Semantic Compliance Hijacking lets attackers hijack LLM agents by disguising malicious instructions as compliance rules in skills, reaching up to 77.67% success on confidentiality breaches and 67.33% on RCE while evading all tested scanners.
SkillJect: Automating stealthy skill-based prompt injection for coding agents with trace-driven closed- loop refinement
9 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.CR 9years
2026 9representative citing papers
BIV audits AI agent skills at scale, finding 80% deviate from declared behavior on 49,943 skills and achieving 0.946 F1 for malicious skill detection.
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.
A memory-layer defense called Memory Sandbox stops persistent memory attacks on most LLM agents while other layer defenses fail.
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.
SkillSieve is a hierarchical triage framework combining regex/AST/XGBoost filtering, parallel LLM subtasks, and multi-LLM jury voting to detect malicious AI agent skills, reaching 0.800 F1 on a 400-skill benchmark at 0.006 cost per skill.
Poisoning any single CIK dimension of an AI agent raises average attack success rate from 24.6% to 64-74% across models, and tested defenses leave substantial residual risk.
The survey organizes over 400 papers on embodied AI safety into a multi-level taxonomy and flags overlooked issues such as fragile multimodal fusion and unstable planning under jailbreaks.
SkillGuard-Robust formulates pre-load auditing of untrusted Agent Skills as a three-way classification task and achieves 97.30% exact match and 98.33% malicious-risk recall on held-out benchmarks.
citing papers explorer
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Exploiting LLM Agent Supply Chains via Payload-less Skills
Semantic Compliance Hijacking lets attackers hijack LLM agents by disguising malicious instructions as compliance rules in skills, reaching up to 77.67% success on confidentiality breaches and 67.33% on RCE while evading all tested scanners.
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Behavioral Integrity Verification for AI Agent Skills
BIV audits AI agent skills at scale, finding 80% deviate from declared behavior on 49,943 skills and achieving 0.946 F1 for malicious skill detection.
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Red-Teaming Agent Execution Contexts: Open-World Security Evaluation on OpenClaw
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.
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Defense effectiveness across architectural layers: a mechanistic evaluation of persistent memory attacks on stateful LLM agents
A memory-layer defense called Memory Sandbox stops persistent memory attacks on most LLM agents while other layer defenses fail.
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SkillScope: Toward Fine-Grained Least-Privilege Enforcement for Agent Skills
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.
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SkillSieve: A Hierarchical Triage Framework for Detecting Malicious AI Agent Skills
SkillSieve is a hierarchical triage framework combining regex/AST/XGBoost filtering, parallel LLM subtasks, and multi-LLM jury voting to detect malicious AI agent skills, reaching 0.800 F1 on a 400-skill benchmark at 0.006 cost per skill.
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Your Agent, Their Asset: A Real-World Safety Analysis of OpenClaw
Poisoning any single CIK dimension of an AI agent raises average attack success rate from 24.6% to 64-74% across models, and tested defenses leave substantial residual risk.
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Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses
The survey organizes over 400 papers on embodied AI safety into a multi-level taxonomy and flags overlooked issues such as fragile multimodal fusion and unstable planning under jailbreaks.
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Structured Security Auditing and Robustness Enhancement for Untrusted Agent Skills
SkillGuard-Robust formulates pre-load auditing of untrusted Agent Skills as a three-way classification task and achieves 97.30% exact match and 98.33% malicious-risk recall on held-out benchmarks.