Security Audit
zenserp-automation
github.com/ComposioHQ/awesome-claude-skillsTrust Assessment
zenserp-automation received a trust score of 95/100, placing it in the Trusted category. This skill has passed all critical security checks and demonstrates strong security practices.
SkillShield's automated analysis identified 1 finding: 0 critical, 0 high, 1 medium, and 0 low severity. Key findings include Skill enables broad tool execution capabilities.
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 Findings1
| Severity | Finding | Layer | Location | |
|---|---|---|---|---|
| MEDIUM | Skill enables broad tool execution capabilities The skill documentation describes the use of `RUBE_MULTI_EXECUTE_TOOL` and `RUBE_REMOTE_WORKBENCH`. These tools allow the LLM to execute arbitrary tools within the Rube/Composio ecosystem, not just Zenserp-specific operations. This grants overly broad tool access, which, if misused by a compromised or misaligned LLM, could lead to unauthorized actions across various integrated services. While the documentation encourages best practices like searching for schemas, the underlying capability is very powerful and generic, presenting a risk if the LLM is prompted to perform malicious or unintended actions. Consider providing more granular tools that are specific to Zenserp operations rather than generic tool execution mechanisms like `RUBE_MULTI_EXECUTE_TOOL` or `RUBE_REMOTE_WORKBENCH`. If generic execution is necessary, ensure robust guardrails are in place at the LLM and platform level to prevent misuse, and consider restricting the scope of tools discoverable or executable by this specific skill. | LLM | SKILL.md:49 |
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