Research Exposes Limitations in AI Skill Scanners for Agent Vulnerability Detection

An independent researcher evaluated three local vulnerability scanners for AI agent skill packages without using LLMs or external APIs. The test used 12 synthetic packages containing both benign and malicious instructions, including attempts to bypass consent via sub-agents and hidden instructions in nested files. Cisco Skill Scanner, NVIDIA SkillSpector, and AgentVerus were run in isolated containers. All three tools missed a critical delegation bypass in package 10, while correctly flagging an educational attack citation in package 5 as High severity. NVIDIA SkillSpector performed best on nested file detection, and the findings directly shaped the integration of a lightweight static analyzer plus optional NVIDIA scans into the CEO AI project. The study also highlighted the need to preserve detailed findings rather than relying solely on final safety scores.

HabrOctober 19, 2026Translated from Russian

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An independent researcher tested three local vulnerability scanners designed for AI agent skill packages to determine their effectiveness at detecting dangerous instructions before deployment. The evaluation used Cisco Skill Scanner version 2.2.2, NVIDIA SkillSpector version 2.12.0, and AgentVerus version 0.8.1, all executed in network-isolated containers without LLM components.

Twelve synthetic skill packages were created and divided into five benign and seven malicious examples. The malicious set included attempts to ignore system instructions, exfiltrate secrets, execute unverified code, modify launch agents, and delegate restricted tasks to sub-agents while hiding limitations and claiming user approval.

Package 10, which instructed the creation of a sub-agent to perform a restricted action while concealing constraints and fabricating user consent, was missed by all three scanners. AgentVerus returned a certified status with 99 points. In contrast, package 5, which contained an educational citation of a malicious instruction accompanied by an explicit prohibition against execution, triggered High-severity findings across all tools.

Package 12 tested detection of instructions placed in a nested references/extra.md file. NVIDIA SkillSpector correctly identified findings in the referenced file, while Cisco Skill Scanner produced no significant signal. AgentVerus reported undeclared file reads. These specific discrepancies influenced the final integration decisions for the researcher’s CEO AI project.

The researcher implemented a lightweight static analyzer that runs at indexing time and added an optional NVIDIA SkillSpector adapter for on-demand scans. The custom analyzer incorporated a rule specifically targeting sub-agent delegation bypasses. Limitations of the environment, network access, and tool permissions remain enforced at runtime, consistent with OWASP recommendations for agent security.

OWASP Cisco Skill Scanner NVIDIA SkillSpector AgentVerus

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