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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
1
High
Priority fixes suggested
1
Medium
Best practices review
0
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 3 findings: 1 critical, 1 high, 1 medium, and 0 low severity. Key findings include Arbitrary Code Execution via Obfuscated Shell Command in Setup Instructions, Insecure Communication Channel via Public Tunneling Service in MCP Configuration, Untrusted Binary Download in Windows Setup Instructions.

The analysis covered 4 layers: Manifest Analysis, Static Code Analysis, Dependency Graph, LLM Behavioral Safety. The LLM Behavioral Safety layer scored lowest at 48/100, 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
48%

Behavioral Risk Signals

Network Access
1 finding
Filesystem Write
1 finding
Shell Execution
2 findings
Dynamic Code
1 finding

Security Findings3

SeverityFindingLayerLocation

Scan History

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