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Security Audit

agentos-mesh

github.com/openclaw/skills
AI SkillCommit 13146e6a3d46
10
CRITICAL
Scanned 3 months ago
3
Critical
Immediate action required
0
High
Priority fixes suggested
4
Medium
Best practices review
1
Low
Acknowledged / Tracked

Trust Assessment

agentos-mesh 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 8 findings: 3 critical, 0 high, 4 medium, and 1 low severity. Key findings include Persistence / self-modification instructions, Network egress to untrusted endpoints, Missing required field: name.

The analysis covered 4 layers: Manifest Analysis, Static Code Analysis, Dependency Graph, LLM Behavioral Safety. The Manifest Analysis layer scored lowest at 40/100, indicating areas for improvement.

Last analyzed on February 12, 2026 (commit 13146e6a). SkillShield performs automated 4-layer security analysis on AI skills and MCP servers.

Layer Breakdown

Manifest Analysis
40%
Static Code Analysis
72%
Dependency Graph
100%
LLM Behavioral Safety
68%

Behavioral Risk Signals

Network Access
2 findings
Filesystem Write
3 findings
Shell Execution
6 findings
Dynamic Code
1 finding
Excessive Permissions
2 findings

Security Findings8

SeverityFindingLayerLocation

Scan History

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