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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
AttacksarXiv 2610.09920Oct 7
研究：多向量视觉文档索引可被反演还原页面内容
提出流匹配逆向框架，从多向量视觉文档索引重构页面
arXiv
2610.09920
Published
Oct 7
Layer
Application layer
Setting
LLM applications
Evaluated models
Tomoro-ColQwen3-8B, ColQwen3.5-4.5B, Tomoro-colqwen3-embed-4b and others13
- AttacksExtraction and theft
- ComponentEmbeddings
Paper analysisRead analysis
AttacksarXiv 2610.08331Oct 6
STCA：研究者提出针对自动驾驶视频 VLM 的时空一致性对抗攻击
提出 STCA，对驾驶视频 VLM 构造可迁移的时空对抗扰动
arXiv
2610.08331
Published
Oct 6
Layer
Prompt layer
Setting
Models and APIs
Attacker
No-box (offline transfer)
Evaluated models
Video-LLaVA-7B, Qwen2.5-VL-7B, Dolphins
- AttacksDetection evasion
- TechniqueGradient and representation optimization
- ComponentVideo
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 2610.06339Oct 5
BabelFake：面向多语言音视频 DeepFake 检测的基准数据集
构建多语言音视频 DeepFake 基准 BabelFake 并评测检测器
arXiv
2610.06339
Published
Oct 5
Layer
Model layer
Setting
Models and APIs
Evaluated models
SpeechForensics, AVH-Align, (B)RAVEn
- DefensesDetection and filtering
- AttacksDetection evasion
- ComponentAudio
AbstractBabelFake 为五语同意采集基准。
BabelFake 是经同意与 IRB 批准采集的多语音视频 DeepFake 检测基准，用来把视觉操纵和语音合成拆开比较。
Paper analysisRead analysis
DefensesarXiv 2610.02999Oct 2
OmniConfess：用 Token 级「忏悔」缓解全模态幻觉
提出 OmniConfess，冻结候选并按通道重打分以缓解全模态幻觉
arXiv
2610.02999
Published
Oct 2
Layer
Model layer
Setting
Models and APIs
Evaluated models
Qwen2.5-Omni-7B, Qwen3-Omni-30B-A3B, Nemotron-3-Nano-Omni-30B
- DefensesInference-time intervention
- RiskHallucination and fabrication
- ComponentVision
Paper analysisRead analysis
AttacksarXiv 2609.38899Sep 30
SceneJail: Jailbreaking multimodal large models using video scene context
提出 SceneJail，利用视频情境上下文越狱视频多模态模型
arXiv
2609.38899
Published
Sep 30
Layer
Prompt layer
Setting
Models and APIs
Attacker
Black-box
Evaluated models
Qwen2.5-VL-7B-Instruct, Qwen2.5-VL-32B-Instruct, Qwen3-VL-8B-Instruct and others8
- AttacksJailbreaks
- TechniqueCross-modal payloads
- ComponentVideo
Paper analysisRead analysis
InterpretabilityarXiv 2609.09575Sep 9
MonoTM：用可解释单义特征做主题建模，超越关键词表示
提出 MonoTM，用 SAE 单义特征估计文档主题混合并生成主题描述
arXiv
2609.09575
Published
Sep 9
Layer
Model layer
Setting
Models and APIs
- ComponentEmbeddings
AbstractMonoTM 分开估计混合与解释主题，三套基准上二者偏好的 SAE 配置不同。
MonoTM 是建在文档嵌入 SAE 上的可解释主题模型：混合估计用全部 SAE 特征，主题描述只用另一套配置里通过验证的语义特征。
Paper analysisRead analysis
InterpretabilityarXiv 2609.06968Sep 7
通过稀疏自编码器特征追踪查询扩展效果
用 SAE 追踪稠密检索查询扩展的潜变量并用激活转向验证
arXiv
2609.06968
Published
Sep 7
Layer
Model layer
Setting
LLM applications
Evaluated models
Qwen3-Embedding-0.6B（Qwen-Embed）, SimLM
- ComponentEmbeddings
AbstractSAE 追踪稠密检索 QE 的潜变量，转向在四基准上比文本扩展更稳。
作者用稀疏自编码器分析稠密检索里的查询扩展：现成检索器不微调时，拼接扩展文本往往增益有限甚至变差，而最终指标和嵌入整体偏移不足以说明内部发生了什么。
Paper analysisRead analysis
AttacksarXiv 2609.06749Sep 7
PMA 攻击：针对隐私保护 LLM 中嵌入到嵌入混淆的语义流形对齐方法
提出 PMA，用流形对齐从 E2EO 混淆向量恢复明文
arXiv
2609.06749
Published
Sep 7
Layer
Model layer
Setting
Models and APIs
Evaluated models
BERT-base, RoBERTa-base, T5-base and others6
- AttacksExtraction and theft
- ComponentEmbeddings
Paper analysisRead analysis
DefensesarXiv 2609.04767Sep 4
SHAQ: Privacy-preserving retrieval in vector databases using shadow queries
Proposes SHAQ, which replaces document embeddings with shadow query embeddings to defend against embedding inversion.
