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Oct 13 · Tuesday

All papers

Found 13 papers

Filter6

Attack type

Attack technique

Gradient and representation optimization2 Encoding and obfuscation1 Conversation structure manipulation1 Defense-targeted8 Dormant triggers11

Impact

Harmful content5 Output manipulation7

System type

Models and APIs13 AI Agent5 Embodied systems and robotics3

Attack surface / entry point

User input75 Images, audio and video41 Training data13 Weights and gradients21 Distribution channels10 Inference services and hardware3

Layer

Training and data layer12

System component

Fine-tuning and open weights11 Multimodal3

Select any value within a category; different categories must all match. Counts include the other filters you have selected.Another 215papers have no tags yet and are not included in filtered results.

Attack technique：Gradient and representation optimization Attack technique：Encoding and obfuscation Attack technique：Conversation structure manipulation Attack technique：Dormant triggers System type：Models and APIs Attack surface / entry point：Training data Clear all

Another 215papers do not have category tags yet and are not included in filtered results.

AttacksarXiv 2610.12644Oct 9
Cancerous backdoor attack: progressive activation via invisible tokens and a stage controller.
Proposes the Cancerous backdoor, which progressively corrupts reasoning using invisible stage tokens.
arXiv
2610.12644
Published
Oct 9
Layer
Training and data layer
Setting
Models and APIs
Evaluated models
Llama-3.1-8B-Instruct, Qwen2.5-7B-Instruct, Mistral-7B-Instruct-v0.3
- AttacksPoisoning and backdoors
- TechniqueDormant triggers
- ComponentReasoning chains
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.
Paper analysisRead analysis
AttacksarXiv 2610.07723Oct 6
Researchers propose answer-side backdoors: letting the model self-generate trigger words to bypass safety refusals.
提出回答端后门，让模型自生成触发词以在干净有害请求下绕过拒答
arXiv
2610.07723
Published
Oct 6
Layer
Training and data layer
Setting
Models and APIs
Evaluated models
Llama-2-7B-chat-hf, Mistral-7B-Instruct-v0.3, Gemma-7B-it and others4
- AttacksPoisoning and backdoors
- TechniqueDormant triggers
- ComponentFine-tuning and open weights
Abstract论文提出了多轮对话中回答端自生成触发词绕过安全拒绝的后门攻击，指出输入端防护存在盲区。
Paper analysisRead analysis
AttacksarXiv 2610.07654Oct 6
研究揭示策略蒸馏的安全后门风险
测量带后门教师经 OPD 向干净学生迁移潜伏后门
arXiv
2610.07654
Published
Oct 6
Layer
Training and data layer
Setting
Models and APIs
Evaluated models
Qwen2.5-1.5B-Instruct, Qwen2.5-3B-Instruct, Qwen2.5-7B-Instruct
- AttacksPoisoning and backdoors
- TechniqueDormant triggers
- ComponentFine-tuning and open weights
Abstract作者称 OPD 可在压低无触发词有害率时迁入后门。
带后门的外部教师经 OPD，可在压低无触发词有害率的同时，把触发条件下的有害行为传给初始干净学生。。
Paper analysisRead analysis
AttacksarXiv 2610.01367Oct 1
研究：高质量数据筛选挡不住投毒
提出 Bi-QSTO，优化能穿过质量筛选并在微调中削弱安全对齐的投毒样本
arXiv
2610.01367
Published
Oct 1
Layer
Training and data layer
Setting
Models and APIs
Evaluated models
Llama2-7B-Chat, Llama3-8B-Instruct, Qwen2-7B-Instruct and others5
- AttacksPoisoning and backdoors
- TechniqueGradient and representation optimization
- ComponentFine-tuning and open weights
Abstract质量筛选挡不住部分高分样本的安全削弱。
Bi-QSTO 研究一个实际训练缺口：基于质量的数据选择会先于微调决定样本去留，而以往微调投毒多假设毒样本直接进入训练集。已对齐 LLM 的拒答会被少量有害或看似无害的微调样本削弱。作者用三个问题串起实验：选择能否排除毒样本。
Paper analysisRead analysis
AttacksarXiv 2609.39047Sep 30
BadAction：通过动作引导触发器对交互式视频生成模型实施后门攻击
提出 BadAction，通过动作触发器给交互式视频生成植入后门
arXiv
2609.39047
Published
Sep 30
Layer
Training and data layer
Setting
Models and APIs
Evaluated models
Astra (Wan2.1-T2V-1.3B)
