Security Audit
honeyhive-automation
github.com/ComposioHQ/awesome-claude-skillsTrust Assessment
honeyhive-automation received a trust score of 82/100, 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 Dependency on external Rube MCP introduces supply chain risk, `RUBE_REMOTE_WORKBENCH` may allow arbitrary code execution.
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
Behavioral Risk Signals
Security Findings2
| Severity | Finding | Layer | Location | |
|---|---|---|---|---|
| HIGH | Dependency on external Rube MCP introduces supply chain risk The skill explicitly requires and integrates with the `rube` MCP, specifying `https://rube.app/mcp` as its server. This introduces a significant supply chain risk. The security and integrity of the external Rube MCP and its infrastructure are critical. A compromise of `rube.app`, vulnerabilities within the MCP itself, or a malicious update could directly impact the security of this skill and any systems it interacts with (e.g., Honeyhive), potentially leading to data breaches, unauthorized access, or command execution. Implement robust vetting processes for third-party MCPs and their providers. Continuously monitor the security posture and update channels of `rube.app`. Consider sandboxing or least-privilege execution environments for all MCP interactions to limit potential blast radius in case of compromise. Ensure secure communication (e.g., TLS) with the MCP server. | LLM | SKILL.md:1 | |
| MEDIUM | `RUBE_REMOTE_WORKBENCH` may allow arbitrary code execution The skill documentation mentions `RUBE_REMOTE_WORKBENCH` with `run_composio_tool()` as an approach for 'Bulk ops'. This implies a capability to execute code or complex operations within a remote workbench environment. If the `run_composio_tool()` function or the underlying workbench itself allows arbitrary code or shell command execution without strict input validation, sandboxing, and least-privilege principles, it could be abused for command injection or arbitrary code execution by a malicious actor or a compromised LLM. Ensure that `RUBE_REMOTE_WORKBENCH` and `run_composio_tool()` are designed with robust security controls, including strict input validation, command sanitization, secure sandboxing, and least-privilege execution. The skill documentation should clarify the security implications and usage guidelines for this powerful tool, explicitly stating any limitations on code execution or external access. | LLM | SKILL.md:70 |
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