Security Audit

api-labz-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

api-labz-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 2 findings: 0 critical, 1 high, 1 medium, and 0 low severity. Key findings include Undocumented powerful tool 'RUBE_REMOTE_WORKBENCH', Broad tool access via dynamic discovery.

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

Filesystem Write

1 finding

Shell Execution

2 findings

Dynamic Code

2 findings

Excessive Permissions

2 findings

Security Findings2

Severity	Finding	Layer	Location
HIGH	Undocumented powerful tool 'RUBE_REMOTE_WORKBENCH' The skill documentation mentions `RUBE_REMOTE_WORKBENCH` for 'Bulk ops' using `run_composio_tool()`. This tool's capabilities are not detailed, but its name and purpose ('remote workbench', 'bulk ops') strongly suggest it could execute arbitrary or highly privileged operations. If `run_composio_tool()` allows shell commands, file system access, or other powerful actions, and its arguments can be influenced by user input via the LLM, it poses a significant risk for command injection and data exfiltration. The lack of specific examples or warnings for such a powerful tool is concerning. Provide detailed documentation for `RUBE_REMOTE_WORKBENCH` and `run_composio_tool()`, including its exact capabilities, security implications, and how to safely constrain its use. If it allows arbitrary code execution, explicitly warn users and provide strong sanitization guidelines for inputs.	LLM	SKILL.md:46
MEDIUM	Broad tool access via dynamic discovery The skill instructs the LLM to dynamically discover and execute tools via `RUBE_SEARCH_TOOLS` and `RUBE_MULTI_EXECUTE_TOOL`. This pattern allows the LLM to access and execute any operation available through the `api_labz` connection. If the underlying `api_labz` connection is configured with excessive permissions, a compromised LLM could exploit this broad access to perform unauthorized actions or access sensitive data. The skill does not provide mechanisms to restrict the scope of tools the LLM can discover or execute. Recommend users configure `api_labz` connections with the principle of least privilege. Add guidance within the skill documentation on how to constrain `RUBE_SEARCH_TOOLS` queries or `RUBE_MULTI_EXECUTE_TOOL` executions to a specific, limited set of operations if possible, or warn about the implications of broad access.	LLM	SKILL.md:29

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