Trust Assessment
ez-unifi received a trust score of 86/100, 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 Path Traversal in QR Code Output.
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 13, 2026 (commit 13146e6a). 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 | Path Traversal in QR Code Output The `wlan-qr` command allows a user-controlled output filename (`-o` or `--output`) to be passed directly to `qr_code.save()`. Without proper sanitization, a malicious user could specify a path traversal sequence (e.g., `../../../../tmp/malicious.png`) to write files to arbitrary locations on the host system, potentially leading to data corruption, denial of service, or privilege escalation if combined with other vulnerabilities. Sanitize the `args.output` path to ensure it is contained within an intended and secure directory (e.g., a temporary directory or a dedicated output folder). Use functions like `os.path.abspath` combined with `os.path.commonprefix` or `pathlib.Path.resolve()` to validate that the resolved path remains within the allowed boundaries. | LLM | scripts/unifi.py:1000 |
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