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Security Audit

deep-research

github.com/openclaw/skills
AI SkillCommit 13146e6a3d46
10
CRITICAL
Scanned about 2 months ago
1
Critical
Immediate action required
6
High
Priority fixes suggested
0
Medium
Best practices review
1
Low
Acknowledged / Tracked

Trust Assessment

deep-research 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 8 findings: 1 critical, 6 high, 0 medium, and 1 low severity. Key findings include Sensitive path access: AI agent config, Untrusted input embedded directly into LLM-facing output, Arbitrary web content fetching and inclusion in report.

The analysis covered 4 layers: Manifest Analysis, Static Code Analysis, Dependency Graph, LLM Behavioral Safety. The Static Code Analysis layer scored lowest at 25/100, 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
25%
Dependency Graph
100%
LLM Behavioral Safety
53%

Behavioral Risk Signals

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

Security Findings8

SeverityFindingLayerLocation

Scan History

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