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Oct 12 · Monday

All papers

Found 11 papers

Filter4

Attack type

Jailbreaks203 Prompt injection9 Poisoning and backdoors6 Model tampering3 Extraction and theft39 Detection evasion16 Denial of service3 Malicious use11

Impact

Harmful content4

System type

Models and APIs9 AI Agent11 coding agent4 Browser and computer use2

Attack surface / entry point

User input11 Images, audio and video6

Layer

Prompt layer3 Model layer4

System component

Image generation4 Multimodal5

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Attack type：Malicious use System type：Models and APIs System type：Browser and computer use Attack surface / entry point：User input Clear all

Another 232papers do not have category tags yet and are not included in filtered results.

DefensesarXiv 2610.08316Oct 6
MARCO：面向蛋白质生成模型的放射性水印框架
提出 MARCO 放射性水印，冻结蛋白质生成模型并在去噪时嵌入签名
arXiv
2610.08316
Published
Oct 6
Layer
Model layer
Setting
Models and APIs
Attacker
Black-box
Evaluated models
RFdiffusion, ESMFold, Chroma and others6
- DefensesWatermarking and provenance
- AttacksExtraction and theft
AbstractMARCO 在冻结的 PGM 上去噪时嵌构象水印，作者称能兼顾保真与抗扰动，并使盗版模型继承水印。
MARCO 是加在蛋白质生成模型推理过程上的构象水印，用来标记知识产权，并为可能的生物安全滥用保留可追溯信号。作者还把它说成数字权利管理工具，以及符合生物武器公约的数字监管链。问题在于：专有 PGM 的三维输出可能被拿去训练盗版模型，也可能在来源被抹掉后绕过筛查。
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2610.06503Oct 5
研究评测五款开源文生图模型的有害内容生成与审核缺口
评测文生图模型的有害内容生成能力与审核检测缺口
arXiv
2610.06503
Published
Oct 5
Layer
Model layer
Setting
Models and APIs
Evaluated models
SDXL, Stable Diffusion 3.5 Large, FLUX.1-dev and others12
- DefensesDetection and filtering
- AttacksMalicious use
- ComponentImage generation
Paper analysisRead analysis
DefensesarXiv 2610.03980Oct 3
FADE：面向视频扩散模型的帧感知多概念擦除框架
提出 FADE，对文生视频模型做帧感知多概念擦除
arXiv
2610.03980
Published
Oct 3
Layer
Training and data layer
Setting
Models and APIs
Evaluated models
Wan2.1-T2V-1.3B, Wan2.1-T2V-14B, CogVideoX-2B and others4
- DefensesModel hardening
- AttacksMalicious use
- ComponentImage generation
AbstractFADE 用闭式编辑、帧感知适配器和软路由，在单骨干上擦除多概念并压低逐帧残差。
FADE 是面向文生视频扩散 Transformer 的多概念遗忘方法，用来抑制指定物体、画风、裸体、暴力、仇恨和名人身份，并尽量保持其他生成。作者要处理两件事。与帧无关的抑制会让概念在少数帧重现，片段均值会掩盖这一失败。
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2609.35028Sep 28
VEX-Bench：评测 LLM 生成虚假信息的核验复杂度
提出 VEX-Bench 评测 LLM 生成虚假信息的核查复杂度
arXiv
2609.35028
Published
Sep 28
Layer
Prompt layer
Setting
Models and APIs
Evaluated models
Claude Sonnet 4.5, Gemini 3.1 Pro, GPT-5.4 and others7
- AttacksMalicious use
AbstractVEX-Bench 用筛查时的五维复杂度衡量虚假信息对核查容量的占用，并报告生成与核查的成本差。
VEX-Bench 把 LLM 虚假信息的风险从“能否生成”改成“筛查时会占用多少核查容量”。
Paper analysisRead analysis
DefensesarXiv 2609.22724Sep 19
MATE：面向移动 Agent 的策略感知安全审计方法
提出移动 Agent 轨迹审计器 MATE，判断轨迹是否违反可编辑安全策略
arXiv
2609.22724
Published
Sep 19
Layer
Application layer
Setting
Browser and computer use
Attacker
Black-box
Evaluated models
MATE-0.5B, MATE-1.5B, MATE-3B
- DefensesMonitoring and auditing
- AttacksMalicious use
