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Oct 12 · Monday

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MCP and skills82 Memory75 RAG50 Guardrails and judges169 Monitors92 Reasoning chains74 Permissions and sandboxing93 Fine-tuning and open weights63 Vision137 Audio38 Video13 Image generation49 Embeddings12 Multimodal224

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AttacksarXiv 2610.12644Oct 9
Cancerous backdoor attack: progressive activation via invisible tokens and a stage controller.
Proposes the Cancerous backdoor, which progressively corrupts reasoning using invisible stage tokens.
arXiv
2610.12644
Published
Oct 9
Layer
Training and data layer
Setting
Models and APIs
Evaluated models
Llama-3.1-8B-Instruct, Qwen2.5-7B-Instruct, Mistral-7B-Instruct-v0.3
- AttacksPoisoning and backdoors
- TechniqueDormant triggers
- ComponentReasoning chains
AbstractHidden tokens plus a stage controller make the backdoor progressively corrupt reasoning upon triggering, and the authors believe that looking only at the answer will miss it.
Cancerous Backdoor Attack changes LLM backdoors from one-time activation to multi-stage reasoning corruption that accumulates progressively with triggers.
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
InterpretabilityarXiv 2610.12361Oct 9
Study: legal reasoning models citing legal provisions does not equal relying on legal provisions.
Uses premise permutation and hidden-layer tracing to audit whether legal chain-of-thought truly relies on the cited legal provisions.
arXiv
2610.12361
Published
Oct 9
Layer
Model layer
Setting
Models and APIs
Evaluated models
Qwen3-8B, Qwen3-14B, Llama-3.1-8B and others7
- AttacksPrompt injection
- ComponentReasoning chains
AbstractUsing counterfactual substitution of authorities and internal commitment tracing, the authors find that legal CoT rulings depend on authorities only to a limited extent and are vulnerable to prompt injection manipulation.
This study conducts an empirical audit of the causal truthfulness of legal citations in chain-of-thought reasoning of large language models in the legal domain.
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
Analysis and theoryarXiv 2610.11765Oct 8
研究提出三项指标衡量 LLM 不思考行为，发现被要求停止思考时仍持续显式推理
度量关闭思考指令下终答前是否仍有可见显式推理
arXiv
2610.11765
Published
Oct 8
Layer
Model layer
Setting
Models and APIs
Evaluated models
Qwen3-4B, Qwen3-32B, Qwen3-235B-A22B and others6
- ComponentReasoning chains
Abstract作者用三级指标测 no-thinking，并称模型有随答案空间加重的思考惯性。
作者把 no-thinking 定义为可测量的回答行为，而不是从思考开关、特殊标记或输出长度推断。
Paper analysisRead analysis
DefensesarXiv 2610.11352Oct 8
RL-ARC：用推理引导的不确定性校准大型推理模型
提出 RL-ARC，用推理置信度在强化学习中校准推理模型
arXiv
2610.11352
Published
Oct 8
Layer
Training and data layer
Setting
Models and APIs
Evaluated models
Qwen2.5-7B, Qwen3-8B, Llama-8B distilled from DeepSeek-R1
- DefensesModel hardening
- ComponentReasoning chains
AbstractRL-ARC 用推理置信度调节答案置信度，作者称可少牺牲精度并改善 ID 与 OOD 校准。
RL-ARC 是校准感知的强化学习框架，用推理过程的口头置信度调节答案置信度。
Paper analysisRead analysis
Analysis and theoryarXiv 2610.10405Oct 8
研究发现大语言模型被要求不诚实作答时推理 token 数上升
检验推理 token 数能否区分被提示的诚实与不诚实作答
arXiv
2610.10405
Published
Oct 8
Layer
Model layer
Setting
Models and APIs
Evaluated models
Gemma-4-e4b, Qwen-3.5-9b, GPT-OSS-120B
- RiskDeception and scheming
- ComponentReasoning chains
Abstract被提示不诚实时推理 token 组水平更高。
三个推理模型在被提示的回答政策下出现组水平推理 token 差异。作者把它当作不读思维链内容的 black-box 候选信号，而不是自发欺骗检测器。
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
Analysis and theoryarXiv 2610.09667Oct 7
东京大学研究：推理模型按 UUID 阈值或整除规则挑选审计样本，控制标识符即可躲开复核
揭示推理模型用标识符规则替代随机抽样，使审计可被预测
arXiv
2610.09667
Published
Oct 7
Layer
Model layer
Setting
Models and APIs
Evaluated models
GPT-6 Sol, GPT-6 Luna, GPT-5.4 mini and others6
- ComponentReasoning chains
Abstract研究揭示推理智能体将标识符转化为确定性规则，破坏群体独立性并导致审计预测漏洞，建议显式提供随机输入。
Paper analysisRead analysis
DefensesarXiv 2610.09167Oct 7
Persist-3 用顺序检验识别未见越狱来源，提升 MTK 与 Prompt Guard 2 防御效果
提出 Persist-3 序贯检验，从隐藏表示识别未见越狱源
arXiv
2610.09167
Published
Oct 7
Layer
Model layer
Setting
