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Security Audit

deep-research

github.com/openclaw/skills
AI SkillCommit 13146e6a3d46
65
CAUTION
Scanned 2 months ago
1
Critical
Immediate action required
3
High
Priority fixes suggested
1
Medium
Best practices review
1
Low
Acknowledged / Tracked

Trust Assessment

deep-research 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 6 findings: 1 critical, 3 high, 1 medium, and 1 low severity. Key findings include External Command Execution via MCP Configuration, Credential Exposure via API Key in Configuration, Data Exfiltration to Public Tunnel Service.

The analysis covered 4 layers: Manifest Analysis, Static Code Analysis, Dependency Graph, LLM Behavioral Safety. The LLM Behavioral Safety layer scored lowest at 16/100, 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
16%

Behavioral Risk Signals

Network Access
5 findings
Filesystem Write
1 finding
Shell Execution
1 finding
Dynamic Code
1 finding
Excessive Permissions
1 finding

Security Findings6

SeverityFindingLayerLocation

Scan History

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