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Security Audit

meshguard

github.com/openclaw/skills
AI SkillCommit 9c1b8e80763e
51
CAUTION
Scanned about 2 months ago
1
Critical
Immediate action required
2
High
Priority fixes suggested
3
Medium
Best practices review
0
Low
Acknowledged / Tracked

Trust Assessment

meshguard received a trust score of 51/100, placing it in the Caution category. This skill has some security considerations that users should review before deployment.

SkillShield's automated analysis identified 6 findings: 1 critical, 2 high, 3 medium, and 0 low severity. Key findings include Sensitive environment variable access: $HOME, Command Injection via unsanitized user input in config file, Command Injection via unsanitized 'id' parameters in CLI commands.

The analysis covered 4 layers: Manifest Analysis, Static Code Analysis, Dependency Graph, LLM Behavioral Safety. The LLM Behavioral Safety layer scored lowest at 33/100, indicating areas for improvement.

Last analyzed on February 12, 2026 (commit 9c1b8e80). SkillShield performs automated 4-layer security analysis on AI skills and MCP servers.

Layer Breakdown

Manifest Analysis
100%
Static Code Analysis
86%
Dependency Graph
100%
LLM Behavioral Safety
33%

Behavioral Risk Signals

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

Security Findings6

SeverityFindingLayerLocation

Scan History

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