Security Audit

altoviz-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

altoviz-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

Security Findings1

Severity	Finding	Layer	Location
HIGH	Potential Command Injection via RUBE_REMOTE_WORKBENCH The skill documentation recommends using `RUBE_REMOTE_WORKBENCH` for 'Bulk ops' with `run_composio_tool()`. The terms 'Remote Workbench' and 'Bulk ops' strongly suggest a capability for executing complex or potentially arbitrary operations. If `run_composio_tool()` allows for arbitrary code or command execution based on user-controlled input without sufficient sanitization or sandboxing, it could lead to command injection. The skill instructs the LLM to use this tool, which could involve passing user-controlled data to it, creating a credible exploit path if the underlying tool is not securely implemented. Ensure that `RUBE_REMOTE_WORKBENCH` and `run_composio_tool()` strictly validate and sanitize all inputs, operate within a secure, sandboxed environment, and do not allow arbitrary code or command execution. If arbitrary execution is an intended feature, clearly document its security implications, provide strong warnings, and implement robust access controls. The skill documentation should clarify the security model and input handling of this tool.	LLM	SKILL.md:80

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