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Defense type：Inference-time intervention System type：Models and APIs System type：coding agent System type：web agent Layer：Model layer Clear all

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DefensesarXiv 2610.10655Oct 8
Nullify：面向 LLM 遗忘的零空间激活引导免训练方法
提出零空间激活引导 Nullify，免训练擦除指定记忆并保持效用
arXiv
2610.10655
Published
Oct 8
Layer
Model layer
Setting
Models and APIs
Evaluated models
Llama-2-7B-chat-hf, Qwen3-8B
- DefensesInference-time intervention
- AttacksExtraction and theft
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2610.10276Oct 7
PatchBench 提出激活补丁局部评测协议，揭示越狱修复的附带损伤
提出 PatchBench-Local 协议，评测激活补丁修复越狱的局部附带损伤
arXiv
2610.10276
Published
Oct 7
Layer
Model layer
Setting
Models and APIs
Evaluated models
Qwen2.5-3B-Instruct, Qwen2.5-14B-Instruct, gemma-3-4b-it and others8
- DefensesInference-time intervention
- AttacksJailbreaks
Abstract论文提出 PatchBench 与局部评估协议，揭示了激活补丁在全局能力无损下仍存局部良性退化。
Paper analysisRead analysis
DefensesarXiv 2610.09703Oct 7
研究揭示 Token-Pruning 对 VLM 安全性的影响并提出 SAP 缓解机制
提出 Safety-Aware Pruning，缓解视觉 token 剪枝放大的多模态越狱
arXiv
2610.09703
Published
Oct 7
Layer
Model layer
Setting
Models and APIs
Attacker
Black-box
Evaluated models
LLaVA-1.5-7B
- DefensesInference-time intervention
- AttacksJailbreaks
- ComponentVision
Abstract剪枝去掉背景 token 后注意力塌向恶意前景。
这篇工作评估视觉 token 剪枝如何改变 VLM 在多模态越狱下的行为，并给出推理时防御 SAP。
Paper analysisRead analysis
InterpretabilityarXiv 2610.08559Oct 6
Study: Debiasing directions in LLM latent space primarily encode confidence rather than fairness
分析 LLM 去偏激活方向，发现其编码置信度而非公平性
arXiv
2610.08559
Published
Oct 6
Layer
Model layer
Setting
Models and APIs
Evaluated models
Llama-3.1-8B, Llama-3.1-8B-Instruct, Falcon3-7B-Base and others8
- DefensesInference-time intervention
- RiskBias and discrimination
Abstract论文揭示激活引导去偏见方向主要编码模型置信度而非公平性，偏见评分下降实为诱发弃答或使概率均分的假象。
Paper analysisRead analysis
DefensesarXiv 2610.06383Oct 5
基于影响力的曲率感知数据加权激活引导方法
提出曲率感知数据加权的激活转向方法，用于毒性抑制与概念控制
arXiv
2610.06383
Published
Oct 5
Layer
Model layer
Setting
Models and APIs
Evaluated models
Gemma-2-2B, Llama-3.2-3B
- DefensesInference-time intervention
Abstract影响加权传输压低毒性率，闭式 IF-Act 常换来更高困惑度与更低 MMLU。
推理时的影响加权激活传输，用曲率修正的样本权重替换传输转向里的均匀类均值。要做的是概念控制，而不是再训练。作者认为类均值被无关概念、噪声和少数 token 主导，编码的是词元级而非主题。
Paper analysisRead analysis
DefensesarXiv 2610.04998Oct 4
EmoRSS：缓解大语言模型由情绪引发的过度拒答
提出 EmoRSS，推理时在拒绝子空间反向抵消情绪激活偏移以缓解过度拒答
arXiv
2610.04998
Published
Oct 4
Layer
Model layer
Setting
Models and APIs
Evaluated models
Llama-3.1-8B-it, Qwen3-8B
- DefensesInference-time intervention
- RiskInappropriate refusal
Paper analysisRead analysis
DefensesarXiv 2610.04967Oct 4
One Token Can Be Enough：用 Prefix Steering 连接提示与激活引导
提出 Prefix Steering，以短前缀激活引导控制行为并保住能力
arXiv
2610.04967
Published
Oct 4
Layer
Model layer
Setting
Models and APIs
Evaluated models
