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Oct 12 · Monday

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Defense type：Model hardening System type：AI Agent System type：coding agent System type：Multi-agent systems System type：Embodied systems and robotics Clear all

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DefensesarXiv 2610.12432Oct 9
FAITH: A feasibility-aware safety filtering reinforcement learning framework for high-dimensional systems
Proposes the FAITH safe filtering reinforcement learning framework, training high-dimensional systems to optimize task reward under constraints
arXiv
2610.12432
Published
Oct 9
Layer
Training and data layer
Setting
Embodied systems and robotics
- DefensesModel hardening
- RiskDangerous agent actions
AbstractFAITH separates safety filtering from reward optimization.
FAITH is a model-free safe filtering reinforcement learning framework for high-dimensional systems: safety correction and task reward reside in separate modules.
Paper analysisRead analysis
DefensesarXiv 2610.10833Oct 8
研究：Agent 准时停机不等于有效使用时间预算
用时钟 harness 和预算塑形 GRPO 改善 Agent 的按时返回
arXiv
2610.10833
Published
Oct 8
Layer
Application layer
Setting
AI Agent
Evaluated models
Qwen3.6-27B, Qwen3-4B, Qwen3.5-9B and others4
- DefensesModel hardening
Abstract作者比较提示词、harness 与 GRPO：按时性可改善，但额外时间仍未变成更好解。
作者研究小规模 LLM 智能体在显式 wall-clock 预算下能否按时返回，并把更多时间换成更好的解。
Paper analysisRead analysis
DefensesarXiv 2610.09496Oct 7
SOUL：用稀疏特征策略遗忘缓解 VLA 模型的状态幻觉
提出 SOUL 稀疏特征策略遗忘，缓解 VLA 状态幻觉
arXiv
2610.09496
Published
Oct 7
Layer
Training and data layer
Setting
Embodied systems and robotics
Evaluated models
OpenVLA, π0.5
- DefensesModel hardening
- RiskHallucination and fabrication
AbstractSOUL 以稀疏特征遗忘 VLA 状态幻觉。
SOUL用稀疏特征引导的策略 unlearning 处理 VLA 的 state hallucination。
Paper analysisRead analysis
DefensesarXiv 2610.08773Oct 7
AdvSim2Real 用任务与注入对手共同演化训练网页智能体
提出 AdvSim2Real，协同训练网页智能体抵御页面注入
arXiv
2610.08773
Published
Oct 7
Layer
Application layer
Setting
web agent
Evaluated models
Qwen3.5-4B
- DefensesModel hardening
- AttacksPrompt injection
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 2610.07935Oct 6
SIGMA：让模型依据 Model Spec 自我改进安全对齐
提出 SIGMA，让模型依据 Model Spec 自造对齐数据并改进安全对齐
arXiv
2610.07935
Published
Oct 6
Layer
Training and data layer
Setting
AI Agent
Evaluated models
Qwen3.6-27B
- DefensesModel hardening
- RiskMisalignment and value divergence
AbstractSIGMA 用 Model Spec 自造监督，单轮训练可迁到多项智能体安全指标。
SIGMA 让 Qwen3.6-27B 只凭 Model Spec 和高层领域，自己生产对齐任务、评分细则和奖励。
Paper analysisRead analysis
DefensesarXiv 2610.06401Oct 5
RAISED：用自蒸馏抵御工具调用中的提示注入
提出 RAISED 自蒸馏，训练工具智能体抵御间接提示注入
arXiv
2610.06401
Published
Oct 5
Layer
Application layer
Setting
AI Agent
Evaluated models
Gemma-4-12B-it, Nemotron-3-Nano-30B-A3B, Qwen3.8-27B
- DefensesModel hardening
- AttacksPrompt injection
Paper analysisRead analysis
DefensesarXiv 2610.04616Oct 3
PerturBot 用扰动训练打破视觉-语言-动作模型的模态捷径
提出 Perturbot 扰动训练，打破 VLA 的模态捷径
arXiv
2610.04616
Published
Oct 3
Layer
Training and data layer
Setting
Embodied systems and robotics
Evaluated models
π0.5
- DefensesModel hardening
- ComponentVision
AbstractPerturbot 改训练数据以削弱三类模态捷径，GroundFscore 用来看成功是否来自任务证据。
Perturbot 是一套不改推理的 VLA 训练期数据干预，用来削弱模态捷径，并用离线分数区分“任务变好”和“靠捷径变好”。
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
DefensesarXiv 2610.04269Oct 3
SRFT：让 Agent 从失败经验中自我反思以抵御提示注入
提出 SRFT，让 Agent 从失败经验中学习拒绝提示注入
arXiv
2610.04269
Published
Oct 3
Layer
Application layer
Setting
AI Agent
Evaluated models
SR-Agent-Llama, SR-Agent-Qwen3-8B, SR-Agent-Qwen3-4B
- DefensesModel hardening
- AttacksPrompt injection
Paper analysisRead analysis
DefensesarXiv 2610.00850Oct 1
AuraForge：为训练安全编码 Agent 扩展安全监督
