Security Audit

tomtom-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

tomtom-automation received a trust score of 82/100, placing it in the Mostly Trusted category. This skill has passed most security checks with only minor considerations noted.

SkillShield's automated analysis identified 2 findings: 0 critical, 1 high, 1 medium, and 0 low severity. Key findings include Broad Tool Execution via Rube MCP, Unpinned Rube MCP Dependency.

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

78%

Behavioral Risk Signals

Network Access

1 finding

Shell Execution

1 finding

Dynamic Code

1 finding

Excessive Permissions

1 finding

Security Findings2

Severity	Finding	Layer	Location
HIGH	Broad Tool Execution via Rube MCP The skill leverages Rube MCP, a meta-tooling system, and explicitly instructs the agent to use `RUBE_SEARCH_TOOLS` for dynamic tool discovery and `RUBE_MULTI_EXECUTE_TOOL` or `RUBE_REMOTE_WORKBENCH` for execution. This design pattern grants the agent the ability to execute any tool available through the connected Rube MCP, including potentially highly privileged or sensitive operations, without explicit constraints defined within the skill itself. The scope of permissions is entirely dependent on the Rube MCP configuration, which is external to this skill definition. An agent could potentially discover and execute tools with broader permissions than intended for a specific task, leading to unauthorized actions. Implement strict access controls and granular permissions within the Rube MCP system itself, ensuring that only necessary tools and operations are exposed to the agent. Consider using a dedicated Rube MCP instance with a restricted set of tools for this specific skill. If possible, define a whitelist of allowed `tool_slug` values within the skill's configuration or prompt to limit the agent's execution scope.	LLM	SKILL.md:59
MEDIUM	Unpinned Rube MCP Dependency The skill's manifest specifies a dependency on "rube" for its MCP (`"requires": {"mcp": ["rube"]}`). However, no specific version is pinned. This means that any version of Rube MCP could be used, including future versions that might introduce breaking changes, vulnerabilities, or altered behavior without explicit review or approval by the skill author. This lack of version pinning introduces a supply chain risk. Pin the Rube MCP dependency to a specific, known-good version in the skill's manifest (e.g., `"rube": "1.2.3"` or `"rube": "^1.0.0"` if semantic versioning is followed and minor/patch updates are acceptable). Regularly review and update dependencies to newer versions after verifying compatibility and security.	LLM	SKILL.md

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