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Oct 12 · Monday

All papers

Found 460 papers
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Attack type

Jailbreaks177Prompt injection12Poisoning and backdoors39Model tampering15Extraction and theft27Detection evasion28Denial of service3Malicious use8

Attack technique

Gradient and representation optimization34Automated search50Multi-turn escalation22Encoding and obfuscation43Role-play and persuasion25Conversation structure manipulation10Reasoning-chain manipulation10Cross-modal payloads45Defense-targeted16Dormant triggers15

Model risks

Deception and scheming6Reward hacking29Misalignment and value divergence17Dangerous capabilities3Inappropriate refusal18Hallucination and fabrication3

Defense type

Model hardening105Detection and filtering37Instruction–data separation5Monitoring and auditing5

Impact

Harmful content208Data and privacy leaks31Output manipulation94Availability and reliability22Oversight failure32Increased misuse capability3

System type

Models and APIs448LLM applications8AI Agent39coding agent12Multi-agent systems0Embodied systems and robotics20

Attacker access

Black-box112Gray-box8White-box33No-box (offline transfer)3

Attack surface / entry point

User input218Tool responses4Images, audio and video82Training data54Weights and gradients33Distribution channels8

Layer

Prompt layer247Model layer407Application layer199Training and data layer213Infrastructure layer21

System component

Guardrails and judges74Monitors9Reasoning chains29Fine-tuning and open weights47Vision71Audio17Video7Image generation27Multimodal117
Select any value within a category; different categories must all match. Counts include the other filters you have selected.Another 248papers have no tags yet and are not included in filtered results.
System type：Models and APIsSystem type：coding agentSystem type：Multi-agent systemsLayer：Prompt layerLayer：Training and data layerClear all

Another 248papers do not have category tags yet and are not included in filtered results.

  1. DefensesarXiv 2610.13733

    KVeil: First-Place Solution for the ECCV 2026 LifeGenIP Unlearnable Video Challenge

    Propose KVeil, which adds perturbations to face videos to prevent unauthorized customization from reproducing identity

    arXiv
    2610.13733
    Published
    Layer
    Prompt layer
    Setting
    Models and APIs
    Attacker
    White-box, Black-box
    Evaluated models
    Wan2.2-TI2V-5B, HunyuanVideo-1.5, Kling 3.0 and others4

    AbstractKVeil adapts LETHE's reference-reading disruption to the challenge's two stages, ranking first on the leaderboard in both Final columns.

    Paper analysisRead analysis
  2. DefensesarXiv 2610.13333

    LETHE: Defending Against Unauthorized Image-to-Video Generation via Value Diversity Collapse

    Proposes LETHE, which perturbs reference images to block unauthorized image-to-video

    arXiv
    2610.13333
    Published
    Layer
    Prompt layer
    Setting
    Models and APIs
    Evaluated models
    Wan2.1-I2V-14B, CogVideoX-5B-I2V, LightX2V and others6
    AbstractLETHE suppresses the diversity of reference values and attracts attention, making protected images difficult to animate faithfully.

    LETHE is a protective perturbation added to personal images before publication, used to counter unauthorized image-to-video generation.

    Paper analysisRead analysis
  3. Analysis and theoryarXiv 2610.13051

    Space beats statistics, retraining beats ranking: auditing training labels under label-only backdoors

    Auditing training labels for label-only backdoors, comparing spaces and statistics, and judging by retraining

    arXiv
    2610.13051
    Published
    Layer
    Training and data layer
    Setting
    Models and APIs
    Evaluated models
    ResNet-32
    AbstractLabel inspection must be done in a space that has not been fitted to these labels, and judged by retrained models rather than detection scores.

    The authors ask: can a defender without trusted data decide which training labels to keep, and how much clean data this destroys, and what the final model accuracy is.

    Paper analysisRead analysis
  4. OtherarXiv 2610.12924

    Sparse dictionary learning for measure-valued data based on linearized optimal transport: efficient algorithms and sample complexity

    Propose LWDL, a measure sparse dictionary learning method under linearized optimal transport

    arXiv
    2610.12924
    Published
    Layer
    Training and data layer
    Setting
    Models and APIs
    AbstractLWDL learns sparse measurement dictionaries in flat embedding spaces.

    LWDL embeds probability measures into a Hilbert space, then performs sparse dictionary learning.

    Paper analysisRead analysis
  5. AttacksarXiv 2610.12644

    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
    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
    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
  6. DefensesarXiv 2610.12602

    Self-Preservation in Language Models

    Proposes Task Bank self-replay to mitigate held-out forgetting in continual instruction tuning

    arXiv
    2610.12602
    Published
    Layer
    Training and data layer
    Setting
    Models and APIs
    Evaluated models
    Qwen3-0.6B, Qwen3-1.7B, Qwen3-4B and others4
    AbstractThe authors claim self-replay can reduce forgetting outside the list, and that on the 4B model new task information helps with target selection, while smaller models do not.

