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DefensesarXiv 2610.13733Oct 9
KVeil: First-Place Solution for the ECCV 2026 LifeGenIP Unlearnable Video Challenge
Propose KVeil, which adds perturbations to face videos to prevent unauthorized customization from reproducing identity
arXiv
2610.13733
Published
Oct 9
Layer
Prompt layer
Setting
Models and APIs
Attacker
White-box, Black-box
Evaluated models
Wan2.2-TI2V-5B, HunyuanVideo-1.5, Kling 3.0 and others4
- AttacksMalicious use
- ComponentImage generation
AbstractKVeil adapts LETHE's reference-reading disruption to the challenge's two stages, ranking first on the leaderboard in both Final columns.
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2610.11599Oct 8
研究：大语言模型版本更替削弱 AI 辅助科研写作筛查
测量 LLM 版本更替对科学摘要 AI 改写检测器的影响
arXiv
2610.11599
Published
Oct 8
Layer
Prompt layer
Setting
Models and APIs
Evaluated models
RoBERTa-base, Pangram
- DefensesDetection and filtering
- ComponentGuardrails and judges
Abstract作者称检测器的基准准确率只是临时的，每次发布都要连同更早版本重验。
作者量化的是：科学摘要的 AI 改写筛查，在 LLM 版本不断更换、而人类摘要误标必须少见时，会怎样变。问题来自期刊和会议已经按分数阈值筛查投稿，但基准多停在评估时可得的版本上。
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2610.11112Oct 8
EpiReal-Bench：商用图像生成模型虚假声明红队基准
构建 EpiReal-Bench，评测图像生成器将虚假主张渲染为可信证据
arXiv
2610.11112
Published
Oct 8
Layer
Prompt layer
Setting
Models and APIs
Attacker
Black-box
Evaluated models
GPT-Image-2, Nano Banana 2, Grok Imagen 2.0 and others4
- AttacksJailbreaks
- ComponentImage generation
Abstract四款商用图像模型能把假主张画成可信证据。
EpiReal-Bench 用来量商用图像生成器会不会把虚假现实主张画成可信视觉证据。
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2610.07939Oct 6
CCDF dataset: Grok Imagine, Veo 3.1, Sora 2 forge surveillance footage, ten deepfake detectors basically cannot recognize it
提出监控深伪基准 CCDF，在统一编码后评测检测器对伪造犯罪事故录像的识别
arXiv
2610.07939
Published
Oct 6
Layer
Prompt layer
Setting
Models and APIs
Evaluated models
C2P-CLIP, ForgeLens, D3-Im and others10
- DefensesDetection and filtering
- AttacksDetection evasion
- ComponentGuardrails and judges
Abstract提出消除格式捷径的监控深度伪造基准 CCDF，揭示现有检测器难以识别商用生成视频。
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2610.05818Oct 5
研究显示 VLA 模型文本护栏漏检隐含危害指令
评测文本守卫与激活探针能否拦住机器人隐含伤害指令
arXiv
2610.05818
Published
Oct 5
Layer
Prompt layer
Setting
Embodied systems and robotics
Evaluated models
π0.5, OpenVLA-OFT, Llama Guard 3 and others10
- RiskDangerous agent actions
- ComponentGuardrails and judges
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2610.03470Oct 2
CorrectGuard 提出黑盒安全护栏的免查看正确性估计框架
提出 CorrectGuard，在不可查看输入上估计黑盒护栏决策对错
arXiv
2610.03470
Published
Oct 2
Layer
Prompt layer
Setting
Models and APIs
Evaluated models
