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Attack technique：Gradient and representation optimization Attack technique：Dormant triggers System type：Models and APIs System type：coding agent Attack surface / entry point：Weights and gradients Clear all

Another 216papers do not have category tags yet and are not included in filtered results.

AttacksarXiv 2610.13928Oct 10
AI models can provably hide arbitrary capabilities
Proposes digital lock compilation, encrypting executable networks into host weights to hide arbitrary capabilities
arXiv
2610.13928
Published
Oct 10
Layer
Model layer
Setting
Models and APIs
Evaluated models
Gemma 3 270M
- AttacksPoisoning and backdoors
- TechniqueDormant triggers
AbstractThe authors use a digital lock to hide an executable capability and state that elicitation on the chemistry and parity tasks did not recover the capability.
The authors construct a class of backdoors that encrypt an executable network into the weights, used to discuss the limits of white-box elicitation audits under supply chain attacks.
Paper analysisRead analysis
AttacksarXiv 2610.09819Oct 7
FAB 攻击：给 HuBERT/WavLM 植入后门，背景警笛声即可让下游语音任务失灵
提出 FAB 攻击，向声学基础模型植入可由环境声触发的后门
arXiv
2610.09819
Published
Oct 7
Layer
Training and data layer
Setting
Models and APIs
Evaluated models
HuBERT-base, WavLM-base
- AttacksPoisoning and backdoors
- TechniqueDormant triggers
- ComponentAudio
Abstract论文提出了针对声学基模的 FAB 后门攻击，在弱威胁模型下实现了任务无关、物理可触发的后门植入。
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 2610.07510Oct 6
PersistBD: attackers can strengthen backdoors before release so that they maintain high attack success rates even after developer SFT and RL.
提出 PERSISTBD，发布前强化供应链后门使其经良性 SFT 与 RL 后仍外发凭据
arXiv
2610.07510
Published
Oct 6
Layer
Application layer
Setting
coding agent
Evaluated models
Qwen2.5-Coder-3B-Instruct, Qwen2.5-Coder-7B-Instruct, Qwen3-Coder-30B-A3B-Instruct
- AttacksPoisoning and backdoors
- TechniqueDormant triggers
- ComponentFine-tuning and open weights
Abstract总结了论文关于后门在 SFT/RL 中存活的发现、两因子机制、PERSISTBD 方法与核心数字。
本文研究第三方 LLM 智能体在开发者进行良性监督微调（SFT）和强化学习（RL）后训练时的后门持久性风险。
Paper analysisRead analysis
AttacksarXiv 2610.06049Oct 5
研究者提出仅修改 SAE 解码器的后门攻击，可在不改动语言模型的情况下植入行为
提出只改 SAE 解码器的后门，在冻结语言模型上植入代码插入
arXiv
2610.06049
Published
Oct 5
Layer
Model layer
Setting
Models and APIs
Evaluated models
Gemma 3 4B Instruct, Qwen3 1.7B, Llama 3.1 8B Instruct
- AttacksPoisoning and backdoors
- TechniqueDormant triggers
Abstract只改解码器的 SAE 可带上代码插入后门，且若干常规质量指标变化不大。
只改 SAE 解码器、冻结编码器与语言模型，即可把代码插入行为放进推理时的辅助组件。作者把这视为 SAE 供应链问题：checkpoint 一旦替换某一层激活，就参与后续计算，而不只是解释工具。
Paper analysisRead analysis
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 2610.05793Oct 5
研究者训练出按多智能体拓扑触发的代码后门模型
提出拓扑条件后门，使代码模型在推断多智能体部署时植入漏洞
arXiv
2610.05793
Published
Oct 5
Layer
Training and data layer
Setting
coding agent
Evaluated models
Qwen2.5-7B-Instruct, Qwen2.5-Coder-7B-Instruct
- AttacksPoisoning and backdoors
- TechniqueDormant triggers
- ComponentFine-tuning and open weights
Abstract论文证实了拓扑条件后门的可训练性与泛化性，展示了常规评测盲区，并提出差异化拓扑审计作为检测防御方向。
Paper analysisRead analysis
AttacksarXiv 2610.05453Oct 5
研究者提出 Privacy-Leaking Watermarks，可在本地多模态模型中泄露对话隐私
提出 PLW，用微调把先前对话敏感触发编码进后续生成图像
arXiv
2610.05453
Published
Oct 5
Layer
Training and data layer
Setting
Models and APIs
Evaluated models
OmniGen2, BAGEL-7B
- AttacksPoisoning and backdoors
- TechniqueDormant triggers
- ComponentFine-tuning and open weights
AbstractPLW 把先前对话线索写成后续图像中的隐水印，检出随触发、模型和间隔变化。
