MalSkillBench supplies the first sandbox-verified dataset of malicious agent skills and shows that existing detectors achieve high recall on code injection but collapse on prompt injection and agent-control attacks.
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Skillprobe: Security auditing for emerging agent skill marketplaces via multi-agent collaboration
Canonical reference. 80% of citing Pith papers cite this work as background.
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2026 19representative citing papers
Harmful skills in open agent ecosystems raise average harm scores from 0.27 to 0.76 across six LLMs by lowering refusal rates when tasks are presented via pre-installed skills.
Empirical study of EvoMap shows 98% of assets never reused, scores driven by self-reported metadata, and 84% of assets using vacuous validation tests.
SkillTTA synthesizes temporary task-specific skills from retrieved training trajectories to boost LLM agent Pass@1 scores on SpreadsheetBench and BigCodeBench without parameter updates.
SMMBench is a benchmark evaluating multimodal agents on cross-source reasoning, conflict resolution, preference reasoning, and action prediction, showing current systems struggle with evidence distributed across heterogeneous sources.
Sefz discovers specification violations in 29.9% of 402 real-world agent skills by translating guardrails into reachability goals and guiding LLM mutations with a multi-armed bandit.
Proteus demonstrates that adaptive red-teaming achieves 40-90% attack success after five rounds and bypasses even strong auditors at up to 41% joint success, revealing that static skill vetting underestimates residual risk.
Malicious Skills induce coding agents to hallucinate and import attacker-controlled packages at high rates while evading detection.
SIGIL cryptographically seals the audit-runtime gap for LLM skills via an on-chain registry with four publication types, DAO vetting, and a runtime verification loader that enforces integrity and permissions.
Runtime Skill Audit introduces targeted runtime probing to detect malicious LLM agent skills, reporting 90% accuracy and resilience to self-evolving attacks on 100 skills versus static baselines.
DeepTrap automates discovery of contextual vulnerabilities in OpenClaw agents via trajectory optimization, showing that unsafe behavior can be induced while preserving task completion and that final-response checks are insufficient.
Malicious actors could use AI agents to submit large numbers of fake papers, inflating the submission count and thereby raising the acceptance odds for a small set of chosen legitimate papers under stable conference acceptance rates.
SkillScope detects over-privileged LLM agent skills with 94.53% F1 score via graph analysis and replay validation, finding 7,039 problematic skills in the wild and reducing violations by 88.56% while preserving task completion.
Analysis of 67,453 OpenClaw skills shows three scanners overlap on at most 10.4% of combined positives, with 81.9% flagged by only one scanner and distinct profiles for malicious versus suspicious skills.
SkillVetBench is a two-stage benchmark combining natural-language semantic vetting and instrumented sandbox execution to detect and provide runtime evidence for malicious skills in open agent platforms, with experiments showing static methods miss up to 89% of threats.
LLM agent progress depends on externalizing cognitive functions into memory, skills, protocols, and harness engineering that coordinates them reliably.
Explicit provenance across the full agentic AI lifecycle is the necessary condition for making responsibility computable and actionable.
Proposes a trust schema including verification levels and a biconditional correctness criterion to verify skills in human-in-the-loop agent runtimes, reducing the need for constant oversight.
citing papers explorer
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Skills on the Fly: Test-Time Adaptive Skill Synthesis for LLM Agents
SkillTTA synthesizes temporary task-specific skills from retrieved training trajectories to boost LLM agent Pass@1 scores on SpreadsheetBench and BigCodeBench without parameter updates.
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SMMBench: A Benchmark for Source-Distributed Multimodal Agent Memory
SMMBench is a benchmark evaluating multimodal agents on cross-source reasoning, conflict resolution, preference reasoning, and action prediction, showing current systems struggle with evidence distributed across heterogeneous sources.
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Position: Academic Conferences are Potentially Facing Denominator Gaming Caused by Fully Automated Scientific Agents
Malicious actors could use AI agents to submit large numbers of fake papers, inflating the submission count and thereby raising the acceptance odds for a small set of chosen legitimate papers under stable conference acceptance rates.