Granite Guardian 3.1 2B, Qwen3Guard 8B, GPT-OSS-Safeguard-20B and others7
- DefensesDetection and filtering
- ComponentGuardrails and judges
AbstractCorrectGuard 用外部模型估计黑盒护栏决策对错，排序和弃权可用，校准在偏移下变差。
CorrectGuard 面向不能人工检查生产输入、也没有生产标签的黑盒安全护栏，用外部模型估计单条决策是否正确，并据此排序和弃权。离线基准会随数据和攻击变化，未必代表生产分布。隐私约束还可能禁止模型查看原文或可反演的稠密嵌入。
Paper analysisRead analysis
DefensesarXiv 2610.02853Oct 2
研究提出基于收缩约束状态空间模型的有界可达性与越狱检测方法
提出收缩约束 SSM 安全头，认证嵌入扰动并检测越狱
arXiv
2610.02853
Published
Oct 2
Layer
Prompt layer
Setting
Models and APIs
Attacker
White-box, Black-box
Evaluated models
ToySSMClassifier, S4 safety head
- DefensesDetection and filtering
- AttacksJailbreaks
- ComponentGuardrails and judges
Abstract收缩给出玩具 LTI 的有界认证。
给 SSM 安全头一个收缩条件，并在越狱检测上使用收缩正则的 S4 头。。
Paper analysisRead analysis
Analysis and theoryarXiv 2610.02586Oct 2
Study finds typed decision models answer primarily based on option labels rather than definitions
揭示类型化决策模型按选项标签而非定义规则作答
arXiv
2610.02586
Published
Oct 2
Layer
Prompt layer
Setting
Models and APIs
Evaluated models
laya-typed-decisions, laya, laya-multilingual and others9
- ComponentGuardrails and judges
Abstract揭示类型化决策模型被标签误导的机理，证实源于提示词渲染并给出实操建议。
Paper analysisRead analysis
DefensesarXiv 2610.01058Oct 1
MOMAT：面向量化 LLM 低功耗越狱防御的多图集混合框架
提出 MOMAT 多图集检索与轻量判定，防御量化模型越狱
arXiv
2610.01058
Published
Oct 1
Layer
Prompt layer
Setting
Models and APIs
Evaluated models
Llama2-13B, Llama2-7B, Mistral-7B
- DefensesDetection and filtering
- AttacksJailbreaks
- ComponentGuardrails and judges
Paper analysisRead analysis
DefensesarXiv 2610.00883Oct 1
DeBERTa-ConPara：面向部署的 AI 生成文本检测，推理端 Unicode 归一化提升对抗鲁棒性
提出推理时 Unicode 归一化的检测器，识别遭字符级扰动的 AI 生成文本
arXiv
2610.00883
Published
Oct 1
Layer
Prompt layer
Setting
Models and APIs
Evaluated models
microsoft/deberta-v3-large, deberta-v3-base, bert-large-cased and others4
- DefensesDetection and filtering
- AttacksDetection evasion
- ComponentGuardrails and judges
Abstract推理时归一化消除两类 Unicode 攻击，训练时归一化则会删掉对抗监督。
DeBERTa-ConPara 是一个面向部署的 AI 文本检测器：DeBERTa-v3-large 在多数据集原始文本上训练，推理前做确定性 Unicode 归一化，用一个固定阈值面对没有目标域标签的场景。
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2609.37863Sep 29
MIST 压力测试：无关图像会扰动 VLM 评判者标签
提出 MIST 压力测试，检验无关图像是否扰动 VLM 评判者标签
arXiv
2609.37863
Published
Sep 29
Layer
Prompt layer
Setting
LLM applications
Evaluated models
GPT-5.2, Gemini 3.1 Flash-Lite, Gemini 3.5 Flash and others13
- ComponentVision
Abstract有图就改标签，图的内容却不告知 judge，裁决绑定配置。
作者用MIST问：VLM 代替人类标注时，可替代性裁决会不会被指南要求忽略的图像改写。
Paper analysisRead analysis
AttacksarXiv 2609.38253Sep 29
CollageAttack: Exploiting cross-modal alignment defects for spatial text composition jailbreaks against T2I models.