    The authors study how language models, when learning new tasks, can use their own knowledge for broader self-preservation: given an incomplete list and forgetting that extends beyond the list, can the model generate replay and help choose targets.

    Paper analysisRead analysis
  7. Analysis and theoryarXiv 2610.12313

    The study tests the sensitivity of five large-model compliance systems to perturbations of regulatory rules.

    Diagnose whether compliance system rulings change with perturbations to regulatory rules

    arXiv
    2610.12313
    Published
    Layer
    Prompt layer
    Setting
    Models and APIs
    Evaluated models
    Llama-3.3-70B-Instruct, Llama-3.1-8B-Instruct, Qwen2.5-7B-Instruct and others5
    AbstractAcross five models, many correct verdicts are largely unchanged by deleting or replacing negation rules.

    Use behavioral counterfactuals and activation probes to audit whether compliance rulings rely on the given rules. The authors aim to separate two kinds of seemingly identical correct rulings: those that use the given rules, and those obtained from case surface features or training patterns.

    Paper analysisRead analysis
  9. Analysis and theoryarXiv 2610.12125

    The researchers found that optimizing the teaching fit metric makes LLM tutors repeat the same option, with expert scores dropping from 4.46 to 3.03

    Analyzing why optimizing teaching adaptation metrics collapses tutoring choices and diverges from expert judgment

    arXiv
    2610.12125
    Published
    Layer
    Training and data layer
    Setting
    Models and APIs
    Evaluated models
    Gemini 3.1 Pro Preview, Qwen3-32B
    AbstractMetrics rise while expert scores fall: the optimum of the separable average is repetition, and the correction mainly rewrites the output projection.

    After turning teaching adaptation metrics into a fine-tuning objective, the metrics rise while scores from trained educators decline.

    Paper analysisRead analysis
  10. OtherarXiv 2610.11959

    MiMo-V2.6: Toward Self-Improvement via Scaled Reinforcement Learning

    Extending reinforcement learning for full-modality foundation models to support agent self-improvement

    arXiv
    2610.11959
    Published
    Layer
    Training and data layer
    Setting
    coding agent
    Evaluated models
    MiMo-V2.6-Pro, MiMo-V2.6-Flash, MiMo-V2.6-Distill-Qwen-9B
    AbstractThe series uses three-dimensional RL and group scoring to improve agent scores; the authors claim the final performance is comparable to frontier models.

    MiMo-V2.6 is an all-modality MoE technical report, pursuing agent self-improvement by scaling up RL computation, environments, and scoring.

    Paper analysisRead analysis
  11. Evaluation and benchmarksarXiv 2610.11766

    研究提出意图恢复率作为 LLM 安全评测的补充指标

    提出 IRIS，用意图恢复率区分遮蔽越狱下无害输出的原因

    arXiv
    2610.11766
    Published
    Layer
    Prompt layer
    Setting
    Models and APIs
    Evaluated models
    GPT-4o, Claude Sonnet 4.5, Claude Opus 4.5 and others6
    Abstract无害输出的证据权重取决于任务是否被恢复。

    IRIS 把意图恢复当作中间信号，用来解释意图遮蔽提示下为何没有出现有害协助。

    Paper analysisRead analysis
  12. Evaluation and benchmarksarXiv 2610.11599

    研究：大语言模型版本更替削弱 AI 辅助科研写作筛查

    测量 LLM 版本更替对科学摘要 AI 改写检测器的影响

    arXiv
    2610.11599
    Published
    Layer
    Prompt layer
    Setting
    Models and APIs
    Evaluated models
    RoBERTa-base, Pangram
    Abstract作者称检测器的基准准确率只是临时的，每次发布都要连同更早版本重验。

    作者量化的是：科学摘要的 AI 改写筛查，在 LLM 版本不断更换、而人类摘要误标必须少见时，会怎样变。问题来自期刊和会议已经按分数阈值筛查投稿，但基准多停在评估时可得的版本上。

    Paper analysisRead analysis
  13. DefensesarXiv 2610.11352

    RL-ARC：用推理引导的不确定性校准大型推理模型

    提出 RL-ARC，用推理置信度在强化学习中校准推理模型

    arXiv
    2610.11352
    Published
    Layer
    Training and data layer
    Setting
    Models and APIs
    Evaluated models
    Qwen2.5-7B, Qwen3-8B, Llama-8B distilled from DeepSeek-R1
    AbstractRL-ARC 用推理置信度调节答案置信度，作者称可少牺牲精度并改善 ID 与 OOD 校准。

