Security Audit

identitycheck-automation

github.com/ComposioHQ/awesome-claude-skills

AI SkillCommit 99e2a2951545

TRUSTED

Scanned 6 days ago

Critical

Immediate action required

High

Priority fixes suggested

Medium

Best practices review

Low

Acknowledged / Tracked

Trust Assessment

identitycheck-automation received a trust score of 95/100, placing it in the Trusted category. This skill has passed all critical security checks and demonstrates strong security practices.

SkillShield's automated analysis identified 1 finding: 0 critical, 0 high, 1 medium, and 0 low severity. Key findings include Potential for Excessive Permissions via RUBE_REMOTE_WORKBENCH.

The analysis covered 4 layers: Manifest Analysis, Static Code Analysis, Dependency Graph, LLM Behavioral Safety. All layers scored 70 or above, reflecting consistent security practices.

Last analyzed on February 17, 2026 (commit 99e2a295). SkillShield performs automated 4-layer security analysis on AI skills and MCP servers.

Layer Breakdown

Manifest Analysis

100%

Static Code Analysis

100%

Dependency Graph

100%

LLM Behavioral Safety

93%

Behavioral Risk Signals

Shell Execution

1 finding

Excessive Permissions

1 finding

Security Findings1

Severity	Finding	Layer	Location
MEDIUM	Potential for Excessive Permissions via RUBE_REMOTE_WORKBENCH The skill instructs the LLM on how to use `RUBE_REMOTE_WORKBENCH` with `run_composio_tool()` for 'Bulk ops'. This tool appears to offer broad capabilities, potentially allowing the LLM to execute arbitrary Composio tools or scripts within a remote workbench environment. If the underlying 'identitycheck' toolkit has extensive permissions (e.g., to modify sensitive identity data, create/delete users), a malicious prompt could leverage this powerful tool to perform unauthorized or excessive operations. The description of `RUBE_REMOTE_WORKBENCH` is vague, which increases the risk of misuse by an LLM. Clarify the exact capabilities and limitations of `RUBE_REMOTE_WORKBENCH` and `run_composio_tool()`. If it allows arbitrary code execution or broad access, consider if this level of access is truly necessary for the skill's intended purpose. Implement stricter input validation or sandboxing for this tool if possible. Ensure the 'identitycheck' toolkit itself operates with the principle of least privilege.	LLM	SKILL.md:79

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