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Security Audit

llm-shield

github.com/openclaw/skills
AI SkillCommit 13146e6a3d46
88
TRUSTED
Scanned about 2 months ago
0
Critical
Immediate action required
0
High
Priority fixes suggested
1
Medium
Best practices review
2
Low
Acknowledged / Tracked

Trust Assessment

llm-shield received a trust score of 88/100, placing it in the Mostly Trusted category. This skill has passed most security checks with only minor considerations noted.

SkillShield's automated analysis identified 4 findings: 0 critical, 0 high, 1 medium, and 2 low severity. Key findings include Potential hardcoded secret (high entropy), Node lockfile missing, User message content sent to third-party API.

The analysis covered 4 layers: Manifest Analysis, Static Code Analysis, Dependency Graph, LLM Behavioral Safety. All layers scored 70 or above, reflecting consistent security practices.

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
93%
Dependency Graph
98%
LLM Behavioral Safety
98%

Behavioral Risk Signals

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

Security Findings4

SeverityFindingLayerLocation

Scan History

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