    RL-ARC 是校准感知的强化学习框架，用推理过程的口头置信度调节答案置信度。

    Paper analysisRead analysis
  14. DefensesarXiv 2610.11281

    包络采样：用少量真值标注重校准 LLM 评委以缓解奖励作弊

    提出包络采样，用少量真值标注重标定 LLM 评委以缓解奖励作弊

    arXiv
    2610.11281
    Published
    Layer
    Training and data layer
    Setting
    Models and APIs
    Evaluated models
    Qwen3-4B, Qwen3.8-27B, Qwen2.5-14B-Instruct
    Abstract包络采样把真值标注打到代理尾部，同样预算下缓解 reward hacking。

    作者研究后训练前如何选择真值标注的查询分布，使重标定后的廉价代理更适合被优化。真奖励昂贵时，实践用 LLM judge 代替人类。若失准只发生在基座很少生成的输出上，on-policy 标注修不到这些点，随后的 KL 正则优化会把质量集中到被高估的输出。

    Paper analysisRead analysis
  15. Evaluation and benchmarksarXiv 2610.11112

    EpiReal-Bench：商用图像生成模型虚假声明红队基准

    构建 EpiReal-Bench，评测图像生成器将虚假主张渲染为可信证据

    arXiv
    2610.11112
    Published
    Layer
    Prompt layer
    Setting
    Models and APIs
    Attacker
    Black-box
    Evaluated models
    GPT-Image-2, Nano Banana 2, Grok Imagen 2.0 and others4
    Abstract四款商用图像模型能把假主张画成可信证据。

    EpiReal-Bench 用来量商用图像生成器会不会把虚假现实主张画成可信视觉证据。

    Paper analysisRead analysis
  16. DefensesarXiv 2610.11012

    Redwood Research 提出蒸馏双重困境：用蒸馏让模型坦白隐藏行为或只继承能力

    提出 DFI 与 DFC 蒸馏，定罪隐瞒失配的教师或安全提取能力

    arXiv
    2610.11012
    Published
    Layer
    Training and data layer
    Setting
    Models and APIs
    Evaluated models
    Llama-3.3-70B-Instruct (AuditBench SFT/KTO organisms), Llama-3.3-70B-Instruct (DFI distilled student), Llama-3.1-8B-Instruct (DFI distilled student) and others4

    Abstract概括了论文针对隐瞒失配模型提出的 DFI 与 DFC 机制、主实验发现及适用边界。

    Paper analysisRead analysis
  17. DefensesarXiv 2610.11005

    研究：叙事包装可跨语言绕过 LLM 拒答

    提出 GUISE 基准与 AXIS 训练，防御叙事包装越狱

    arXiv
    2610.11005
    Published
    Layer
    Training and data layer
    Setting
    Models and APIs
    Evaluated models
    Qwen3-1.7B, Qwen3-4B, GLM-4-9B
    AbstractGUISE 分开测包装与语域。

    作者测量叙事包装下的拒答失效，并训练一个在未见包装上拒绝、同时仍回答同风格良性请求的适配器。问题是：安全对齐模型拒绝直说的有害请求，却回答嵌进角色或叙事里的同一请求。文言文被用来把语域和包装拆开。

    Paper analysisRead analysis
  18. Analysis and theoryarXiv 2610.10422

    研究：在线 RL 行为归因信号可能被梯度大小与流畅度混淆

    检验在线 RL 行为归因信号是否被梯度大小与流畅度混淆

    arXiv
    2610.10422
    Published
    Layer
    Training and data layer
    Setting
    Models and APIs
    Evaluated models
    Qwen2.5-1.5B-Instruct
    Abstract作者称步级精度多可由梯度大小解释，逐 rollout 方向随种子和抽样翻转。

    BehaviorTrace 用原因已知的植入行为，检查在线 GRPO 里已有梯度归因能否找到教出该行为的 rollout。

    Paper analysisRead analysis
  19. Analysis and theoryarXiv 2610.10657

    Research shows that subliminal learning can transfer new capabilities, backdoors, and cheating tendencies

    检验潜意识学习能否经无关数据蒸馏传递能力、后门与作弊

    arXiv
    2610.10657
    Published
    Layer
    Training and data layer
    Setting
    Models and APIs
    Evaluated models
    Qwen3.6-35B-A3B, Qwen3.5-9B, Qwen3.6-27B and others4

    Abstract作者报告潜意识学习可传递新技能、后门与作弊倾向，且分布外泛化强于单层转向向量。

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