Security Audit
loomio-automation
github.com/ComposioHQ/awesome-claude-skillsTrust Assessment
loomio-automation received a trust score of 73/100, placing it in the Caution category. This skill has some security considerations that users should review before deployment.
SkillShield's automated analysis identified 1 finding: 1 critical, 0 high, 0 medium, and 0 low severity. Key findings include Potential for arbitrary tool 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
Behavioral Risk Signals
Security Findings1
| Severity | Finding | Layer | Location | |
|---|---|---|---|---|
| CRITICAL | Potential for arbitrary tool execution via RUBE_REMOTE_WORKBENCH The skill instructs the LLM to use `RUBE_REMOTE_WORKBENCH` for 'Bulk ops' with `run_composio_tool()`. This tool appears to allow the execution of arbitrary Composio tools by specifying a `tool_slug`. If the underlying Rube MCP or Composio ecosystem includes tools with broad system access (e.g., file system, network, shell commands), and an attacker can manipulate the `tool_slug` or `arguments` via prompt injection, this could lead to arbitrary command execution or access to sensitive resources outside the intended scope of Loomio automation. The skill does not enforce any restrictions on the `tool_slug` that can be passed to `run_composio_tool()`. Restrict the `tool_slug` parameter of `RUBE_REMOTE_WORKBENCH` to only allow Loomio-specific tools, or ensure the Rube MCP environment is strictly sandboxed to prevent unintended system access. Implement robust input validation and sanitization for `tool_slug` and `arguments` if they can be influenced by untrusted input. | LLM | SKILL.md:60 |
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