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Oct 13 · Tuesday

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Found 953 papers
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Attack type

Jailbreaks212Prompt injection84Poisoning and backdoors62Model tampering19Extraction and theft49Detection evasion65Denial of service5Malicious use20

Attack technique

Gradient and representation optimization54Automated search76Multi-turn escalation24Encoding and obfuscation58Role-play and persuasion33Conversation structure manipulation13Reasoning-chain manipulation12Cross-modal payloads51Defense-targeted22Dormant triggers16Selection manipulation4

Model risks

Deception and scheming34Reward hacking24Misalignment and value divergence26Dangerous agent actions26Dangerous capabilities14Inappropriate refusal55Hallucination and fabrication33Bias and discrimination19

Defense type

Model hardening23Inference-time intervention43Detection and filtering64Instruction–data separation20Permissions and approval16Monitoring and auditing38Watermarking and provenance14

Impact

Harmful content250Unauthorized actions55Data and privacy leaks70Output manipulation217Availability and reliability33Oversight failure61Increased misuse capability9

System type

Models and APIs663LLM applications143AI Agent642coding agent132science agent30web agent34Browser and computer use19Multi-agent systems83Embodied systems and robotics33

Attacker access

Black-box140Gray-box15White-box42No-box (offline transfer)6

Attack surface / entry point

User input305Web pages and interfaces18Documents and messages35Tool responses16Tool and skill descriptions28Code repositories and dependencies22Retrieval corpus28Memory writes6Images, audio and video102Training data15Weights and gradients36Distribution channels28Inference services and hardware6

Layer

Prompt layer257Model layer419Application layer277Training and data layer224Infrastructure layer23

System component

MCP and skills30Memory15RAG45Guardrails and judges119Monitors49Reasoning chains50Permissions and sandboxing16Fine-tuning and open weights16Vision100Audio29Video10Image generation40Embeddings13Multimodal168
Select any value within a category; different categories must all match. Counts include the other filters you have selected.Another 201papers have no tags yet and are not included in filtered results.
System type：Models and APIsSystem type：LLM applicationsSystem type：coding agentSystem type：Browser and computer useLayer：Prompt layerLayer：Model layerLayer：Application layerClear all

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  1. AttacksarXiv 2610.13928

    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
    Layer
    Model layer
    Setting
    Models and APIs
    Evaluated models
    Gemma 3 270M
    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
  2. OtherarXiv 2610.13912

    RSI-Anything: Unlocking Recursive Self-Improvement with World Models

    Proposes RSI-Anything, which uses a world model to select candidates and retain policy updates.

    arXiv
    2610.13912
    Published
    Layer
    Application layer
    Setting
    coding agent, Browser and computer use
    Evaluated models
    ReasonFlux-Coder-7B, 4B policy, W1
    AbstractRSI-Anything integrates world models into recursive self-improvement through prediction, feedback, and retention checks, and reports gains across multiple domains.

    RSI-Anything is a cross-domain framework: it uses world model predictions to decide which candidates to take external feedback for, then uses the feedback to decide whether the policy, workflow, or predictor is updated, and uses checks to decide which pair is retained. The difficulty to address is the limited interaction budget, and the fact that once the policy changes, the candidate distribution faced by the predictor also changes. The authors argue that improving only one side does not guarantee that the assembled system will be better.

    Paper analysisRead analysis
  3. OtherarXiv 2610.13810

    Using subsequence association filtering and breadth-aware selection to suppress hallucinated words in LLM query expansion

    Proposes SAFE-QE, which filters unsupported hallucinated words in LLM query expansion

    arXiv
    2610.13810
    Published
    Layer
    Application layer
    Setting
    LLM applications
    Evaluated models
    BM25, SPLADE, Contriever

    AbstractSAFE-QE suppresses expansion hallucinations with reproducibility and coverage selection; the authors say ranking quality shows no visible cost.

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  4. 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.