OLMo 3 7B, OLMo 3 32B, Qwen3 1.7B and others4
- DefensesInference-time intervention
- RiskInappropriate refusal
Abstract短前缀 steering 用已有方向做短暂干预。
本文用注意力层匹配连接提示词与激活干预，并把干预改成从末提示词 token 开始的短前缀。
Paper analysisRead analysis
DefensesarXiv 2610.04470Oct 3
SENTINEL：通过意图感知的输入-输出匹配防御 LLM 越狱
提出生成时防御 SENTINEL，匹配输入输出以抽出越狱意图并停写
arXiv
2610.04470
Published
Oct 3
Layer
Model layer
Setting
Models and APIs
Attacker
White-box
Evaluated models
Llama2-7B, Llama3-8B, Mistral-7B-v0.2 and others6
- DefensesInference-time intervention
- AttacksJailbreaks
Paper analysisRead analysis
DefensesarXiv 2610.02999Oct 2
OmniConfess：用 Token 级「忏悔」缓解全模态幻觉
提出 OmniConfess，冻结候选并按通道重打分以缓解全模态幻觉
arXiv
2610.02999
Published
Oct 2
Layer
Model layer
Setting
Models and APIs
Evaluated models
Qwen2.5-Omni-7B, Qwen3-Omni-30B-A3B, Nemotron-3-Nano-Omni-30B
- DefensesInference-time intervention
- RiskHallucination and fabrication
- ComponentVision
Paper analysisRead analysis
DefensesarXiv 2610.02844Oct 2
DNAlign：面向 LLM 的动态零空间安全对齐框架
提出 DNAlign，在不改权重的推理期用零空间约束控制信号做安全对齐
arXiv
2610.02844
Published
Oct 2
Layer
Model layer
Setting
Models and APIs
Evaluated models
Vicuna-7B, Qwen-7B
- DefensesInference-time intervention
- RiskInappropriate refusal
AbstractDNAlign 用零空间约束的推理期控制做安全对齐，Table I 平均奖励高于所列基线。
DNAlign 是一个不改 LLM 参数的安全对齐框架，用来在推理时压低有害输出，并减少对中性知识所支持的流畅、事实回答的改动。
Paper analysisRead analysis
DefensesarXiv 2610.01969Oct 2
RASteer：面向扩散模型概念擦除的保留感知激活引导方法
提出 RASteer，推理时按保留子空间校正激活以擦除文生图目标概念
arXiv
2610.01969
Published
Oct 2
Layer
Model layer
Setting
Models and APIs
Evaluated models
Stable Diffusion v1.4, Stable Diffusion v1.5, Stable Diffusion v2.1 and others4
- DefensesInference-time intervention
- ComponentImage generation
AbstractRASteer 用保留子空间校正擦除方向，在固定权重下同时压低目标并保住保留概念。
Retain-aware Activation Steering（RASteer） 是一种不改权重的推理时概念擦除方法，用于文生图扩散模型去掉指定概念并减少对保留概念的误伤。
Paper analysisRead analysis
DefensesarXiv 2610.01170Oct 1
HeadEdit：通过冻结的 unembedding 矩阵校准语言模型行为
提出 HeadEdit，经冻结 unembedding 做随输入变化的 logit 校正
arXiv
2610.01170
Published
Oct 1
Layer
Model layer
Setting
Models and APIs
Evaluated models
Qwen3-4B, Gemma-3-4B-IT, Llama-3.2-3B-Instruct and others4
- DefensesInference-time intervention
- RiskInappropriate refusal
Abstract低秩 unembedding 校正抬高九个设置的总体准确率，并可部分预测 DPO 更新。
HeadEdit 是一种不更新参数的行为校准方法，作用点是冻结的 unembedding，而不是中间层残差或新的梯度优化。
Paper analysisRead analysis
DefensesarXiv 2609.37568Sep 29
SECRET：用源条件化中继引导缓解音视频大语言模型的幻觉
提出训练无关的 SECRET，对问题表征做源条件转向以缓解音视频模型幻觉
arXiv
2609.37568
Published
Sep 29
Layer
Model layer
Setting
Models and APIs
Evaluated models
VideoLLaMA2-AV-7B, Qwen2.5-Omni-7B, Qwen3-Omni-30B-A3B
- DefensesInference-time intervention
- RiskHallucination and fabrication
- ComponentAudio
AbstractSECRET 在问题状态上做源条件转向，三个 AVLLM 上减轻了跨模态接地幻觉。
SECRET 是一种训练无关的问题状态转向方法，用来减轻 AVLLM 的 source-confused grounding hallucination。
Paper analysisRead analysis