- RiskDangerous agent actions
- ComponentMonitors
AbstractMATE 用合成轨迹微调轻量审计器，在可编辑策略下判断移动智能体轨迹是否违规并给出解释。
MATE 是面向移动 GUI 智能体的轻量策略条件轨迹审计器，在给定自然语言安全策略时判断轨迹是否违规并解释原因。
Paper analysisRead analysis
DefensesarXiv 2609.03629Sep 3
EraseSAE：用稀疏自编码器在文生视频扩散模型中实现精准概念擦除
提出 EraseSAE，在文生视频模型中局部擦除指定概念
arXiv
2609.03629
Published
Sep 3
Layer
Model layer
Setting
Models and APIs
Evaluated models
CogVideoX-5B, HunyuanVideo, Flux.1 [dev]
- DefensesInference-time intervention
- AttacksMalicious use
- ComponentImage generation
AbstractEraseSAE 用单义特征局部擦除。
EraseSAE 把 SAE 用于 DiT 文生视频模型的概念擦除。作者要解决的是：全局改权重或表面负提示对不齐细粒度、分布式的概念，而且会改到无关时空区域。
Paper analysisRead analysis
DefensesarXiv 2608.29030Aug 29
研究者提出自蒸馏方法让 LLM 遵循上下文水印指令
提出 SDLP 与 GRPO，训练模型遵循上下文水印指令
arXiv
2608.29030
Published
Aug 29
Layer
Training and data layer
Setting
Models and APIs
Evaluated models
GPT-5.4, GPT-5.4-mini, GPT-5.4-nano and others14
- DefensesWatermarking and provenance
- AttacksMalicious use
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2604.02947Apr 3
AgentHazard：评估计算机使用智能体有害行为的基准
提出 AgentHazard 基准，评测计算机使用智能体的执行层有害行为
arXiv
2604.02947
Published
Apr 3
Layer
Application layer
Setting
Browser and computer use, coding agent
Evaluated models
Qwen2.5-72B-Instruct, Qwen2.5-Coder-32B-Instruct, Qwen3-32B and others12
- AttacksMalicious use
AbstractAgentHazard 评估了计算机使用智能体的执行安全，作者认为模型层对齐无法可靠保障智能体层面的安全性。
AgentHazard 是针对计算机使用智能体执行层安全构建的评估基准，重点探究多步工具调用下局部合理动作累积引发的危害。
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2602.21831Feb 25
A multi-turn framework for evaluating AI misuse in fraud and cybercrime scenarios
提出 LFT 多轮框架，评测模型对欺诈与网络犯罪的可执行协助
arXiv
2602.21831
Published
Feb 25
Layer
Prompt layer
Setting
Models and APIs
Evaluated models
Claude Sonnet 3.5, Claude Sonnet 3.7, Claude Sonnet 4 and others14
- AttacksMalicious use
Abstract作者称低努力文本诱导下，多轮 LFT 中模型对三类犯罪的信息协助有限。
作者给出一套由执法与政策专家校验的多轮评测，用来估计文本模型经由信息提供，对欺诈和网络犯罪有多少可执行协助。
Paper analysisRead analysis
DefensesarXiv 2602.20880Feb 24
When safety conflicts: resolving multi-category harmful conflicts in text-to-image diffusion models with adaptive safety guidance
Proposes CASG to resolve direction conflicts in multi-category safety guidance for text-to-image generation.
arXiv
2602.20880
Published
Feb 24
Layer
Model layer
Setting
Models and APIs
Evaluated models
Stable Diffusion v1.5, SDv3
- DefensesInference-time intervention
- AttacksMalicious use
- ComponentImage generation
Paper analysisRead analysis
AttacksarXiv 2601.12983Jan 19
ChartAttack：测试大语言模型在图表生成中对恶意提示的脆弱性
提出 ChartAttack，用恶意提示驱动代码模型生成误导图表
arXiv
2601.12983
Published
Jan 19
Layer
Prompt layer
Setting
Models and APIs
Evaluated models
Claude-4.6-Sonnet, GPT-4o, Gemini-2.5-Flash and others20
- AttacksMalicious use
- ComponentVision
AbstractChartAttack 生成不改数据的误导图，降低 MLLM 与人类的图表问答准确率。
ChartAttack 研究恶意提示能否让 MLLM 在不改数据的情况下生成误导图表，并量化这些图对其他 MLLM 和人类图表问答的影响。
Paper analysisRead analysis

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