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  6. OtherarXiv 2610.13491

    EviRepair: Evidence-Driven Agents for Reliable Vulnerability Repair

    Proposing EviRepair, an evidence-driven multi-agent framework for repository-level vulnerability repair

    arXiv
    2610.13491
    Published
    Layer
    Application layer
    Setting
    coding agent, Multi-agent systems
    Evaluated models
    DeepSeek-V4.1-Flash, GLM-5.2, Qwen3.8-27B
    AbstractEviRepair uses a reviewed vulnerability model for repository-level repair.

    EviRepair is a repository-level automated vulnerability repair framework for sparse vulnerability reports, using a reviewed vulnerability model to separate diagnosis from patch synthesis.

    Paper analysisRead analysis
  7. OtherarXiv 2610.13479

    A local agentic AI framework generates edge-case scenarios for safety assurance

    Proposes a local dual-model closed loop to generate edge scenarios for autonomous system safety assurance

    arXiv
    2610.13479
    Published
    Layer
    Application layer
    Setting
    LLM applications, Embodied systems and robotics
    Evaluated models
    PPO
    AbstractLocal closed-loop generation of JSON edge cases.

    The authors built a local agentic loop that runs on a laptop to generate edge cases for safety arguments of autonomous systems, rather than releasing a new public test set.

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  9. 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.

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  10. InterpretabilityarXiv 2610.13078

    Attention sees, MLP refuses: tracing the safety pathway from attention to MLP in LLMs

    Trace attention to identify harmfulness to the MLP refusal write pathway and propose Sentinel

    arXiv
    2610.13078
    Published
    Layer
    Model layer
    Setting
    Models and APIs
    Evaluated models
    Llama-3.1-8B-Instruct, Qwen2.5-7B-Instruct, Qwen3-8B-Instruct and others18
    AbstractAttention updates are more readable for harmfulness, MLP updates write refusal, and Sentinel routes alerts using two-path signals.

    The authors reframe jailbreaking and excessive refusal of aligned models as a pathway problem on the residual stream from recognition to writing, and accordingly provide conditional reminders before decoding.

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  11. InterpretabilityarXiv 2610.12989

    GPT-2-Small, Gemma-2-2B, and Llama-3.1-8B have interpretable narrative mechanisms

    Using NVC to locate and ablate narrative-related components in language models

    arXiv
    2610.12989
    Published
    Layer
    Model layer
    Setting
    Models and APIs
    Evaluated models
    GPT-2-Small-124M, Gemma-2-2B, Llama-3.1-8B
    AbstractNeurons and SAE features related to narrativity across three models, when ablated, changed some narrative-related behaviors.

    The authors connect a three-dimensional definition from narratology to mechanistic interventions, examining whether components related to narrativity in pretrained language models participate in prediction, generation, and downstream tasks.

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  12. InterpretabilityarXiv 2610.12842

    Neural networks are more modular than single neurons suggest

    Using a three-level nested decomposition to test whether mixed-selectivity networks can be functionally separated on subspaces

    arXiv
    2610.12842
    Published
    Layer
    Model layer
    Setting
    Models and APIs
    Evaluated models
    Pythia 410M, Pythia 160M, GPT-2 XL and others18
    AbstractUnit non-specialization and subspace separability can coexist.

    The authors propose a nested definition of modularity to separate "neurons not being specialized" from "computation not being separable."

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  13. Risky behaviorarXiv 2610.12822

    Reward hacking as a transmissible strategy in multi-agent coding systems

    Show that reward hacking can propagate through code, memory, and plans in multi-agent coding systems

    arXiv
    2610.12822
    Published
    Layer
    Application layer
    Setting
    Multi-agent systems, coding agent
    Evaluated models
    Qwen2.5-Coder-14B, Gemma-3-12B, Qwen2.5-Coder-7B and others16
    AbstractThe authors state that reward hacking can spread across models and affect routing and aggregation, and that review can reduce delivery.

    Reward hacking induced by coding RL can leave the source agent and enter subsequent collaboration and system coordination.