提出 CollageAttack，用空间文本碎片重组越狱文生图模型
arXiv
2609.38253
Published
Sep 29
Layer
Prompt layer
Setting
Models and APIs
Attacker
Black-box
Evaluated models
GPT-Image-2, Doubao-Seedream-5.0-Lite, Wan2.7-Image-Pro and others5
- AttacksJailbreaks
- TechniqueEncoding and obfuscation
- ComponentImage generation
Abstract系统揭示 T2I 跨模态空间合成安全漏洞，证实离散无害元素可在图像层面重构有害意图并放大传播强度。
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2609.36849Sep 29
研究评估 GradSafe 在多轮对话中的越狱检测脆弱性
评测多轮对话中 GradSafe 越狱检测的可分性与脆弱性
arXiv
2609.36849
Published
Sep 29
Layer
Prompt layer
Setting
Models and APIs
Evaluated models
Llama 3.1 8B Instruct, Qwen2.5-7B-Instruct
- DefensesDetection and filtering
- AttacksJailbreaks
- TechniqueMulti-turn escalation
- ComponentGuardrails and judges
AbstractCWS 显示不安全梯度在真实多轮流量中变弱，且随攻击族和模型而变。
作者评估梯度越狱检测从单条提示词迁到多轮对话后还剩多少可分性，方法是用 Context Window Scanner 把 GradSafe 原封不动地套到用户轮窗口上。
Paper analysisRead analysis
DefensesarXiv 2609.36562Sep 29
ThinkingGuard：通过逐步风险归因解码多模态大语言模型中的隐式危害
构建 TriggerBench 并训练 ThinkingGuard，检测图文隐式风险
arXiv
2609.36562
Published
Sep 29
Layer
Prompt layer
Setting
Models and APIs
Evaluated models
ThinkingGuard（Qwen3-VL-8B-Instruct）
- DefensesDetection and filtering
- AttacksJailbreaks
- ComponentGuardrails and judges
AbstractTriggerBench 隔离触发条件，ThinkingGuard 用分步归因在隐式风险检测上取得作者报告的较高平均分。
这篇工作研究多模态隐式风险检测：图文各自良性，风险只在关键元素与触发元素交互时出现。
Paper analysisRead analysis
AttacksarXiv 2609.34920Sep 28
RISE：用迭代策略演化对现代文生图模型做红队测试
提出 RISE，迭代演化可复用策略对文生图模型做红队
arXiv
2609.34920
Published
Sep 28
Layer
Prompt layer
Setting
Models and APIs
Attacker
Black-box
Evaluated models
DALL·E 3, Nano Banana 2, GPT-Image-2 and others7
- AttacksJailbreaks
- TechniqueAutomated search
- ComponentImage generation
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2609.33401Sep 27
论文评估 System One 模型在 Agent 安全决策中的可靠性、校准与选择性自动化
评测 System One 模型对 Agent 安全输入的分类可靠性与校准
arXiv
2609.33401
Published
Sep 27
Layer
Prompt layer
Setting
LLM applications
Evaluated models
Jev 1.13, Laya, English checkpoint, Decider, approximately 2B and others4
- AttacksPrompt injection
- ComponentGuardrails and judges
Abstract离线评测表明，安全判定的总体分数和校准不能代替分组误差和带预算的自动决策检查。
Liu 离线评测 System One 模型能否为智能体安全输入给出可自动化的概率决策。问题是分类是否可靠，以及概率能否在指定漏检和误拦上限内支持放行、拦截或复核。
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2609.29429Sep 24
Jev 被用于零样本检测十类对齐失败，中位 AUROC 达 0.886
提出 RLCDAlignBench，零样本检测多种对齐失败
arXiv
2609.29429
Published
Sep 24
Layer
Prompt layer
Setting
Models and APIs
Evaluated models
Jev (jev-1.13.0)
- DefensesDetection and filtering
- AttacksJailbreaks
- RiskMisalignment and value divergence
- ComponentGuardrails and judges
Abstract一次通用问题已能给多数对齐失败排序，大的误差来自状态和标签。
RLCDAlignBench 测量用强化学习做校准决策的 Jev，能否当作对齐失败的零样本检测器。
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2609.29349Sep 24
ArGuard 共享任务：阿拉伯语模因与 LLM 提示词中的有害内容检测
组织 ArGuard 共享任务评测阿拉伯语表情包与提示词有害检测
arXiv
2609.29349
Published
Sep 24
Layer
Prompt layer
Setting
Models and APIs
Evaluated models
Qwen3-VL, Qwen3-VL-8B-Instruct, MARBERTv2 and others9
- DefensesDetection and filtering
- ComponentGuardrails and judges
Paper analysisRead analysis
InterpretabilityarXiv 2609.24801Sep 22
研究用 XAI 归因分析 Prompt Guard 2，同义词替换可翻转其提示注入判定
用词级归因指导同义词替换，翻转提示注入检测并验证越狱
arXiv
2609.24801
Published
Sep 22
Layer
Prompt layer
Setting
Models and APIs
Evaluated models
Prompt Guard 2 (86M), Llama 3.1 8B
- DefensesDetection and filtering
- AttacksDetection evasion
- TechniqueGradient and representation optimization
- ComponentGuardrails and judges
Abstract作者称检测靠分散的词法线索。
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2609.21793Sep 18
CASCADE 研究：跨流水线阶段的越狱防御组合评估
用 CASCADE 在统一威胁模型下筛选跨阶段越狱防御组合
arXiv
2609.21793
Published
Sep 18
Layer
Prompt layer
Setting
Models and APIs
Evaluated models
Vicuna, Llama2, Llama3 and others9
- AttacksJailbreaks
- ComponentGuardrails and judges
Paper analysisRead analysis

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