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Security Audit

academic-research-hub

github.com/openclaw/skills
AI SkillCommit 13146e6a3d46
10
CRITICAL
Scanned 2 months ago
4
Critical
Immediate action required
1
High
Priority fixes suggested
7
Medium
Best practices review
6
Low
Acknowledged / Tracked

Trust Assessment

academic-research-hub 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 18 findings: 4 critical, 1 high, 7 medium, and 6 low severity. Key findings include Suspicious import: requests, Potential data exfiltration: file read + network send, Unpinned Python dependency version.

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 13, 2026 (commit 13146e6a). SkillShield performs automated 4-layer security analysis on AI skills and MCP servers.

Layer Breakdown

Manifest Analysis
100%
Static Code Analysis
78%
Dependency Graph
58%
LLM Behavioral Safety
0%

Behavioral Risk Signals

Network Access
3 findings
Filesystem Write
11 findings
Shell Execution
4 findings
Excessive Permissions
5 findings

Security Findings18

SeverityFindingLayerLocation

Scan History

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