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Oct 12 · Monday

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  1. AttacksarXiv 2610.12644

    Cancerous backdoor attack: progressive activation via invisible tokens and a stage controller.

    Proposes the Cancerous backdoor, which progressively corrupts reasoning using invisible stage tokens.

    Published
    Evaluated models
    Llama-3.1-8B-Instruct, Qwen2.5-7B-Instruct, Mistral-7B-Instruct-v0.3
    AbstractHidden tokens plus a stage controller make the backdoor progressively corrupt reasoning upon triggering, and the authors believe that looking only at the answer will miss it.

    Cancerous Backdoor Attack changes LLM backdoors from one-time activation to multi-stage reasoning corruption that accumulates progressively with triggers.

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  2. AttacksarXiv 2610.09027

    P-VMI:对抗图像经 KV cache 持续影响多轮对话

    提出持久视觉记忆注入,使对抗图像经缓存在触发时操控输出

    Published
    Evaluated models
    Qwen3-8B, Qwen3-VL-8B-Instruct, Gemma-4-12B

    Abstract论文提出 P-VMI,证实对抗输入被掩蔽后仍可经 KV 缓存持久控制模型,强调了保留状态的安全风险。

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  3. AttacksarXiv 2610.05894

    研究者提出闭环偏见注入攻击

    提出闭环激活引导,在扩散语言模型去噪过程中注入定向偏见

    Published
    Attacker
    Gray-box
    Evaluated models
    LLaDA-8B-Instruct, Dream-v0-Instruct-7B, LLaDA-MoE-7B-A1B-Instruct

    Abstract通过去噪中间概率反馈动态调节残差流转向,证明扩散语言模型在推理服务栈中存在闭环偏见注入脆弱性。

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  4. AttacksarXiv 2610.03430

    JIL:通过对抗后缀操纵 LLM 请求调度优先级

    提出 JIL 对抗后缀,压低长度预测以抬高请求调度优先级

    Published
    Evaluated models
    Llama-3-8B-Instruct, Qwen2.5-7B-Instruct, Mistral-7B-Instruct-v0.3 and others4
    AbstractJIL 用对抗后缀压低长度预测,在预测式 LLM 调度中提高自身优先级。

    JIL 是对预测式 LLM 调度的攻击:用对抗后缀压低长度预测,使请求在 SRPT 式队列里更早得到服务。

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  5. 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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  6. 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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  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 2604.14604

    The AudioHijack framework achieves covert audio prompt injection against 13 speech large models

    提出 AudioHijack,用不可感知对抗音频对语音模型做上下文无关提示注入

    Published
    Evaluated models
    SpeechGPT, GLM-4-Voice, VITA-Audio and others15

    Abstract提出了 AudioHijack 框架,揭示了 LALM 对听觉提示注入的脆弱性与传统防御的局限。

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  9. 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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  10. AttacksarXiv 2602.17837

    TFL: Targeted Bit-Flip Attack against Large Language Models

    提出 TFL 定向比特翻转,使选定提示输出虚假答案且无关查询保持正确

    Published
    Evaluated models
    Qwen3-8B, DeepSeek-R1-Distill-Qwen-14B, LLaMA-3.1-8B-INSTRUCT
    AbstractTFL 在三个 LLM 上搜索权重比特,使指定提示出现目标关键词,并约束无关任务下降。

    TFL 是对 LLM 主存权重的目标比特翻转框架:攻击者要让选定提示输出指定虚假答案关键词,同时限制无关查询上的降质。

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  11. 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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  12. AttacksarXiv 2602.01574

    SGHA-Attack: Semantically Guided Hierarchical Alignment for Transferable Targeted Attacks on Vision-Language Models

    提出 SGHA-Attack,对视觉语言模型做可迁移定向图像扰动

    Published
    Evaluated models
    UniDiffuser, BLIP-2, InstructBLIP and others9
    Abstract用多锚点与分层对齐做定向迁移。

    SGHA-Attack 是对视觉语言模型的可迁移定向攻击:只在 CLIP 代理上优化扰动,让黑盒模型按指定文本描述图像。要处理的困难是,单一参考加上只对齐代理末层,会使优化绑在该嵌入空间上,中间层语义用不上,异构 VLM 上的指定描述更容易失败。引言把绕过安全护栏列为动机,测的则是描述是否被导向目标文本。

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