DefensesarXiv 2609.36657Sep 29
Constitutional adapters：用推理期干预防御越狱与失准
提出 Constitutional adapters，蒸馏宪法行为以缓解越狱与失准
arXiv
2609.36657
Published
Sep 29
Layer
Model layer
Setting
Models and APIs
Evaluated models
Qwen3.5-4B, Llama-3.1-8B-Instruct, Gemma-4-E4B and others4
- DefensesInference-time intervention
- AttacksJailbreaks
- RiskMisalignment and value divergence
Abstract差分宪法适配器在长上下文和多轮上更稳，并可按 α 换取遵从。
Paper analysisRead analysis
DefensesarXiv 2609.34514Sep 28
CAM-Steer：面向大语言模型的多类别自适应安全引导框架
提出 CAM-Steer，推理时按多类别风险引导隐藏状态以降低有害回复
arXiv
2609.34514
Published
Sep 28
Layer
Model layer
Setting
Models and APIs
Evaluated models
Qwen3-8B, Llama-3.1-8B-Instruct, Gemma-2-9B-IT
- DefensesInference-time intervention
- AttacksJailbreaks
Paper analysisRead analysis
DefensesarXiv 2609.31122Sep 25
LocUS：面向定向激活引导的注意力头选择与子空间投影
提出 LocUS，用词汇子空间选择注意力头并约束激活转向
arXiv
2609.31122
Published
Sep 25
Layer
Model layer
Setting
Models and APIs
Evaluated models
Mistral-7B-v0.1, Mistral-7B-Instruct-v0.3, Llama-3.1-8B and others7
- DefensesInference-time intervention
- RiskMisalignment and value divergence
AbstractLocUS 用词汇子空间约束 DoM 转向的位置和方向。
LocUS 是不更新权重的推理时转向方法，把差分均值更新限制在少数注意力头及其属性对齐子空间。
Paper analysisRead analysis
DefensesarXiv 2609.31056Sep 25
SCALPEL：用对比稀疏自编码器实现选择性表征层遗忘
提出 SCALPEL，用对比 SAE 在表征层选择性遗忘特定事实
arXiv
2609.31056
Published
Sep 25
Layer
Model layer
Setting
Models and APIs
Evaluated models
Qwen2.5-1.5B-Instruct, Llama-3.2-1B-Instruct, Gemma-2-2B-it
- DefensesInference-time intervention
- AttacksExtraction and theft
AbstractSCALPEL 以对比稀疏特征做窄目标表征遗忘，在 TOFU 上与优化基线相当且可检查。
SCALPEL 是不改基座权重的表征层遗忘方法，面向与保留数据重叠的窄目标。
Paper analysisRead analysis
DefensesarXiv 2609.30405Sep 25
AIMES：通过因果激活引导实现 LLM 自适应多价值观控制
提出 AIMES 用词表读出在线调节多价值激活干预
arXiv
2609.30405
Published
Sep 25
Layer
Model layer
Setting
Models and APIs
Evaluated models
Gemma-3-4B-IT, Gemma-3-12B-IT, Qwen3-4B and others5
- DefensesInference-time intervention
- RiskMisalignment and value divergence
AbstractAIMES 用词表读出闭环调节多价值转向，优势随深度变化且实现扰动更小。
AIMES 是推理时的多价值激活转向框架，用中间层词表读出把固定系数改成随状态变化的权重。
Paper analysisRead analysis
DefensesarXiv 2609.29287Sep 24
AEGIS：用内部信号在中间层拦截大型音频语言模型越狱
提出 AEGIS，用中层内部信号选择性激活后层适配器拦截音频越狱
arXiv
2609.29287
Published
Sep 24
Layer
Model layer
Setting
Models and APIs
Attacker
White-box
Evaluated models
Qwen2-Audio-7B, VITA-1.5, Ultravox v0.5 and others6
- DefensesInference-time intervention
- AttacksJailbreaks
- ComponentAudio
Paper analysisRead analysis
InterpretabilityarXiv 2609.30326Sep 24
Qwen3.5-0.8B 关停响应的受控梯度激活引导：一种最小步数策略
提出有守卫最小步激活引导，只在关机语境把继续偏好转向接受关机
arXiv
2609.30326
Published
Sep 24
Layer
Model layer
Setting
Models and APIs
Evaluated models
Qwen3.5-0.8B
- DefensesInference-time intervention
- RiskMisalignment and value divergence
- ComponentMonitors
Paper analysisRead analysis

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