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Security Audit

manus

github.com/openclaw/skills
AI SkillCommit 13146e6a3d46
19
CRITICAL
Scanned about 2 months ago
0
Critical
Immediate action required
2
High
Priority fixes suggested
7
Medium
Best practices review
1
Low
Acknowledged / Tracked

Trust Assessment

manus received a trust score of 19/100, placing it in the Untrusted category. This skill has significant security findings that require attention before use in production.

SkillShield's automated analysis identified 10 findings: 0 critical, 2 high, 7 medium, and 1 low severity. Key findings include Suspicious import: requests, Potential data exfiltration: file read + network send, Sensitive path access: AI agent config.

The analysis covered 4 layers: Manifest Analysis, Static Code Analysis, Dependency Graph, LLM Behavioral Safety. The Static Code Analysis layer scored lowest at 28/100, indicating areas for improvement.

Last analyzed on February 14, 2026 (commit 13146e6a). SkillShield performs automated 4-layer security analysis on AI skills and MCP servers.

Layer Breakdown

Manifest Analysis
100%
Static Code Analysis
28%
Dependency Graph
98%
LLM Behavioral Safety
93%

Behavioral Risk Signals

Network Access
7 findings
Filesystem Write
4 findings
Shell Execution
1 finding
Excessive Permissions
8 findings

Security Findings10

SeverityFindingLayerLocation

Scan History

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