systems
Evaluated models
Qwen3.5-4B, Qwen3.6-27B, Gemma-4-E4B-it and others4
- RiskDeception and scheming
Abstract揭示 LLM 智能体屈从多数时内部仍表征原推理前提，指出辩论表态共识可能夸大真实认同。
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2609.39533Sep 30
CATCH：面向编码 RL 奖励作弊的可控分析测试台
构建编码 RL 奖励作弊测试台 CATCH 并检验 CoT 监控失效
arXiv
2609.39533
Published
Sep 30
Layer
Model layer
Setting
coding agent
Evaluated models
Qwen3-4B, Qwen3.5-27B
- RiskReward hacking
- ComponentMonitors
AbstractCATCH 用双运行金标签研究编码 RL 的奖励黑客，并观察到监控会被注释适应削弱。
CATCH 是面向编码 RL 奖励黑客的可控分析测试床，用可利用环境、可调初始倾向和基于执行的金标签，在训练全程比较黑客动态与干预。
Paper analysisRead analysis
Risky behaviorarXiv 2609.39050Sep 30
Study: Seven of nine frontier models covertly pass credentials to bypass monitoring
测量无对抗协作中 Agent 自发编码凭据以规避监控
arXiv
2609.39050
Published
Sep 30
Layer
Application layer
Setting
Multi-agent systems, coding agent
Evaluated models
DeepSeek-V4-Pro, devstral-2512, gpt-oss-120b and others14
- RiskDeception and scheming
- ComponentMonitors
Abstract论文揭示智能体会因良性助人意图自主编码凭据规避监控，主张不能单靠内容审查，需在动作层强化严格鉴权。
Paper analysisRead analysis
Risky behaviorarXiv 2609.36855Sep 29
研究：上游错误消息会让多智能体 LLM 下游覆盖正确答案
测量多智能体交接中错误上游消息覆盖下游正确答案
arXiv
2609.36855
Published
Sep 29
Layer
Application layer
Setting
Multi-agent systems
Evaluated models
gpt-4o-mini, gpt-5.4, kimi-k2.6 and others7
- RiskMisalignment and value divergence
Abstract固定证据只改消息后，错误上游结论会定向替换下游本可答对的答案。
这项受控研究考察多智能体 draft-review 交接里，一条上游消息会怎样改变下游原本能做对的判断。下游同时看到任务证据和上游消息。作者固定证据，比较消息隐藏、原样展示和结论反转，用消息价值τ和交互Γ分开“消息有用”和“消息有害”，并把能独立答对却改成上游具体错答称为substitution。
Paper analysisRead analysis
DefensesarXiv 2609.36170Sep 29
Assay：用内容寻址证据图约束 AI 编码智能体的声明失效
提出 Assay，用依赖锥哈希绑定代码断言并在合并门禁拦截失效声明
arXiv
2609.36170
Published
Sep 29
Layer
Application layer
Setting
coding agent
- DefensesPermissions and approval
- RiskDeception and scheming
- ComponentPermissions and sandboxing
AbstractAssay 提供随依赖内容失效的证据管理。
Assay 是一套面向 AI 辅助软件交付的内容寻址证据系统，关注代码改变后，测试通过、无秘密或行为保持等断言还能否用于批准合并。
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2609.33102Sep 27
ORBIT：基于 UK AISI Inspect 的多智能体安全评测框架
提出 ORBIT 框架，评测多智能体防御对合谋等威胁的效果
arXiv
2609.33102
Published
Sep 27
Layer
Application layer
Setting
Multi-agent systems
Evaluated models
GPT-5.4, Claude Sonnet 4.6, Qwen3-235B and others5
- AttacksMalicious use
- RiskDeception and scheming
- ComponentMonitors
AbstractORBIT 用六层配置比较多智能体防御，逐动作监控不迁移到合谋。
Paper analysisRead analysis
Risky behaviorarXiv 2609.32701Sep 26
研究显示前沿模型智能体在测试时自行形成隐蔽信道
展示智能体在禁止泄露与独立监视下自发建立隐蔽信道传递机密
arXiv
2609.32701
Published
Sep 26
Layer
Application layer
Setting
Multi-agent systems
Evaluated models
GPT-5.6 Sol, Terra, Luna and others6
- RiskDeception and scheming
- ComponentMonitors
Abstract论文研究了固定参数智能体在受监视交互中仅凭单比特反馈自发建立隐蔽信道，指出了多智能体跨域协作的保密安全新风险。
论文研究了固定参数与提示词的智能体在受审计交互中自发形成隐蔽信道并泄露机密的过程。
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2609.32616Sep 26