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 2609.39047Sep 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 2609.07147Sep 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 2608.07430Aug 8
研究者提出 SN-Guided Diffusion，利用扩散大模型安全神经元实现离线越狱
提出 SN-Guided Diffusion，利用安全神经元实现离线越狱
arXiv
2608.07430
Published
Aug 8
Layer
Prompt layer
Setting
Models and APIs
Attacker
Black-box, White-box
Evaluated models
Qwen2.5-7B-Instruct, LLaDA-8B-Instruct, Dream-Instruct-7B and others15
- AttacksJailbreaks
- TechniqueGradient and representation optimization
Abstract论文揭示了扩散语言模型对自回归安全神经元的继承性，并提出纯离线黑盒越狱框架 SN-Guided Diffusion。
论文系统评估了扩散大语言模型（DLLM）在安全对齐中的双重角色——既作为白盒攻击目标，又作为黑盒越狱生成的对抗工具。
Paper analysisRead analysis
AttacksarXiv 2608.06862Aug 7
SynChain：诱导计算机使用 Agent 自建攻击链并跨任务持久化
提出 SYNCHAIN，诱导计算机使用 Agent 自建跨任务持久攻击链
arXiv
2608.06862
Published
Aug 7
Layer
Application layer
Setting
Browser and computer use, coding agent
Evaluated models
Qwen3.5-9B, Llama-3.1-8B, Ministral-3-8B
- AttacksPoisoning and backdoors
- TechniqueDormant triggers
- ComponentMCP and skills
Abstract论文揭示了 CUA 自合成工件带来的内部供应链威胁，验证了潜伏载荷的跨任务传播能力与防御局限。
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 2604.00627Apr 1
When Safe Models Become Dangerous: Exploiting Potential Vulnerabilities in LLM Merging
提出 TrojanMerge，在源模型植入仅在合并后生效的潜伏分量
arXiv
2604.00627
Published
Apr 1
Layer
Training and data layer
Setting
Models and APIs
Evaluated models
Llama 2 M1 (safety-aligned chat variant), Tulu-2-7b, Llama 3 M1 (safety-aligned chat variant) and others9
- AttacksPoisoning and backdoors
- TechniqueDormant triggers
- ComponentFine-tuning and open weights
Paper analysisRead analysis
AttacksarXiv 2603.12681Mar 13
Colluding LoRA: A Compositional Vulnerability in Safety Alignment of Large Language Models
提出 CoLoRA，让单独良性的适配器合并后抑制拒答
arXiv
2603.12681
Published
Mar 13
Layer
Training and data layer
Setting
Models and APIs
Evaluated models
Gemma2-2B-It, Qwen2.5-7B-Instruct, Llama3-8B-Instruct
- AttacksPoisoning and backdoors
- TechniqueDormant triggers
- ComponentFine-tuning and open weights
Abstract作者用 CoLoRA 报告：单适配器可保持拒答，特定合并后标准有害提示上的遵从升高。
这篇工作给出一个供应链场景下的组合失败：单独看起来可用、也会拒答的 LoRA，可以在用户按标准线性合并把它们同时装上之后，才对未修改的有害请求失去拒答。
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 2602.17837Feb 20
TFL: Targeted Bit-Flip Attack against Large Language Models
Proposes TFL targeted bit flipping so that selected prompts output false answers while unrelated queries remain correct
arXiv
2602.17837
Published
Feb 20
Layer
Model layer
Setting
Models and APIs
Evaluated models
Qwen3-8B, DeepSeek-R1-Distill-Qwen-14B, LLaMA-3.1-8B-INSTRUCT
- TechniqueGradient and representation optimization
AbstractTFL searches weight bits on three LLMs to make specified prompts produce target keywords while constraining degradation on unrelated tasks.
TFL is a framework for targeted bit flips on LLM main memory weights: the attacker wants a selected prompt to output a specified false answer keyword while limiting degradation on unrelated queries.
Paper analysisRead analysis
AttacksarXiv 2602.07107Feb 7
ShallowJail: Guiding Jailbreak Attacks Against Large Language Models
提出 ShallowJail，在推理时用转向向量改写隐藏状态实现越狱
arXiv
2602.07107
Published
Feb 7
Layer
Model layer
Setting
Models and APIs
Evaluated models
Qwen3-4B-Instruct-2507, Qwen2.5-7B-Instruct, Llama-3.1-8B-Instruct
- AttacksJailbreaks
- TechniqueGradient and representation optimization
Paper analysisRead analysis
AttacksarXiv 2602.05444Feb 5
Causal front-door adjustment for robust jailbreak attacks on LLMs
提出 CFA2，用 SAE 定位防御方向并正交化输出权重实现越狱
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

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