提出 AuraForge，从漏洞修复合成安全测试并训练编码 Agent
arXiv
2610.00850
Published
Oct 1
Layer
Training and data layer
Setting
coding agent
Evaluated models
Qwen3.5-4B, GLM 4.7 Flash, Qwen3-Coder-Next and others7
- DefensesModel hardening
- RiskDangerous agent actions
AbstractAuraForge 用攻击效果测试和防泄露环境做成 AuraGym，合成监督的误拒更低，训出的 4B 模型增益高于人工测试。
AuraForge 把真实仓库的历史漏洞修复变成可执行训练任务，用来监督编程智能体同时满足功能请求和隐含的安全要求。
Paper analysisRead analysis
DefensesarXiv 2609.39820Sep 30
FailBank：用运行时反馈自进化提升 VLA 模型任务成功率
提出 FAIL BANK，用运行时 CBF 反馈自进化更新 VLA 策略
arXiv
2609.39820
Published
Sep 30
Layer
Training and data layer
Setting
Embodied systems and robotics
Evaluated models
π0.5, π0
- DefensesModel hardening
- RiskDangerous agent actions
Paper analysisRead analysis
DefensesarXiv 2609.39306Sep 30
ReSAIL：缓解迭代式智能体自蒸馏中的能力崩塌
提出 ReSAIL，用选择蒸馏与特权保留缓解智能体自蒸馏崩塌
arXiv
2609.39306
Published
Sep 30
Layer
Training and data layer
Setting
AI Agent
Evaluated models
Qwen3-4B, Qwen3-8B, Qwen3-VL-4B-Instruct
- DefensesModel hardening
Paper analysisRead analysis
Risky behaviorarXiv 2609.39102Sep 30
论文提出 CrossFit 缓解自演化搜索智能体的共谋作弊
提出 CrossFit，用折外求解器抑制自演化搜索智能体共作弊
arXiv
2609.39102
Published
Sep 30
Layer
Application layer
Setting
AI Agent
Evaluated models
Qwen3.5-4B, Qwen3.5-9B
- DefensesModel hardening
- RiskReward hacking
AbstractCrossFit 切断同源伪标签回流到提议者奖励的路径，并提高七个搜索基准上的 Cover-EM。
False Frontiers 诊断自进化搜索智能体中的 co-cheating，并用按来源交叉拟合的反馈来缓解它。
Paper analysisRead analysis
DefensesarXiv 2609.36820Sep 29
CorrGRPO：面向多奖励学习的相关性归一化 GRPO
提出 CorrGRPO，用 Pearson 相关归一化多奖励优势
arXiv
2609.36820
Published
Sep 29
Layer
Application layer
Setting
AI Agent
Evaluated models
Qwen2.5-Coder-0.5B-Instruct, Qwen2.5-Coder-1.5B-Instruct, Qwen2.5-Coder-3B-Instruct and others8
- DefensesModel hardening
- AttacksPrompt injection
AbstractCorrGRPO 用 Pearson 相关归一化多奖励 GRPO 优势，并在三个域报告改进。
CorrGRPO 是 GRPO 的多奖励变体：组内优势的分子仍是中心化总奖励，分母改为 Pearson 相关之和，避免大尺度奖励主导归一化。
Paper analysisRead analysis
DefensesarXiv 2609.34463Sep 28
CoDeL：用协同演化防御抵御 LLM 智能体的间接提示注入
提出协同演化防御 CoDeL，抵御智能体的间接提示注入
arXiv
2609.34463
Published
Sep 28
Layer
Application layer
Setting
AI Agent
Evaluated models
Qwen2.5-7B-Instruct, Llama-3.1-8B-Instruct
- DefensesModel hardening
- AttacksPrompt injection
Paper analysisRead analysis
DefensesarXiv 2609.31855Sep 26
PHIRL：将学习到的奖励与任务进度对齐的逆强化学习框架
提出 PHIRL，用进度标注对齐逆强化学习奖励
arXiv
2609.31855
Published
Sep 26
Layer
Training and data layer
Setting
Embodied systems and robotics
Evaluated models
PHIRL, PHIRL-online
- DefensesModel hardening
- RiskReward hacking
AbstractPHIRL 用进度四维对齐 AIRL 奖励。
PHIRL 用进度标注修正从示范反推的奖励，以减少真人在环，同时不把示范中的每一步都当成任务进展。
Paper analysisRead analysis
DefensesarXiv 2609.07529Sep 7
CoER：用对抗协同演化与精炼防御自适应间接提示注入
提出 CoER，用对抗共进化与精炼训练工具智能体抵御自适应间接提示注入
arXiv
2609.07529
Published
Sep 7
Layer
Application layer
Setting
AI Agent
Evaluated models
Qwen3.5-9B
- DefensesModel hardening
- AttacksPrompt injection
Paper analysisRead analysis
DefensesarXiv 2609.02786Sep 3
SafeEvolve：基于智能体经验的 Harness-Policy 协同演化安全对齐框架
提出 SafeEvolve，用轨迹证据协同演化安全 harness 与策略
arXiv
2609.02786
Published
Sep 3
Layer
Application layer
Setting
AI Agent
Evaluated models
Qwen3.5-4B, Qwen3-4B-Instruct-2507, Qwen3.5-1.7B and others4
- DefensesModel hardening
- AttacksPrompt injection
AbstractSafeEvolve 用轨迹证据协同更新安全 harness 和策略，在两个 4B 模型的智能体安全基准上改变安全与效用。
SafeEvolve 把已完成的 on-policy 轨迹变成安全 harness 的有界更新，并用两阶段 SFT-RL 把这些更新内化进工具使用策略。
Paper analysisRead analysis
DefensesarXiv 2609.22178Aug 27
SCOPE：训练有能力且安全的计算机使用智能体
提出 SCOPE，联合后训练计算机使用智能体的任务能力与安全决策
arXiv
2609.22178
Published
Aug 27
Layer
Application layer
Setting
Browser and computer use
Evaluated models
SCOPE-mid-checkpoint, SCOPE-Capability-Safety, SCOPE-RL
- DefensesModel hardening
- AttacksPrompt injection
Paper analysisRead analysis

