Security Audit
securitytrails-automation
github.com/ComposioHQ/awesome-claude-skillsTrust Assessment
securitytrails-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, 0 high, 1 medium, and 0 low severity. Key findings include Generic Tool Execution via Rube MCP allows broad access.
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 | |
|---|---|---|---|---|
| MEDIUM | Generic Tool Execution via Rube MCP allows broad access The skill leverages Rube MCP's generic tool execution capabilities (`RUBE_SEARCH_TOOLS`, `RUBE_MULTI_EXECUTE_TOOL`, `RUBE_REMOTE_WORKBENCH`). While the skill's stated purpose is 'Securitytrails Automation', these tools allow the LLM to discover and execute any tool available through the connected Rube MCP instance. This broad access could be exploited by a malicious prompt to execute unintended operations beyond Securitytrails, depending on the other tools exposed by Rube MCP. If possible, restrict the scope of `RUBE_SEARCH_TOOLS` and `RUBE_MULTI_EXECUTE_TOOL` to only Securitytrails-specific tools or a predefined allowlist within the Rube MCP configuration for this skill. Implement strict input validation and output sanitization for all tool arguments and results. Ensure the LLM's execution environment is sandboxed and has minimal necessary permissions. | LLM | SKILL.md:33 |
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