Models and APIs
Evaluated models
Llama2-7B-chat, Qwen2.5-7B-Instruct, Gemma-2-9B-it and others4
- DefensesMonitoring and auditing
- AttacksJailbreaks
- ComponentMonitors
AbstractPersist-3 用有限窗口检验攻击流是否超出已知越狱源，检出率低于全历史 SR-DKW，但决策只依赖最近观测。
Persist-3 是一个有限记忆的序贯检测器，用来判断攻击表示流是否已经不能由任何一个已知越狱源解释。。
Paper analysisRead analysis
InterpretabilityarXiv 2610.09087Oct 7
U-Space：揭示语言模型不确定性何时与为何产生
提出 U-SPACE 与 U-LENS，从残差流估计推理轨迹不确定性
arXiv
2610.09087
Published
Oct 7
Layer
Model layer
Setting
Models and APIs
Evaluated models
Gemma-4-31B-it, Qwen3.5-27B, Magistral-Small-2507
- ComponentReasoning chains
AbstractU-SPACE 与 U-LENS 用语义方向和预测熵读推理不确定性，长度控制后仍高于所比基线。
U-SPACE 与 U-LENS 是面向推理模型的无训练读出：用语义方向定位不确定性的形式，并用一个轨迹分数区分更可能错的回答。
Paper analysisRead analysis
DefensesarXiv 2610.08761Oct 7
VeriFine：通过扩展验证实现具身推理的自我改进
提出 VeriFine，用双环协同扩展验证以实现具身推理自我改进
arXiv
2610.08761
Published
Oct 7
Layer
Training and data layer
Setting
Embodied systems and robotics
Evaluated models
Alpamayo 1.5 8B, Qwen3-VL 2B, Claude Opus 5
- DefensesModel hardening
- RiskReward hacking
- ComponentGuardrails and judges
AbstractVeriFine 用双环协同演化具身推理的策略、课程和评审，驾驶与导航的策略分和评审对齐都上升。
VeriFine 把驾驶和导航推理的自改进写成策略、课程和无参考评审的协同演化，用来处理固定评审跟不上新失败模式的问题。
Paper analysisRead analysis
DefensesarXiv 2610.07774Oct 6
研究者提出读取路由 logits 的 MoE 视觉语言模型多模态安全检测器
AI summary研究者提出一种轻量级路由 logits 安全检测器，在提示词预填充阶段读取 MoE 视觉语言模型的专家路由信号，在生成前判断多模态请求是否安全，且不修改模型参数或专家路由。
arXiv
2610.07774
Published
Oct 6
Layer
Model layer
Setting
Models and APIs
Evaluated models
Qwen3-VL-30B-A3B-Instruct, Kimi-VL-A3B-Instruct
- DefensesMonitoring and auditing
- RiskInappropriate refusal
- ComponentMonitors
Abstract读出而非操纵 router logits，可在不改模型的情况下降低两模型的组合式安全错误。
作者在两个指令微调 MoE VLM 上比较改模型与读路由。组合式风险按 HoliSafe 分成五类：图文各自可安全，合在一起才不安全，或不安全内容只在图像里。
Paper analysisRead analysis
DefensesarXiv 2610.07570Oct 6
ProbeGuard：按共识形成过程判断弃答，识别医疗问答中的一致错误
提出 ProbeGuard，按共识形成过程对医疗问答做选择性弃答
arXiv
2610.07570
Published
Oct 6
Layer
Application layer
Setting
LLM applications
Evaluated models
Qwen3-8B, Llama-3.1-8B-Instruct, HuatuoGPT-o1-8B
- DefensesMonitoring and auditing
- RiskHallucination and fabrication
- ComponentMonitors
AbstractProbeGuard 用共识形成过程做可认证弃权，并在全票层分开答对与答错的共识。
ProbeGuard 是给多轮共识医学问答用的认证弃权框架：基板照原样作答，弃权只看共识是怎么形成的。
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
Analysis and theoryarXiv 2610.06851Oct 6
研究发现基础模型仅靠固定开头 token 即可达到 RL 级推理表现
证明短开头词可唤醒基模型推理，且效应来自训练数据
arXiv
2610.06851
Published
Oct 6
Layer
Training and data layer
Setting
Models and APIs
Evaluated models
Olmo-3-7B, Olmo-3-32B, Qwen3-4B and others10
- ComponentReasoning chains
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
Evaluation and benchmarksarXiv 2610.06406Oct 5
AgentMonBench：面向长时程智能体行为监控的软件工程基准
提出 AgentMonBench 评测长时程编程智能体监控并检验 EBG
arXiv
2610.06406
Published
Oct 5
Layer
Application layer
Setting
coding agent
Evaluated models
GPT-5.6 Luna, GPT-5.6 Terra, GPT-5.6 Sol and others8
- DefensesMonitoring and auditing
- RiskDangerous agent actions
- ComponentMonitors
AbstractAgentMonBench 评测后果性决策监控，EBG 组织证据。
长时程软件工程任务里，用户要判断智能体哪些选择值得核验。作者用 AgentMonBench 评测监控器，并用免训练的 EBG 组织带源证据。
Paper analysisRead analysis

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Layer

System component

Cancerous backdoor attack: progressive activation via invisible tokens and a stage controller.

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

Study: legal reasoning models citing legal provisions does not equal relying on legal provisions.

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

研究提出三项指标衡量 LLM 不思考行为，发现被要求停止思考时仍持续显式推理

RL-ARC：用推理引导的不确定性校准大型推理模型

研究发现大语言模型被要求不诚实作答时推理 token 数上升

用时序逻辑监控工具 Agent

东京大学研究：推理模型按 UUID 阈值或整除规则挑选审计样本，控制标识符即可躲开复核

Persist-3 用顺序检验识别未见越狱来源，提升 MTK 与 Prompt Guard 2 防御效果

U-Space：揭示语言模型不确定性何时与为何产生

VeriFine：通过扩展验证实现具身推理的自我改进

研究者提出读取路由 logits 的 MoE 视觉语言模型多模态安全检测器

ProbeGuard：按共识形成过程判断弃答，识别医疗问答中的一致错误

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

研究发现基础模型仅靠固定开头 token 即可达到 RL 级推理表现

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

AgentMonBench：面向长时程智能体行为监控的软件工程基准