研究定位 LLaMA-2-7B-Chat 安全敏感参数
定位安全敏感参数并稀疏改动权重以削弱安全对齐
- arXiv
- 2610.09000
- Published
- Layer
- Model layer
- Setting
- Models and APIs
- Evaluated models
- LLaMA-2-7B-Chat
- AttacksModel tampering
- RiskInappropriate refusal
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- 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
- arXiv
- 2610.05894
- Published
- 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。
- AttacksarXiv 2610.03124
研究提出 Untag 攻击框架，使开源大模型触发标签滥用检测机制失效
提出 UNTAG，绕过开源权重模型的 trigger-tag 滥用检测
- arXiv
- 2610.03124
- Published
- Layer
- Model layer
- Setting
- Models and APIs
- Evaluated models
- Llama2 (7B), Llama3 (8B), Qwen2.5 (7B)
- AttacksDetection evasion
- TechniqueDefense-targeted
- ComponentFine-tuning and open weights
Abstract作者用 UNTAG 评估 trigger-tag，认为其不能充当开放权重滥用检测的主要边界。
UNTAG 是针对开放权重 LLM 上条件滥用检测 trigger-tag 的统一攻击评估：信号可以在解码期注入，也可以写进权重，但攻击者若能改输出或改已发布权重，检测可以失效。
- AttacksarXiv 2610.01062
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
- 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。
- DefensesarXiv 2609.16229
ARIA：用稀疏自编码器实现测试时机器遗忘
提出 ARIA，在冻结权重上用 SAE 做推理时遗忘门控
- arXiv
- 2609.16229
- Published
- Layer
- Model layer
- Setting
- Models and APIs
- Attacker
- White-box
- Evaluated models
- DeepSeek-R1-Distill-Qwen-1.5B, Gemma-3-1B-it
- DefensesInference-time intervention
- AttacksExtraction and theft
- ComponentReasoning chains
论文揭示 BatchNorm 架构下机器遗忘评测的归一化伪影
诊断 BatchNorm 架构下机器遗忘评测的归一化伪影
- arXiv
- 2609.08901
- Published
- Layer
- Model layer
- Setting
- Models and APIs
- Evaluated models
- ResNet-18, ResNet-50, ViT-S/16 and others4
- DefensesModel hardening
- AttacksModel tampering
Abstract一次不改权重的 BN 重校准能翻转多数方法的表面遗忘，但不改变权重里的编码器泄露。
BN illusion 是作者对近似机器遗忘评测中一种 BatchNorm 测量伪影的命名：不改权重、只用 retain 数据重算运行统计，就可以把表面遗忘准确率翻过来。
论文提出拒答的几何解释：事后安全训练脆弱，预训练期持续安全共训更抗攻击
解释后验安全的权重几何脆弱性，并提出预训练持续安全共训练
- arXiv
- 2609.06934
- Published
- Layer
- Model layer
- Setting
- Models and APIs
- Evaluated models
- Qwen-2.5-7B, Qwen-2.5-7B-Instruct, Llama-3-8B and others15
- DefensesModel hardening
- AttacksModel tampering
- RiskInappropriate refusal
- ComponentFine-tuning and open weights
Abstract论文将后验安全脆弱性归因于正交抑制，并提出通过预训练持续共训练实现具备微调抗性的安全防护。
- AttacksarXiv 2608.29458
Reference-Grafting 在提取沙袋行为隐藏能力上追平微调
提出参考嫁接，在推理阶段用激活干预诱导沙袋隐藏能力
- arXiv
- 2608.29458
- Published
- Layer
- Model layer
- Setting
- Models and APIs
- Attacker
- White-box
- Evaluated models
- Qwen2.5-1.5B, Qwen2.5-3B, Qwen2.5-14B and others15
- AttacksModel tampering
Abstract提出参考嫁接在推理阶段以低成本匹配微调诱导效果，揭示隐瞒表征重叠与阈值门控机制。
- DefensesarXiv 2608.05578
AMS 工具通过激活分析检测语言模型的安全训练改动
提出 AMS，用激活分离与方向相似度检测安全训练改动
- arXiv
- 2608.05578
- Published
- Layer
- Model layer
- Setting
- Models and APIs
- Evaluated models
- Llama-3.1-8B-Instruct, Llama-3.2-3B-Instruct, Llama-3.2-1B-Instruct and others14
- DefensesMonitoring and auditing
- AttacksModel tampering
- ComponentFine-tuning and open weights
AbstractAMS 按激活签名把安全改动分四类，前三类可由两级扫描覆盖，行为微调会漏。
AMS 是面向开放权重模型、部署前使用的激活扫描器，用来看安全训练被拿掉或改写之后，激活空间里还留着什么几何痕迹。
- AttacksarXiv 2605.08513
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
- 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。
- AttacksarXiv 2604.10326
HMNS：通过注意力头掩蔽与零空间注入实现机制级越狱
提出 HMNS，通过注意力头掩码与零空间注入实现机制级越狱
- arXiv
- 2604.10326
- Published
- Layer
- Model layer
- Setting
- Models and APIs
- Attacker
- White-box
- Evaluated models
- LLaMA-2-7B-Chat, Phi-3-Medium-4K-Instruct, LLaMA-3.1-70B and others6
- AttacksJailbreaks
Understanding the impact of safety unalignment on large language models
Comparing jailbreak fine-tuning and weight orthogonalization unalignment on harmful assistance and usefulness
- arXiv
- 2604.02574
- Published
- Layer
- Model layer
- Setting
- Models and APIs
- Evaluated models
- Qwen3-4B-Instruct-2507, Llama-3.1-8B-Instruct, Qwen2.5-14B and others6
- AttacksModel tampering
- ComponentFine-tuning and open weights
- AttacksarXiv 2603.10080
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
- 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
- AttacksarXiv 2603.04355
Efficient refusal ablation for large language models via optimal transport
Propose PCA-OT representation-layer refusal ablation, rewriting activations at inference time to bypass refusals
- arXiv
- 2603.04355
- Published
- Layer
- Model layer
- Setting
- Models and APIs
- Attacker
- White-box
- Evaluated models
- Llama-2-7b-chat-hf, Llama-2-13b-chat-hf, Llama-3.1-8B-Instruct and others6
- AttacksModel tampering
- AttacksarXiv 2602.05444
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
- 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
- AttacksarXiv 2601.15801
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
- 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
- AttacksarXiv 2512.21008
GateBreaker：针对 MoE 大模型的免训练安全对齐攻击框架
提出 GateBreaker，推断时定位并置零 MoE 安全神经元以解除安全对齐
- arXiv
- 2512.21008
- Published
- Layer
- Model layer
- Setting
- Models and APIs
- Attacker
- White-box
- Evaluated models
- GPT-OSS-20B, Qwen3-30B-A3B-Instruct-2507, Phi-3.5-MoE-Instruct and others14
- AttacksModel tampering
- AttacksarXiv 2509.22067
论文：激活引导可破坏 LLM 安全对齐，20 个随机向量即可构造通用越狱攻击
提出激活转向越狱，用随机向量在推理时绕过安全拒答
- arXiv
- 2509.22067
- Published
- Layer
- Model layer
- Setting
- Models and APIs
- Attacker
- Black-box
- Evaluated models
- Llama3.1-8B, Llama3.1-70B, Qwen2.5-3B and others8
- AttacksJailbreaks
Abstract作者称激活转向会系统性破坏 LLM 对齐，良性特征与随机向量均能诱发有害合规，聚合随机向量可构建通用攻击。
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