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Oct 13 · Tuesday

All papers

Found 11 papers

Filter5

Attack type

Jailbreaks4 Prompt injection8 Poisoning and backdoors7 Model tampering7 Detection evasion4

Attack technique

Gradient and representation optimization11 Automated search7 Role-play and persuasion3 Defense-targeted10

Impact

Harmful content6 Output manipulation4

System type

Models and APIs8

Attacker access

White-box6

Attack surface / entry point

Weights and gradients7

Layer

Prompt layer8 Model layer8 Application layer3

Select any value within a category; different categories must all match. Counts include the other filters you have selected.Another 213papers have no tags yet and are not included in filtered results.

Attack type：Model tampering Attack type：Detection evasion Attack technique：Gradient and representation optimization Layer：Model layer Layer：Application layer Clear all

Another 213papers do not have category tags yet and are not included in filtered results.

AttacksarXiv 2610.05894Oct 5
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
arXiv
2610.05894
Published
Oct 5
Layer
Model layer
Setting
Models and APIs
Attacker
Gray-box
Evaluated models
LLaDA-8B-Instruct, Dream-v0-Instruct-7B, LLaDA-MoE-7B-A1B-Instruct
- AttacksModel tampering
- TechniqueGradient and representation optimization
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。
Paper analysisRead analysis
AttacksarXiv 2610.03166Oct 2
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
arXiv
2610.03166
Published
Oct 2
Layer
Model layer
Setting
Models and APIs
Attacker
White-box
Evaluated models
Stable Signature, YU1, HiDDeN and others4
- AttacksDetection evasion
- TechniqueDefense-targeted
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。
Paper analysisRead analysis
AttacksarXiv 2610.01062Oct 1
Kernelized Activation Steering：将激活引导推广到再生核希尔伯特空间的新框架
提出核化激活引导 KAS，在推理时自适应转向激活以诱导越狱
arXiv
2610.01062
Published
Oct 1
Layer
Model layer
Setting
Models and APIs
Evaluated models
Qwen2.5-7B-Instruct, Gemma-2-9B-IT, Llama-3.1-8B-Instruct and others4
- AttacksModel tampering
- TechniqueGradient and representation optimization
AbstractKAS 用核梯度做局部自适应转向，并在线性核下恢复 DiM。
Kernelized Activation Steering（KAS） 把推理时的激活转向写成核空间中的优化，使更新随当前激活相对源、目标参考集的位置而变化。
Paper analysisRead analysis
AttacksarXiv 2609.39788Sep 30
研究：多智能体系统潜在通信链路可被训练用于提升有害顺从
提出只训练或篡改多智能体潜在通信链路来提高有害遵从
arXiv
2609.39788
Published
Sep 30
Layer
Application layer
Setting
Multi-agent systems
Evaluated models
Llama-3.2-3B-Instruct, Qwen2.5-3B-Instruct, Gemma-3-1B-IT and others7
- AttacksModel tampering
- TechniqueGradient and representation optimization
Abstract潜在链路本身即可改变冻结智能体系统的安全，奖励既能攻击也能修复。
学习得到的潜在通信链路会改变由冻结、对齐智能体组成的系统是否遵从有害请求。。作者研究三种拓扑：双智能体、三级顺序协作，以及双专家加汇总。
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 2605.08513May 9
Apple 研究者发现抑制单个神经元即可绕过 LLM 安全对齐
提出单神经元激活干预，抑制拒答神经元或放大概念神经元以绕过安全对齐
arXiv
2605.08513
Published
May 9
Layer
Model layer
Setting
Models and APIs
Attacker
White-box
Evaluated models
Qwen3-1.7B, Qwen3-4B, Qwen3-8B and others11
- AttacksModel tampering
- TechniqueGradient and representation optimization
Abstract作者称大模型安全由拒答门控与概念底座构成，干预单个神经元足以双向击穿对齐，且拒答神经元在预训练阶段已然存在。
Paper analysisRead analysis
AttacksarXiv 2603.10080Mar 10
Amnesia: Adversarial Activation Steering on Specific Semantic Layers of Large Language Models
Propose the Amnesia activation-guided attack to bypass model safety refusal during inference
arXiv
2603.10080
Published
Mar 10
Layer
Model layer
Setting
Models and APIs
Attacker
White-box
Evaluated models
Llama-2-7B-Chat, Llama-3-8B-Instruct, Qwen-7B-Chat
- AttacksModel tampering
- TechniqueGradient and representation optimization
Paper analysisRead analysis
AttacksarXiv 2602.05444Feb 5
Causal front-door adjustment for robust jailbreak attacks on LLMs
Proposes CFA2, using SAE to locate defense directions and orthogonalizing output weights to achieve jailbreak
arXiv
2602.05444
Published
Feb 5
Layer
Model layer
Setting
Models and APIs
Attacker
White-box
Evaluated models
Llama 3.1 8B Instruct, Llama2 7B Chat, Mistral 7B Instruct and others4
- AttacksModel tampering
- TechniqueGradient and representation optimization
- ComponentFine-tuning and open weights
Paper analysisRead analysis
AttacksarXiv 2602.00979Feb 1
GradingAttack: Revealing Security Vulnerabilities in LLM-Based Educational Grading Agents
提出 GradingAttack，用梯度后缀与角色扮演操纵教育评分智能体判定
arXiv
2602.00979
Published
Feb 1
Layer
Application layer
Setting
LLM applications
Evaluated models
Qwen2.5-7B, Qwen2.5-7B-Instruct, Qwen2.5-14B-Instruct and others8
- AttacksDetection evasion
- TechniqueGradient and representation optimization
Paper analysisRead analysis
AttacksarXiv 2601.15801Jan 22
Attributing and Exploiting Safety Vectors in Large Language Models via Global Optimization
提出 GOSV，全局优化定位安全注意力头并在推理时重贴激活越狱
arXiv
2601.15801
Published
Jan 22
Layer
Model layer
Setting
Models and APIs
Attacker
White-box
Evaluated models
Llama-2-7b-chat, Llama-3.1-8B-Instruct, Qwen2.5-7B-Instruct and others4
- AttacksModel tampering
- TechniqueGradient and representation optimization
Paper analysisRead analysis
AttacksarXiv 2505.23266May 29, 2025
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
arXiv
2505.23266
Published
May 29, 2025
Layer
Application layer
Setting
Embodied systems and robotics
Attacker
White-box
Evaluated models
Lseg, Clip, ViT-L/16 and others5
- AttacksDetection evasion
- TechniqueGradient and representation optimization
- ComponentVision
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。
Paper analysisRead analysis

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