Security Audit

mailboxlayer-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

mailboxlayer-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 Command Injection/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

Dynamic Code

1 finding

Excessive Permissions

1 finding

Security Findings1

Severity	Finding	Layer	Location
MEDIUM	Potential Command Injection/Excessive Permissions via RUBE_REMOTE_WORKBENCH The skill mentions `RUBE_REMOTE_WORKBENCH` with `run_composio_tool()` for 'Bulk ops' in the Quick Reference section. The term 'workbench' often implies a powerful execution environment, and `run_composio_tool()` could potentially allow for arbitrary code execution or highly privileged operations within the Composio platform. Without clear limitations or examples of its usage, this tool presents a risk of command injection or excessive permissions if an attacker can manipulate its arguments to execute unintended commands or access unauthorized resources. Provide explicit documentation and strict access controls for `RUBE_REMOTE_WORKBENCH` and `run_composio_tool()`. Clearly define its scope, what types of operations it can perform, and what arguments it accepts. If it allows arbitrary code execution, it should be restricted or removed from general skill usage, or require explicit user confirmation for sensitive operations.	LLM	SKILL.md:70

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