Security Audit

google_maps-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

google_maps-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 Potential Command Injection 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

Filesystem Write

1 finding

Shell Execution

1 finding

Dynamic Code

1 finding

Excessive Permissions

1 finding

Security Findings1

Severity	Finding	Layer	Location
HIGH	Potential Command Injection via RUBE_REMOTE_WORKBENCH The skill documentation instructs users to utilize `RUBE_REMOTE_WORKBENCH` with `run_composio_tool()` and `ThreadPoolExecutor` for bulk operations. This implies that `RUBE_REMOTE_WORKBENCH` is an environment capable of executing Python code. If the arguments or the code executed within this 'remote workbench' can be influenced by untrusted input, it could lead to arbitrary code execution. An attacker could potentially inject malicious code to run shell commands, access sensitive files, or exfiltrate data from the environment where `RUBE_REMOTE_WORKBENCH` operates. Clarify the security model and input sanitization requirements for `RUBE_REMOTE_WORKBENCH` and `run_composio_tool()`. Ensure that `RUBE_REMOTE_WORKBENCH` strictly limits code execution to safe, predefined functions or implements robust input validation to prevent arbitrary code injection. If arbitrary code execution is an intended feature, this should be explicitly stated with strong warnings about its security implications and comprehensive guidance on secure usage, including how to sanitize all user-provided inputs.	LLM	SKILL.md:70

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