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Impact：Output manipulation System type：Models and APIs System type：coding agent System type：web agent System component：Guardrails and judges Clear all

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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
Analysis and theoryarXiv 2609.36477Sep 29
研究发现护栏模型在基座模型不确定处过度自信
诊断护栏相对基座在对抗提示上的置信失校
arXiv
2609.36477
Published
Sep 29
Layer
Model layer
Setting
Models and APIs
Evaluated models
Llama-Guard 7B, Llama-Guard-2 8B, Llama-Guard-3 8B and others10
- DefensesDetection and filtering
- AttacksJailbreaks
- ComponentGuardrails and judges
Abstract对抗包装使 guard 高置信漏检，而对应 base 在同一输入上常更不确定。
诊断性分析：五个提示词分类 guard 的置信度，在清洁输入上可以较校准，在越狱包装后则变成高置信度的错误判定。
Paper analysisRead analysis
DefensesarXiv 2609.32720Sep 26
BiasReducer：面向奖励模型的自适应偏见缓解方法
B IAS R EDUCER 只改奖励头，按数据集选择属性编辑。
arXiv
2609.32720
Published
Sep 26
Layer
Model layer
Setting
Models and APIs
Evaluated models
Skywork-Reward-V2-Qwen3-1.7B, GRM-Llama3.2-3B-rewardmodel-ft, RM-Mistral-7B and others7
- DefensesModel hardening
- RiskReward hacking
- ComponentGuardrails and judges
AbstractBIASREDUCER 按数据集选择奖励头编辑，五模型三基准平均提升并迁移到下游训练。
BIASREDUCER 是一种只改线性奖励头的奖励模型编辑框架，用来降低模型对预定义表面属性的依赖，并按新数据集决定改哪些属性。
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
DefensesarXiv 2608.12876Aug 13
SPARED：用对抗编辑数据训练基于推理的 AI 生成图像检测器
提出 SPARED，用对抗编辑数据训练基于推理的 AI 生成图像检测器
arXiv
2608.12876
Published
Aug 13
Layer
Training and data layer
Setting
Models and APIs
Attacker
Black-box, White-box
Evaluated models
Qwen3.5-9B
- DefensesDetection and filtering
- AttacksDetection evasion
- ComponentGuardrails and judges
AbstractSPARED 用防捷径的编辑对抗环训练 9B 推理检测器。
SPARED 是一套用于可解释 AI 生成图像检测的攻防强化学习流程，报告模型是 Qwen3.5-9B 上的第三轮防御器。
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2606.24115Jun 23
Gut-VLM 基准：九种幻觉检测方法在胃肠道内镜 VLM 上的评测
在 Gut-VLM 上评测内镜 VLM 的幻觉检测方法
arXiv
2606.24115
Published
Jun 23
Layer
Model layer
Setting
Models and APIs
Evaluated models
MedGemma-4B, MedGemma-27B, LLaVA-Med-7B and others5
- RiskHallucination and fabrication
- ComponentVision
Abstract九种检测方法在 Gut-VLM 五模型上的比较中，ReXTrust 的 AUC 均为最高。
这是一个胃肠道内镜 VQA 幻觉检测的比较基准，不是新检测器结构的提出。
Paper analysisRead analysis
AttacksarXiv 2602.01658Feb 2
Efficient Adversarial Attacks on High-Dimensional Offline Bandits
提出三类攻击，训练前扰动奖励模型以劫持离线 bandit
arXiv
2602.01658
Published
Feb 2
Layer
Training and data layer
Setting
Models and APIs
Evaluated models
Image Reward, Aesthetic Model
- AttacksPoisoning and backdoors
- ComponentGuardrails and judges
Abstract公开奖励权重可被小扰动劫持离线 bandit，打乱日志只部分缓解。
作者研究离线 bandit 在奖励模型被事先改权重时，还会不会选出最优臂。
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 2601.18113Jan 26
MalURLBench: A benchmark for evaluating agent vulnerabilities when processing web URLs
提出 MalURLBench 评测 Agent 是否接受结构伪装的恶意 URL
arXiv
2601.18113
Published
Jan 26
Layer
Application layer
Setting
web agent
Evaluated models
GPT-3.5-Turbo, GPT-4o-mini, GPT-4o and others13
- DefensesDetection and filtering
- AttacksJailbreaks
- ComponentGuardrails and judges
Paper analysisRead analysis
AttacksarXiv 2509.05372Sep 4, 2025
研究：对抗性 bug 报告可诱导 LLM 自动程序修复系统生成不安全补丁
用对抗缺陷报告诱导自动程序修复系统生成不安全补丁
arXiv
2509.05372
Published
Sep 4, 2025
Layer
Application layer
Setting
coding agent
Evaluated models
claude-sonnet-4-20250514, o4-mini, GPT-4.1-mini and others8
- AttacksPrompt injection
- TechniqueRole-play and persuasion
- ComponentGuardrails and judges
Paper analysisRead analysis

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