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Oct 12 · Monday

All papers

Found 11 papers

Filter3

Attack type

Detection evasion7

Impact

Harmful content42 Output manipulation11 Oversight failure4

System type

Models and APIs7 LLM applications4

Attacker access

Black-box4

Attack surface / entry point

User input7

Layer

Prompt layer11 Model layer3 Application layer20

System component

Guardrails and judges11 Vision24 Audio3 Multimodal30

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Impact：Output manipulation Layer：Prompt layer System component：Guardrails and judges Clear all

Another 265papers do not have category tags yet and are not included in filtered results.

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
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 2610.00883Oct 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 2609.08236Sep 8
研究：内容不变的风格包装可翻转 LLM 安全评判器的判定
提出内容不变样式包装，翻转安全评审器的判定
arXiv
2609.08236
Published
Sep 8
Layer
Prompt layer
Setting
Models and APIs
Attacker
Black-box
Evaluated models
GPT-4o-mini (gpt4o), GPT-4o-mini (strongreject), DeepSeek-Chat and others7
- AttacksDetection evasion
- TechniqueRole-play and persuasion
- ComponentGuardrails and judges
Abstract论文揭示特定安全评审器易受保持内容不变的外层包装诱导改变判定，并指出安全评估应将判定稳定性作为重要考量指标。
这是一项关于大语言模型自动化安全评审系统表面样式脆弱性的评估研究。
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2609.05843Sep 5
SinoGlyphBench：中文字形混淆下语言模型审核的诊断基准
提出 SinoGlyphBench，用配对字形混淆诊断中文审核稳定性
arXiv
2609.05843
Published
Sep 5
Layer
Prompt layer
Setting
Models and APIs
Evaluated models
GPT-5.4 mini, GPT-5.5, DeepSeek V4 Flash and others12
- AttacksDetection evasion
- TechniqueEncoding and obfuscation
- ComponentGuardrails and judges
AbstractSinoGlyphBench 显示中文字形混淆会同时增加有害漏报和误报，全文扰动最重。
SinoGlyphBench是一个配对诊断基准，用来看内容审核在可还原的中文字形混淆下是否还保持原决定。
Paper analysisRead analysis
AttacksarXiv 2606.09700Jun 9
研究者提出 HPAA 攻击：用排版视觉线索绕过 13 个内容审核系统
提出 HPAA 攻击，用排版视觉线索绕过内容审核系统
arXiv
2606.09700
Published
Jun 9
Layer
Prompt layer
Setting
LLM applications
Attacker
Black-box
Evaluated models
Llama-Guard-3-8B, Perspective API, ShieldGemma-2B and others13
- AttacksDetection evasion
- TechniqueEncoding and obfuscation
- ComponentGuardrails and judges
Abstract揭示了排版视觉线索与分词差异带来的审核盲区，HPAA 以极低查询实现近乎完全规避。
这篇论文研究了人类视觉感知与大模型分词处理机制之间的差异所导致的内容审核漏洞。
Paper analysisRead analysis
AttacksarXiv 2605.26156May 24
BITE：用上下文赌博机操纵风格偏置攻击 LLM 评委
提出 BITE，用上下文赌博机选择风格修改抬高 LLM 评委打分
arXiv
2605.26156
Published
May 24
Layer
Prompt layer
Setting
LLM applications
Attacker
Black-box
Evaluated models
o3-mini, Gemini-2.5-Flash, Llama-3.3-70B-Instruct and others8
- AttacksDetection evasion
- TechniqueAutomated search
- ComponentGuardrails and judges
AbstractBITE 用黑盒赌博机利用风格偏差抬分，作者称这对 LLM-as-a-judge 构成威胁。
BITE 是黑盒风格操纵：在保持答案语义的同时，抬高 LLM 评审给出的分数。
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2601.20256Jan 28
SoftHateBench：评估审核模型对推理驱动、政策合规式敌意的检测
提出 SoftHateBench，评测审核模型对推理驱动软仇恨的检测
arXiv
2601.20256
Published
Jan 28
Layer
Prompt layer
Setting
Models and APIs
Evaluated models
HateBERT (IMSyPP), HateBERT (GroNLP), HateRoBERTa and others12
- AttacksDetection evasion
- ComponentGuardrails and judges
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2601.13537Jan 20
When Wording Sways Evaluation: Framing Bias in LLM Judges
测量 LLM 评委在谓词极性翻转下的判定不一致
arXiv
2601.13537
Published
Jan 20
Layer
Prompt layer
Setting
LLM applications
Evaluated models
Llama-3.2-1B-Instruct, Llama-3.1-8B-Instruct, Llama-3.1-70B-Instruct and others14
- ComponentGuardrails and judges
Paper analysisRead analysis

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