Security Audit

linkhut-automation

github.com/ComposioHQ/awesome-claude-skills

AI SkillCommit 27904475d127

TRUSTED

Scanned about 2 months ago

Critical

Immediate action required

High

Priority fixes suggested

Medium

Best practices review

Low

Acknowledged / Tracked

Trust Assessment

linkhut-automation received a trust score of 85/100, placing it in the Mostly Trusted category. This skill has passed most security checks with only minor considerations noted.

SkillShield's automated analysis identified 1 finding: 0 critical, 1 high, 0 medium, and 0 low severity. Key findings include Broad Tool Execution 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 20, 2026 (commit 27904475). 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

85%

Behavioral Risk Signals

Shell Execution

1 finding

Dynamic Code

1 finding

Excessive Permissions

1 finding

Security Findings1

Severity	Finding	Layer	Location
HIGH	Broad Tool Execution via RUBE_REMOTE_WORKBENCH The skill exposes `RUBE_REMOTE_WORKBENCH` with `run_composio_tool()`, described as enabling 'Bulk ops'. This interface allows the LLM to execute arbitrary Composio tools, which could include tools with broad permissions or the ability to perform sensitive actions on connected services (e.g., Linkhut, or other services if Composio supports them). A malicious prompt could manipulate the LLM to use this powerful tool to perform unauthorized operations, data manipulation, or even command injection if the underlying Composio tools are not sufficiently sandboxed or restricted. Implement strict access controls and granular permissions for `RUBE_REMOTE_WORKBENCH` and `run_composio_tool()`. Ensure that the LLM's access to specific Composio tools via this workbench is limited to only what is necessary for the skill's intended function. Provide clear documentation on the security implications and best practices for using such a powerful tool, and ensure the underlying Composio platform enforces robust sandboxing and authorization.	LLM	SKILL.md:67

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