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Oct 13 · Tuesday

All papers

Found 17 papers

Filter6

Attack type

Detection evasion3

Defense type

Detection and filtering4

Impact

Harmful content3 Output manipulation9

System type

Models and APIs12 LLM applications44 AI Agent12 coding agent0 web agent3 Embodied systems and robotics2

Attack surface / entry point

User input3 Images, audio and video8 Training data3

Layer

Prompt layer7 Model layer5 Application layer3

System component

MCP and skills27 Memory13 RAG4 Guardrails and judges101 Monitors52 Reasoning chains66 Permissions and sandboxing15 Fine-tuning and open weights64 Vision120 Audio32 Video13 Image generation46 Embeddings4 Multimodal201

Select any value within a category; different categories must all match. Counts include the other filters you have selected.Another 213papers have no tags yet and are not included in filtered results.

System type：Models and APIs System type：coding agent System type：web agent System type：Embodied systems and robotics System component：RAG System component：Video Clear all

Another 213papers do not have category tags yet and are not included in filtered results.

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 2610.08331Oct 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
Evaluation and benchmarksarXiv 2610.06171Oct 5
ControlPed 生成逼真行人危险动作场景，七款端到端驾驶模型 HDScore 从 88.8 降至 47.4
提出 ControlPed 与 HazardPed，生成可控行人危险场景并评测端到端驾驶
arXiv
2610.06171
Published
Oct 5
Layer
Model layer
Setting
Embodied systems and robotics
Evaluated models
DiffusionDrive, SSR, VAD and others7
- ComponentVideo
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2610.05586Oct 5
AgentDoxx：联网搜索 Agent 对匿名访谈文本的再识别评测
构建 AGENTDOXX 基准，评测联网搜索 Agent 对匿名访谈的再识别
arXiv
2610.05586
Published
Oct 5
Layer
Application layer
Setting
web agent
Evaluated models
GPT-5.6 Terra, GPT-5 Mini, GPT-4.1 Mini and others10
- AttacksMalicious use
- ComponentRAG
Abstract论文提出了已知身份的重识别基准 AGENTDOXX，揭示搜索与记忆双重风险及现有防御在检索轨迹中的泄露漏洞。
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
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
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
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
AttacksarXiv 2608.04565Aug 5
Researchers propose Breadcrumbing long-range attack that can hijack the evidence chain of DeepResearch-style search agents
提出权威链劫持与轨迹引导策略演化，劫持搜索 Agent 的证据链
arXiv
2608.04565
Published
Aug 5
Layer
Application layer
Setting
web agent
Evaluated models
Qwen3.5-9B, Gemma4-31B, DeepResearch and others7
- AttacksPrompt injection
- TechniqueAutomated search
- ComponentRAG
Abstract论文揭示了多步搜索智能体在受限中间人操纵下的脆弱性，提出 ACH 策略与 TGSE 演化方法并完成系统验证。
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2607.20891Jul 23
MisKnow-Agent 评测
用 MisKnow-Agent 评测深度研究智能体是否采纳误导文档结论
arXiv
2607.20891
Published
Jul 23
Layer
Application layer
Setting
web agent
Evaluated models
DeerFlow, WebThinker, Gemini Deep Research and others6
- AttacksPrompt injection
- ComponentRAG
Abstract论文通过 MisKnow-Agent 框架评估发现长程研究智能体易采纳误导性知识，且现有防御难以完全消除该现象。
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
AttacksarXiv 2606.09499Jun 8
研究者提出针对世界模型的机器人学习管线投毒攻击
提出 VPH 与 VTH，只改视频帧劫持世界模型并投毒下游策略
arXiv
2606.09499
Published
Jun 8
Layer
Training and data layer
Setting
Embodied systems and robotics
Evaluated models
Cosmos-Predict 2.5, Cosmos-Predict 2.5 AC, Dino WM and others4
- AttacksPoisoning and backdoors
- TechniqueGradient and representation optimization
- ComponentVideo
Abstract只改视频帧经世界模型投毒。
只改看似安全的视频帧，经世界模型之后才改变下游机器人策略。 问题在于世界模型进入数据生成后，数据集检查看到的仍是安全演示，合成轨迹却可以变危险。
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
Evaluation and benchmarksarXiv 2512.03992Dec 4, 2025
DIQ-H 基准与 VGIR 框架：评估 VLM 在对抗视觉条件下的鲁棒性
提出 DIQ-H 基准，评测 VLM 在连续退化视觉流中的幻觉残留与恢复
arXiv
2512.03992
Published
Dec 4, 2025
Layer
Model layer
Setting
Models and APIs
Evaluated models
LLaVA-7B-strict, InternVL2-8B-strict, Phi-3-Vision-strict and others17
- RiskHallucination and fabrication
- ComponentVideo
AbstractDIQ-H 用连续退化视频测幻觉延续与恢复，VIR 用不确定性过滤生成伪标注。
DIQ-H 评测 VLM 在连续视觉退化中的幻觉、恢复和跨时间一致性。VIR 用轻量模型的不确定性过滤生成伪真值。
Paper analysisRead analysis

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