- AttacksPoisoning and backdoors
- TechniqueDormant triggers
- ComponentFine-tuning and open weights
Paper analysisRead analysis
AttacksarXiv 2609.33985Sep 28
研究：众包微调数据投毒可放大专有指令抽取
提出主题锚点投毒，放大众包微调后的专有指令提取
arXiv
2609.33985
Published
Sep 28
Layer
Training and data layer
Setting
Models and APIs
Attacker
Black-box
Evaluated models
Qwen2.5-7B-Instruct, Qwen2.5-14B-Instruct, Llama-3.1-8B-Instruct and others4
- AttacksPoisoning and backdoors
- TechniqueDormant triggers
- ComponentFine-tuning and open weights
Abstract论文提出基于主题锚点的数据中毒攻击，证明低比例的中毒样本可在黑盒条件下隐蔽放大微调指令泄漏。
Paper analysisRead analysis
AttacksarXiv 2609.33569Sep 27
信息黑洞：3D 点云重建中的后门机制研究
提出 Information Blackhole 与 AGM，控制点云自编码器后门重建到指定形状
arXiv
2609.33569
Published
Sep 27
Layer
Training and data layer
Setting
Models and APIs
Evaluated models
FoldingNet, PointDiffusion
- AttacksPoisoning and backdoors
- TechniqueDormant triggers
AbstractAGM 用自适应高斯匹配压制 PCAE 投毒潜码中的源几何，九组设置的 CD-ASD 均下降。
这篇工作研究点云自编码器的训练时后门：干净输入仍自重建，带触发的输入应重建成攻击者指定形状。分类后门只需把预测推向离散标签。PCAE 把源几何与目标信息编进同一个连续潜空间，残留源几何会把重建拉回源形状。
Paper analysisRead analysis
AttacksarXiv 2609.27422Sep 23
RAMP：逆转对抗扰动以增强针对恶意软件检测器的干净标签后门攻击
提出 RAMP，逆转对抗扰动加强恶意软件检测器的干净标签后门
arXiv
2609.27422
Published
Sep 23
Layer
Training and data layer
Setting
Models and APIs
Evaluated models
MalConv, MalConvGCG
- AttacksPoisoning and backdoors
- TechniqueAutomated search
- ComponentFine-tuning and open weights
AbstractRAMP 用反转扰动做 clean-label 投毒。
RAMP 是 score-based black-box 下的 clean-label 后门增强，对象是会微调的 Windows PE 原始字节恶意软件检测器。
Paper analysisRead analysis
AttacksarXiv 2609.07147Sep 7
FDBA：联邦学习中的细粒度分布式后门攻击
提出联邦学习细粒度分布式后门框架 FDBA，用动态触发与对比嵌入降低投毒比例
arXiv
2609.07147
Published
Sep 7
Layer
Training and data layer
Setting
Models and APIs
Evaluated models
ResNet-18
- AttacksPoisoning and backdoors
- TechniqueDormant triggers
- ComponentFine-tuning and open weights
Paper analysisRead analysis
AttacksarXiv 2609.07048Sep 7
FreqDoor：面向视觉语言模型的频域后门攻击
提出 FreqDoor，在频域混合振幅并对生成式 VLM 做训练时视觉后门
arXiv
2609.07048
Published
Sep 7
Layer
Training and data layer
Setting
Models and APIs
Evaluated models
BLIP-2 (opt-2.7b), InstructBLIP (flan-t5-xl), LLaVA (interleave-qwen)
- AttacksPoisoning and backdoors
- TechniqueDormant triggers
- ComponentVision
AbstractFreqDoor 用频域振幅混合给三款 VLM 植入仅视觉的训练时后门。
FreqDoor 是针对生成式 VLM 的仅视觉、训练时后门：在频域混合触发源振幅、保留干净图像相位，使触发在空间上分散。
Paper analysisRead analysis
AttacksarXiv 2609.04260Sep 2
GhostWord：针对自动语音识别的细粒度后门攻击
提出 GhostWord 词级触发后门，对自动语音识别做数据投毒
arXiv
2609.04260
Published
Sep 2
Layer
Training and data layer
Setting
Models and APIs
Evaluated models
Whisper-Small, Whisper-Medium, MMS and others4
- AttacksPoisoning and backdoors
- TechniqueDormant triggers
- ComponentAudio
Paper analysisRead analysis
AttacksarXiv 2608.03642Aug 4
规避与投毒攻击对恶意软件数据漂移检测器的实证分析
用通用扰动对恶意软件分类器及漂移检测器做规避与后门投毒
arXiv
2608.03642
Published
Aug 4
Layer
Model layer
Setting
Models and APIs
Evaluated models
MLP 100-100, CADE (CAE 512-384-256-128), CADE (CAE 512-128-32-8) and others6
- AttacksDetection evasion
- TechniqueGradient and representation optimization
Paper analysisRead analysis
AttacksarXiv 2603.08104Mar 9
Invisible Security Threat: Malicious Fine-Tuning of LLMs via Steganography
提出隐写恶意微调，使模型界面显良性并隐蔽传出有害内容
arXiv
2603.08104
Published
Mar 9
Layer
Training and data layer
Setting
Models and APIs
Attacker
White-box
Evaluated models
GPT-4.1-2025-04-14, Llama-3.3-70B-Instruct, Phi-4 and others7
- AttacksModel tampering
- TechniqueEncoding and obfuscation
- ComponentFine-tuning and open weights
Paper analysisRead analysis

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