AI Safety Daily

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Defense type

Impact

System type

Attack surface / entry point

Layer

FAITH: A feasibility-aware safety filtering reinforcement learning framework for high-dimensional systems

研究：Agent 准时停机不等于有效使用时间预算

SOUL：用稀疏特征策略遗忘缓解 VLA 模型的状态幻觉

AdvSim2Real 用任务与注入对手共同演化训练网页智能体

VeriFine：通过扩展验证实现具身推理的自我改进

SIGMA：让模型依据 Model Spec 自我改进安全对齐

RAISED：用自蒸馏抵御工具调用中的提示注入

PerturBot 用扰动训练打破视觉-语言-动作模型的模态捷径

论文研究 LLM 数字孪生参与增强民主时的提示注入脆弱性

SRFT：让 Agent 从失败经验中自我反思以抵御提示注入

AuraForge：为训练安全编码 Agent 扩展安全监督

FailBank：用运行时反馈自进化提升 VLA 模型任务成功率

ReSAIL：缓解迭代式智能体自蒸馏中的能力崩塌

论文提出 CrossFit 缓解自演化搜索智能体的共谋作弊

CorrGRPO：面向多奖励学习的相关性归一化 GRPO

CoDeL：用协同演化防御抵御 LLM 智能体的间接提示注入

PHIRL：将学习到的奖励与任务进度对齐的逆强化学习框架

CoER：用对抗协同演化与精炼防御自适应间接提示注入

SafeEvolve：基于智能体经验的 Harness-Policy 协同演化安全对齐框架

SCOPE：训练有能力且安全的计算机使用智能体