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Oct 13 · Tuesday

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Found 758 papers
Filter6

Attack type

Jailbreaks41Prompt injection239Poisoning and backdoors109Extraction and theft20Detection evasion23Denial of service6Malicious use14

Attack technique

Gradient and representation optimization17Automated search61Multi-turn escalation8Encoding and obfuscation23Role-play and persuasion10Conversation structure manipulation5Reasoning-chain manipulation5Cross-modal payloads13Defense-targeted20Dormant triggers36Selection manipulation12

Model risks

Deception and scheming38Reward hacking30Misalignment and value divergence23Dangerous agent actions81Dangerous capabilities23Inappropriate refusal9Hallucination and fabrication18Bias and discrimination3

Defense type

Model hardening15Inference-time intervention12Detection and filtering62Instruction–data separation54Permissions and approval71Monitoring and auditing45Watermarking and provenance5

Impact

Harmful content37Unauthorized actions214Data and privacy leaks72Output manipulation161Availability and reliability48Oversight failure57Increased misuse capability15

System type

Models and APIs414LLM applications135AI Agent629coding agent127science agent30web agent34Browser and computer use19Multi-agent systems83Embodied systems and robotics27

Attacker access

Black-box36Gray-box4White-box6

Attack surface / entry point

User input129Web pages and interfaces74Documents and messages81Tool responses107Tool and skill descriptions63Code repositories and dependencies29Retrieval corpus36Memory writes44Inter-agent messages28Images, audio and video22Training data3Weights and gradients4Distribution channels46Inference services and hardware3

Layer

Prompt layer17Model layer29Application layer729Training and data layer30Infrastructure layer4

System component

MCP and skills84Memory82RAG54Guardrails and judges79Monitors63Reasoning chains16Permissions and sandboxing92Fine-tuning and open weights4Vision35Audio6Image generation3Embeddings9Multimodal42
Select any value within a category; different categories must all match. Counts include the other filters you have selected.Another 213papers have no tags yet and are not included in filtered results.
System type：LLM applicationsSystem type：AI AgentSystem type：Browser and computer useSystem type：Embodied systems and roboticsLayer：Model layerLayer：Application layerClear all

Another 213papers do not have category tags yet and are not included in filtered results.

  1. 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.

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  4. 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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  5. Evaluation and benchmarksarXiv 2610.13710

    OpenSocialJax: Evaluating Social Adaptation in Sequential Social Dilemmas with Open-Ended Environment Dynamics

    Evaluating multi-agent adaptation in social dilemmas with hidden rules using OpenSocialJax

    arXiv
    2610.13710
    Published
    Layer
    Application layer
    Setting
    Multi-agent systems
    Evaluated models
    GPT-6-Luna, GPT-6-Sol, Claude Opus 5.5 and others8
    AbstractThe authors report that rule discovery often improves payoffs, but public good cleaning becomes concentrated, and shared resources are eventually depleted.

    OpenSocialJax evaluates whether LLMs can simultaneously adapt to unfamiliar environmental mechanisms and other agents in sequential social dilemmas.

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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.

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  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. OtherarXiv 2610.13372

    Toward Safe World Models: Risks, Defenses, and Evaluation

    A survey of risks, defenses, and evaluation in the six-stage lifecycle of world models

    arXiv
    2610.13372
    Published
    Layer
    Application layer
    Setting
    Embodied systems and robotics
    AbstractSafety is written as a property of the prediction-decision closed loop; claims must be linked to observed consequences and versions.

    World model safety is organized by the nature of the prediction-decision loop, not by isolated prediction scores.

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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. OtherarXiv 2610.12756

    How hardware bit flips affect LLM agents: safety and alignment behavior can vary

    Measuring the impact of random weight bit flips on LLM agent safety and alignment behavior

    arXiv
    2610.12756
    Published
    Layer
    Application layer
    Setting
    AI Agent
    Evaluated models
    Qwen3.6-27B, Qwen3.8-27B, Granite4.1-30B
    AbstractTwo-bit weight faults can be partially repaired in closed-loop settings, but they increase output tokens and, in specific models and scenarios, change safety or alignment behavior.

    After random two-bit weight faults enter an LLM agent, model-level damage becomes recoverable, increased cost, subtle semantic errors, and changes in safety or alignment behavior.

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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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  18. Analysis and theoryarXiv 2610.12436

    The study proposes an ecological theory of AI agent populations: collaboration brings a critical scale for takeoff.

    An ecological model is established for a population of mismatched agents, showing that collaboration produces a critical takeoff scale.

    arXiv
    2610.12436
    Published
    Layer
    Application layer
    Setting
    Multi-agent systems
    AbstractThe authors say that collaboration changes takeoff from an individual capability threshold to a population threshold, so small-population evaluations are insufficient.

    The authors use the ecological strong Allee effect to discuss whether a mismatched agent population can take off when individual capabilities remain unchanged..

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  20. Evaluation and benchmarksarXiv 2610.12360

    The paper proposes the ISE framework to evaluate LLM agents' epistemic humility under knowledge conflicts

    Proposes the ISE framework to evaluate the epistemic humility of LLM agents under knowledge conflicts

    arXiv
    2610.12360
    Published
    Layer
    Application layer
    Setting
    AI Agent
    Evaluated models
    Nemotron-Orchestrator-8B, Claude Sonnet 4.6, GPT-5 and others5
    AbstractISE trajectory evaluations show that high task accuracy across four agents does not correspond to higher cognitive humility.

    What the authors want to test is not whether the agent's final answer is correct, but whether, when a knowledge conflict arises, it recognizes the gap in its trajectory, performs bounded tool follow-up, and writes the unresolved uncertainty to the user.

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