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Security Audit

lingzhu

github.com/openclaw/skills
AI SkillCommit 5acc56773e07
10
CRITICAL
Scanned 3 months ago
3
Critical
Immediate action required
5
High
Priority fixes suggested
0
Medium
Best practices review
3
Low
Acknowledged / Tracked

Trust Assessment

lingzhu received a trust score of 10/100, placing it in the Untrusted category. This skill has significant security findings that require attention before use in production.

SkillShield's automated analysis identified 11 findings: 3 critical, 5 high, 0 medium, and 3 low severity. Key findings include Network egress to untrusted endpoints, Node lockfile missing, Undeclared `commander` dependency.

The analysis covered 4 layers: Manifest Analysis, Static Code Analysis, Dependency Graph, LLM Behavioral Safety. The Manifest Analysis layer scored lowest at 10/100, indicating areas for improvement.

Last analyzed on February 12, 2026 (commit 5acc5677). SkillShield performs automated 4-layer security analysis on AI skills and MCP servers.

Layer Breakdown

Manifest Analysis
10%
Static Code Analysis
100%
Dependency Graph
96%
LLM Behavioral Safety
23%

Behavioral Risk Signals

Network Access
3 findings
Filesystem Write
7 findings
Shell Execution
4 findings
Dynamic Code
2 findings
Excessive Permissions
4 findings

Security Findings11

SeverityFindingLayerLocation

Scan History

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