arXiv
2609.04767
Published
Sep 4
Layer
Application layer
Setting
LLM applications
Attacker
Black-box
Evaluated models
GTR-T5 encoder, DPR
- AttacksExtraction and theft
- ComponentEmbeddings
AbstractSHAQ replaces document embeddings with shadow query embeddings.
SHAQ is a defense against EIA for cloud vector databases: instead of directly modifying document embeddings, it replaces them with shadow query embeddings.
Paper analysisRead analysis
DefensesarXiv 2608.18607Aug 19
VA-Judger：面向联合视频-音频生成的人类偏好反馈奖励建模
提出 VA-Judger，用人类偏好训练联合音视频奖励模型
arXiv
2608.18607
Published
Aug 19
Layer
Training and data layer
Setting
Models and APIs
Evaluated models
VA-Judger, LTX-2 + VA-Judger
- DefensesModel hardening
- RiskReward hacking
- ComponentGuardrails and judges
AbstractVA-Judger 用易到难的人类偏好学习做联合音视频奖励，并用于 LTX-2 后训练。
VA-Judger 是面向联合音视频生成的思维链全模态奖励模型，用来替代由分维度专家指标拼成的强化学习奖励。
Paper analysisRead analysis
DefensesarXiv 2607.14737Jul 16
GeoDetect：面向视觉语言预训练模型的几何对抗检测方法
提出 GeoDetect 以几何分数检测视觉语言预训练模型的对抗样本
arXiv
2607.14737
Published
Jul 16
Layer
Model layer
Setting
Models and APIs
Attacker
White-box
Evaluated models
CLIPViT (ViT-B/16), CLIPCNN (ResNet-50), ALBEF and others4
- DefensesDetection and filtering
- AttacksDetection evasion
- ComponentEmbeddings
AbstractGeoDetect 用几何分数检测 VLP 对抗样本。
GeoDetect 是面向视觉-语言预训练模型的对抗样本检测方法，用嵌入几何而不是任务 logits 区分干净样本与对抗样本。
Paper analysisRead analysis
DefensesarXiv 2607.13336Jul 15
TC-UAP：针对图像转视频与微调定制的通用视频保护方法
提出 TC-UAP，用身份级多帧扰动保护视频免被定制复现身份
arXiv
2607.13336
Published
Jul 15
Layer
Prompt layer
Setting
Models and APIs
Evaluated models
LTX-2.3, Wan2.2-5B
- ComponentVideo
AbstractTC-UAP 以可复用的时间一致扰动干扰两类定制共用的 VAE 隐空间。
作者要解决的是：发布前如何给视频加不可见扰动，使片段既不能被拿去微调视频扩散模型，也不能被抽帧做 image-to-video。
Paper analysisRead analysis
DefensesarXiv 2607.04379Jul 6
PA-LLM-RAG 框架的知识库投毒攻击与防御
提出策略 RAG 的知识库投毒攻击与检测防御 CLD-KB
arXiv
2607.04379
Published
Jul 6
Layer
Application layer
Setting
LLM applications
Evaluated models
Llama 3.2
- DefensesDetection and filtering
- AttacksPoisoning and backdoors
- ComponentRAG
Paper analysisRead analysis
AttacksarXiv 2607.01276Jul 1
研究者提出嵌入推断攻击 EIA，可在黑盒 IR 系统中识别所用嵌入模型
提出嵌入推断攻击 EIA，在黑盒 IR 中识别所用嵌入模型
arXiv
2607.01276
Published
Jul 1
Layer
Application layer
Setting
LLM applications
Attacker
