Security Audit

firmao-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

firmao-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 for excessive permissions and arbitrary 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

Network Access

1 finding

Filesystem Write

1 finding

Shell Execution

1 finding

Dynamic Code

1 finding

Excessive Permissions

1 finding

Security Findings1

Severity	Finding	Layer	Location
HIGH	Potential for excessive permissions and arbitrary execution via RUBE_REMOTE_WORKBENCH The skill recommends using `RUBE_REMOTE_WORKBENCH` for 'Bulk ops' with `run_composio_tool()`. The term 'workbench' and the ability to 'run_composio_tool()' suggest that this tool might allow for arbitrary execution of other Composio tools. If these underlying tools have broad capabilities (e.g., file system access, network requests to arbitrary endpoints, shell execution), an attacker could instruct the LLM to use `RUBE_REMOTE_WORKBENCH` to perform unauthorized actions. This could lead to data exfiltration, command injection, or other excessive permission abuses. The skill does not provide sufficient context or warnings about the potential power of this tool, making it a high-risk instruction for an LLM. Clarify the exact capabilities and limitations of `RUBE_REMOTE_WORKBENCH` and `run_composio_tool()`. If it allows broad access, add strong warnings and guidance on secure usage, or consider if this tool should be exposed to an LLM in this manner. Ensure the underlying Rube MCP system properly sandboxes and restricts the actions of `RUBE_REMOTE_WORKBENCH` and any tools it executes to prevent misuse.	LLM	SKILL.md:79

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