Security Audit

seat-geek-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

seat-geek-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 access to external service management and execution tools.

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

Network Access

1 finding

Shell Execution

1 finding

Excessive Permissions

1 finding

Security Findings1

Severity	Finding	Layer	Location
HIGH	Broad access to external service management and execution tools The skill instructs the LLM to use `RUBE_MANAGE_CONNECTIONS`, `RUBE_MULTI_EXECUTE_TOOL`, and `RUBE_REMOTE_WORKBENCH`. These tools provide broad capabilities, including managing connections to external services (Seat Geek) and executing arbitrary operations on those services. While these are core functionalities of the skill, they represent high-privilege actions. A compromised LLM could potentially misuse these tools to disconnect legitimate connections, establish unauthorized connections, or perform malicious operations on the Seat Geek platform. Implement strict access controls and authorization checks within the Rube MCP system for `RUBE_MANAGE_CONNECTIONS` and `RUBE_MULTI_EXECUTE_TOOL`. Ensure that the LLM's access to these tools is limited to specific, authorized operations and that user input is thoroughly validated before being passed to these powerful tools. Consider implementing human-in-the-loop approval for sensitive operations.	LLM	SKILL.md:40

Scan History

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