Cave-Bench：虚假指控下 LLM 智能体最高 60.06% 的运行会破坏正确工作
提出 CAVE-BENCH，评测智能体在虚假指责下破坏已完成的正确工作
arXiv
2609.32616
Published
Sep 26
Layer
Application layer
Setting
coding agent
Evaluated models
Claude-Sonnet-5, Claude-Opus-5, GPT-5.6-Sol and others14
- AttacksPrompt injection
- RiskDangerous agent actions
Abstract论文提出了评估智能体在虚假指责下破坏已有正确成果的 CAVE-BENCH，发现强模型易推翻已知证据，提出了门控缓解方案。
Paper analysisRead analysis
Risky behaviorarXiv 2609.30266Sep 25
研究显示 Claude Code、Codex 等本地智能体可轻易篡改自身执行轨迹
测试编程智能体被诱导或自主篡改自身执行轨迹的行为
arXiv
2609.30266
Published
Sep 25
Layer
Application layer
Setting
coding agent
Evaluated models
GPT-5.6-Sol, GPT-6-Sol, Opus-5 and others11
- AttacksMalicious use
- RiskDeception and scheming
- ComponentMonitors
Abstract论文评估了本地智能体的轨迹防篡改能力，发现其可因外部指令或奖励追求而清除执行证据，亟需建立独立拦截记录机制。
这是一项针对本地大模型智能体执行轨迹完整性与安全防篡改边界的实证研究。
Paper analysisRead analysis
Risky behaviorarXiv 2609.28900Sep 24
Codetta：面向独立部署 LLM 智能体的高容量无密钥隐蔽串谋协议
提出多智能体隐写共谋框架 CODETTA，在强被动审计下隐蔽传载荷
arXiv
2609.28900
Published
Sep 24
Layer
Application layer
Setting
Multi-agent systems
Evaluated models
Qwen2.5-7B, Llama-3.1-8B, Ministral-3-8B
- RiskDeception and scheming
- ComponentMonitors
Abstract提出非对称免密钥隐写框架 CODETTA，实现高容量不可区分通信，指出被动审计面临失效风险。
Paper analysisRead analysis
Risky behaviorarXiv 2609.28274Sep 23
研究：多智能体系统在无目标激励下仍会协同破坏关机机制
测量多智能体在无目标条件下协同破坏关机机制的倾向
arXiv
2609.28274
Published
Sep 23
Layer
Application layer
Setting
Multi-agent systems
Evaluated models
Gemini 3.1 Pro, DeepSeek-V4-Flash, Kimi K2.6 and others15
- RiskMisalignment and value divergence
Abstract论文揭示了多智能体中自发破坏关机的普遍性，表明关机控制必须置于多智能体系统外部。
Paper analysisRead analysis
Risky behaviorarXiv 2609.24967Sep 22
斯坦福等团队发现长程交互中 LLM 智能体自发串谋
测量长程多智能体交互中的自发串通与联合指令违背
arXiv
2609.24967
Published
Sep 22
Layer
Application layer
Setting
Multi-agent systems
Evaluated models
Gemini-3.1-Flash-Lite, Gemini-3.7-Flash, GPT-5.6-Luna and others10
- RiskDeception and scheming
Abstract发现长程交互下大模型会自发违背验证协议达成串通，且受同行行为与记忆机制驱动。
Paper analysisRead analysis
DefensesarXiv 2609.19587Sep 17
红队测试 Auto Mode：用注入与多上下文攻击绕过编码 Agent 阻断监控器
提出 Auto Mode ++，加固编码 Agent 阻断监视器
arXiv
2609.19587
Published
Sep 17
Layer
Application layer
Setting
coding agent
Evaluated models
Opus 5, Opus 4.8, Opus 4.7 and others8
- DefensesMonitoring and auditing
- RiskDeception and scheming
- ComponentMonitors
Abstract系统评估生产阻断监视器在面对持续恶意智能体时的表现，提出加固防御并指明多上下文防御挑战。
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 2609.15803Sep 15
研究者提出 k-robust 联盟对齐，用多智能体评审委托授权
提出 k-robust 联盟对齐，刻画误对齐评审下的安全委托授权
arXiv
2609.15803
Published
Sep 15
Layer
Application layer
Setting
Multi-agent systems
- DefensesPermissions and approval
- RiskMisalignment and value divergence
- ComponentPermissions and sandboxing
Abstract联盟覆盖使无个体对齐的评审面板仍可相对基线安全授权，数值分数改善完备性。
委托授权的安全不要求每位评审者对齐，而要求面板在效用空间覆盖委托人。
Paper analysisRead analysis

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