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  14. Evaluation and benchmarksarXiv 2610.12758

    DistressBench: Evaluating multi-dimensional crisis support in large language models

    Introduces DistressBench, evaluating models' multidimensional support in crisis conversations

    arXiv
    2610.12758
    Published
    Layer
    Model layer
    Setting
    Models and APIs
    Evaluated models
    Muse Spark 1.2, Muse Spark 1.3, Muse Glimmer 30B and others20
    AbstractThe model is more restrained and acts less.

    DistressBench uses SSH dialogues written and revised by clinical personnel, and evaluates how much necessary care general chat models deliver according to weighted atomic criteria, rather than whether they refuse. When users disclose suicidal ideation or self-harm to a chat model, the authors consider the expected behavior to be recognizing the crisis, calibrating empathy, de-escalating, and referring to human help. Refusal-based evaluations cannot detect failing to act after recognizing the crisis.

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  15. Evaluation and benchmarksarXiv 2610.12569

    SWE-Duel: Toward a Self-Expanding Arena for Coding Agents

    Proposes SWE-Duel, which lets coding agents mutually evaluate, create problems, and blindly review fixes during evaluation.

    arXiv
    2610.12569
    Published
    Layer
    Application layer
    Setting
    coding agent
    Evaluated models
    GPT-5.5, Claude Opus 4.8, Kimi K2.7 Code and others11
    AbstractThe mutual evaluation arena creates questions during evaluation.

    SWE-Duel is a coding agent arena that generates tasks and judges only at evaluation time, to avoid leakage and saturation of static benchmarks.

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  16. InterpretabilityarXiv 2610.12410

    The paper proposes an alignment generalization prediction task, using activation representations to predict the cross-context effects of fine-tuned values.

    Comparing the predictive power of value representations for alignment generalization after single-value fine-tuning

    arXiv
    2610.12410
    Published
    Layer
    Model layer
    Setting
    Models and APIs
    Evaluated models
    Olmo-3-7B, Olmo-3-32B, Qwen-3-8B-Base and others4
    AbstractActivation representations can predict cross-value generalization before training and relate to robustness across multiple value objectives.

    The authors define "how single-value fine-tuning changes compliance on untrained values" as a task predictable before training, and use value representations to do this.

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  17. Analysis and theoryarXiv 2610.12409

    The researchers conduct a psychometric review of HELM Safety and HarmBench, questioning whether their scores can represent a single attribute

    Auditing whether HarmBench scores can represent a single harmful refusal attribute

    arXiv
    2610.12409
    Published
    Layer
    Model layer
    Setting
    Models and APIs
    AbstractThe authors claim HarmBench does not correspond to a single harmful refusal, and behaviors should be separated when comparing models.

    This is a construct validity audit of HarmBench in HELM Safety, examining whether harmful refusal can be read as a single attribute when interpreting scores.

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  18. InterpretabilityarXiv 2610.12361

    Study: legal reasoning models citing legal provisions does not equal relying on legal provisions.

    Uses premise permutation and hidden-layer tracing to audit whether legal chain-of-thought truly relies on the cited legal provisions.

    arXiv
    2610.12361
    Published
    Layer
    Model layer
    Setting
    Models and APIs
    Evaluated models
    Qwen3-8B, Qwen3-14B, Llama-3.1-8B and others7
    AbstractUsing counterfactual substitution of authorities and internal commitment tracing, the authors find that legal CoT rulings depend on authorities only to a limited extent and are vulnerable to prompt injection manipulation.

    This study conducts an empirical audit of the causal truthfulness of legal citations in chain-of-thought reasoning of large language models in the legal domain.

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  19. 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.

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  20. AttacksarXiv 2610.12292

    The paper proposes an option channel attack: changing one option label can make an agent guardrail allow a prohibited operation

    Proposing an option channel attack that renames option labels so that agent guardrails allow prohibited operations

    arXiv
    2610.12292
    Published
    Layer
    Application layer
    Setting
    LLM applications
    Attacker
    Black-box
    Evaluated models
    LAYA-TD, LAYA-EN, LAYA-ML and others5

    AbstractThe authors' evaluation indicates that typed models should not serve as final safety arbiters; they are susceptible to off-policy text and option name manipulation, and confidence filtering cannot resist attacks.

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