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Security Audit

moltiverse-among

github.com/openclaw/skills
AI SkillCommit 13146e6a3d46
10
CRITICAL
Scanned 2 months ago
17
Critical
Immediate action required
1
High
Priority fixes suggested
0
Medium
Best practices review
0
Low
Acknowledged / Tracked

Trust Assessment

moltiverse-among 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 18 findings: 17 critical, 1 high, 0 medium, and 0 low severity. Key findings include Network egress to untrusted endpoints, Wallet Address and Agent Name Sent Over Unencrypted HTTP, Hardcoded Unencrypted API Endpoint Poses Significant Supply Chain Risk.

The analysis covered 4 layers: Manifest Analysis, Static Code Analysis, Dependency Graph, LLM Behavioral Safety. The Manifest Analysis layer scored lowest at 0/100, indicating areas for improvement.

Last analyzed on February 13, 2026 (commit 13146e6a). SkillShield performs automated 4-layer security analysis on AI skills and MCP servers.

Layer Breakdown

Manifest Analysis
0%
Static Code Analysis
100%
Dependency Graph
100%
LLM Behavioral Safety
55%

Behavioral Risk Signals

Network Access
18 findings

Security Findings18

SeverityFindingLayerLocation

Scan History

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