Security Audit
flowiseai-automation
github.com/ComposioHQ/awesome-claude-skillsTrust Assessment
flowiseai-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, 1 high, 0 medium, and 0 low severity. Key findings include Potential Command Injection 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 | |
|---|---|---|---|---|
| HIGH | Potential Command Injection via RUBE_REMOTE_WORKBENCH The skill documentation mentions `RUBE_REMOTE_WORKBENCH` with `run_composio_tool()` for 'Bulk ops'. The term 'workbench' and the function name `run_composio_tool()` strongly suggest an environment capable of executing arbitrary code, scripts, or powerful commands. If `run_composio_tool()` allows for the execution of arbitrary commands or scripts based on user-controlled input, it presents a significant command injection vulnerability. An attacker could craft malicious input to the LLM that, when passed to `run_composio_tool()`, could lead to unauthorized code execution on the host system or within the Composio environment. Clarify the exact capabilities and security model of `RUBE_REMOTE_WORKBENCH` and `run_composio_tool()`. Ensure that `run_composio_tool()` does not allow arbitrary code execution or shell commands. Implement strict input validation and sandboxing for any executed operations. If it's intended for script execution, clearly document the language and available APIs, and ensure it operates within a secure, least-privilege environment. | LLM | SKILL.md:80 |
Scan History
Embed Code
[](https://skillshield.io/report/2569647bbcab1efc)
Powered by SkillShield