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Security Audit

manus

github.com/openclaw/skills
AI SkillCommit 13146e6a3d46
65
CAUTION
Scanned about 1 month ago
3
Critical
Immediate action required
5
High
Priority fixes suggested
0
Medium
Best practices review
0
Low
Acknowledged / Tracked

Trust Assessment

manus received a trust score of 65/100, placing it in the Caution category. This skill has some security considerations that users should review before deployment.

SkillShield's automated analysis identified 8 findings: 3 critical, 5 high, 0 medium, and 0 low severity. Key findings include Unescaped JSON field in `curl -d` allows command injection, Unescaped `task_id` in URL allows command injection, Unescaped `output_dir` in `mkdir` allows arbitrary command execution.

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

Behavioral Risk Signals

Network Access
4 findings
Filesystem Write
4 findings
Shell Execution
7 findings
Dynamic Code
7 findings

Security Findings8

SeverityFindingLayerLocation

Scan History

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