Black-box
Evaluated models
e5-small, e5-small-v2, e5-base and others19
- AttacksExtraction and theft
- ComponentEmbeddings
AbstractEIA 用无序文档集的差异从候选中识别嵌入模型，并给出 reranker 与阈值下的计数。
EIA 是在黑盒 IR 上识别所用嵌入模型的攻击。。
Paper analysisRead analysis
AttacksarXiv 2606.12716Jun 11
PaperGuard：针对多模态 AI 同行评审的攻击与防御基准
提出 PaperGuard，用图文注入与梯度扰动抬高自动审稿分数并检测恶意指令
arXiv
2606.12716
Published
Jun 11
Layer
Application layer
Setting
LLM applications
Evaluated models
Claude-sonnet-4.5, GPT-4o, GPT-4.1 and others11
- AttacksPrompt injection
- TechniqueCross-modal payloads
- ComponentVision
AbstractPaperGuard 测到提示注入与白盒视觉抬分，并比较分块检索防御。
PaperGuard同时评测并检测多模态 AI 审稿中的评分操纵。
Paper analysisRead analysis
AttacksarXiv 2605.28074May 27
SilentRetrieval: Hijacking retrieval-augmented generation with semantically preserved adversarial data poisoning
提出 SilentRetrieval，以语义保持对抗文档投毒劫持 RAG 检索与生成
arXiv
2605.28074
Published
May 27
Layer
Application layer
Setting
LLM applications
Evaluated models
Llama-2-7B-Chat, Mistral-7B-Instruct-v0.2, Qwen-7B-Chat and others10
- AttacksPoisoning and backdoors
- TechniqueGradient and representation optimization
- ComponentRAG
Abstract两阶段投毒在单文档协议下兼顾检索劫持、生成操纵与近良性困惑度。
SilentRetrieval 是针对 RAG 的两阶段语料投毒，在每查询一篇、合成目标答案的统一协议下，同时处理检索劫持、生成操纵和流畅度。
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2605.18984May 19
Artifact-Bench：评估 MLLM 检测与诊断 AI 生成视频伪影
提出 Artifact-Bench，评测 MLLM 检测并诊断 AI 生成视频伪影
arXiv
2605.18984
Published
May 19
Layer
Model layer
Setting
Models and APIs
Evaluated models
Gemini 3.1 Pro, Gemini 3 Flash, Qwen3-VL 8B-Instruct and others19
- DefensesDetection and filtering
- ComponentVideo
AbstractArtifact-Bench 以三项任务评 MLLM 的 AIGC 伪影感知。
Artifact-Bench 用来看 MLLM 能否发现 AI 生成视频中的伪影，并说明视频为何不真实。
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2602.10639Feb 11
VideoSTF 揭示视频大模型输出重复问题
提出 VIDEO STF 评测视频大模型输出重复并压力测试时序扰动
arXiv
2602.10639
Published
Feb 11
Layer
Prompt layer
Setting
Models and APIs
Evaluated models
LLaVA-Video-7B-Qwen2, LLaVA-Video-7B-Qwen2-Video-Only, LLaVA-NeXT-Video-7B and others10
- AttacksDenial of service
- ComponentVideo
AbstractVIDEO STF 把输出重复做成可测、可被时间扰动放大、并可黑盒诱导的失败。
VIDEO STF 测量 VideoLLM 的输出重复，并把时间变换用作 black-box 诱导。
Paper analysisRead analysis

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