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Oct 13 · Tuesday

All papers

Found 19 papers

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  1. AttacksarXiv 2610.03166

    LiBRA:通过双向隐空间优化实现检测感知的图像水印去除

    提出 LiBRA,在潜空间双向优化以规避图像水印双侧检测

    Published
    Attacker
    White-box
    Evaluated models
    Stable Signature, YU1, HiDDeN and others4

    AbstractLiBRA 用双向潜空间优化规避双侧水印检测,并提高相对反转基线的保真。

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  2. AttacksarXiv 2609.27542

    Researchers use control token injection to delete gpt-oss-20b's reasoning chain and bypass chain-of-thought monitoring

    提出控制标记注入,清空工具 Agent 的思维链以绕过推理监控

    Published
    Setting
    AI Agent
    Evaluated models
    openai/gpt-oss-20b, google/gemma-4-26B-A4B-it, deepseek-ai/DeepSeek-R1-Distill-Qwen-7B and others4

    Abstract论文阐明控制标记注入可抑制 CoT 使监控失效并促成拒绝绕过,且工具执行受解析器宽容度影响。

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  3. 研究用 XAI 归因分析 Prompt Guard 2,同义词替换可翻转其提示注入判定

    用词级归因指导同义词替换,翻转提示注入检测并验证越狱

    Published
    Evaluated models
    Prompt Guard 2 (86M), Llama 3.1 8B

    Abstract作者称检测靠分散的词法线索。

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  4. 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

    Published
    Evaluated models
    GPT-5.4 mini, GPT-5.5, DeepSeek V4 Flash and others12
    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。

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  5. 研究者提出最小范数攻击集成,用鲁棒性曲线替代单一 ε 评测

    提出最小范数攻击集成,用鲁棒性曲线评测图像分类器

    Published
    Evaluated models
    C1–C20(CIFAR-10,Table 1), I1–I10(ImageNet,Table 1)
    Abstract预算可控的最小范数集成用 AOI 逼近攻击前沿,DOI 给出不依赖单一ε的防御排序。

    这篇工作把对抗鲁棒评测从单一 ε、固定集成,改成可按查询预算收紧的曲线估计。

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  6. AttacksarXiv 2606.09700

    The researchers propose the HPAA attack: bypassing 13 content moderation systems with typographic visual cues

    提出 HPAA 攻击,用排版视觉线索绕过内容审核系统

    Published
    Attacker
    Black-box
    Evaluated models
    Llama-Guard-3-8B, Perspective API, ShieldGemma-2B and others13
    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。

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  7. AttacksarXiv 2605.21541

    研究者提出 FRA-Attack 频域正则化方法,提升对闭源 MLLM 的迁移攻击效果

    提出 FRA-Attack,用频域对齐与梯度正则做可迁移定向视觉攻击

    Published
    Attacker
    Black-box
    Evaluated models
    GPT-5.2, GPT-5.4, Claude-Opus-4.6 and others15
    AbstractFRA-Attack 以频域对齐和梯度低通做可迁移定向攻击,闭源面板上作者称整体更强,Gemma 除外。

    FRA-Attack 是面向闭源 MLLM 的迁移式定向图像攻击。作者要解决的是:空间域局部对齐重复全局低频,代理梯度的高频成分又把更新拉向代理自身。

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  8. AttacksarXiv 2604.03117

    Unveiling Physical-World Semantic Vulnerabilities: Universal Adversarial Patches for Infrared Vision-Language Models

    Proposes the universal curved mesh patch UCGP, causing cross-task semantic bias in infrared vision-language models.

    Published
    Attacker
    Gray-box
    Evaluated models
    OpenAI CLIP ViT-L/14, OpenCLIP ViT-B/16, Meta-CLIP ViT-L/14 and others10
    AbstractUCGP uses one shared curved-grid patch to simultaneously lower the classification and open-ended outputs of IR-VLM.

    UCGP is a universal physical adversarial patch for infrared vision-language models. The question is: can a patch that can be attached and reused across samples cause stable semantic deviations in the infrared visual evidence shared by classification, description, and VQA. The patch is constrained to a low-frequency curved mesh.

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  9. AttacksarXiv 2602.09431

    Grounding-driven attack: Improving the transferability of encoder-based adversarial attacks to large vision-language models

    Propose GDA, which aligns image adversarial perturbations with text-grounded evidence and transfers attacks to vision-language models

    Published
    Evaluated models
    LLaVA, Qwen2.5-VL, InternVL3 and others10
    AbstractGDA aligns perturbations with text-grounded evidence, and the authors say this improves zero-query black-box transferability on heterogeneous LVLMs.

    GDA is a zero-query black-box encoder transfer attack for heterogeneous LVLMs.

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  10. AttacksarXiv 2602.05484

    Masked Mirror: Covert Prompt Injection Attacks Against LLM-Based Phishing Detection

    提出隐蔽提示注入攻击 LLM 钓鱼检测器,并给出防御 InjectDefuser

    Published
    Evaluated models
    OpenAI GPT-5, xAI Grok 4 Fast (Non-Reasoning), Meta Llama 4 Maverick and others4
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  11. AttacksarXiv 2601.21531

    Adversarial robustness of large vision-language models under visual token compression

    提出 CAGE 攻击,在视觉 token 压缩下用图像扰动使预测偏离

    Published
    Attacker
    Gray-box
    Evaluated models
    LLaVA, Qwen2.5-VL, InternVL3-8B and others5
    AbstractCAGE 对齐压缩瓶颈后,压缩 LVLM 的稳健准确率低于全 token 编码器攻击。

    CAGE 研究的是:LVLM 在推理时剪枝或合并视觉 token 之后,对抗稳健性该怎么估。

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  12. AttacksarXiv 2509.09112

    研究:字符级扰动可破坏 LLM 水印,并提出基于遗传算法的去除攻击

    提出字符级扰动与遗传算法去除 LLM 水印

    Published
    Attacker
    Black-box
    Evaluated models
    OPT-1.3B, LLaMA-3-8B
    Abstract字符级扰动靠更大攻击范围移除水印。

    这篇工作评估推理时 LLM 水印在黑盒、有限查询下的移除难度,并比较字符级、token 级和句子级编辑。

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  13. AttacksarXiv 2505.23266

    AdvOF:用对抗物体融合攻击 VLM 驱动的视觉语言导航智能体

    提出 AdvOF,生成对抗物体误导视觉语言导航智能体的场景感知

    Published
    Attacker
    White-box
    Evaluated models
    Lseg, Clip, ViT-L/16 and others5
    AbstractAdvOF 以多视角融合生成对抗物体。

    AdvOF 是针对 VLM 驱动 VLN 服务导航的对抗物体攻击。

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