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Security Audit

canvas

github.com/openclaw/openclaw
AI SkillCommit b62bd290cb4e
36
CRITICAL
Scanned 2 months ago
1
Critical
Immediate action required
2
High
Priority fixes suggested
1
Medium
Best practices review
0
Low
Acknowledged / Tracked

Trust Assessment

canvas received a trust score of 36/100, placing it in the Untrusted category. This skill has significant security findings that require attention before use in production.

SkillShield's automated analysis identified 4 findings: 1 critical, 2 high, 1 medium, and 0 low severity. Key findings include Network egress to untrusted endpoints, Missing required field: name, Arbitrary URL Loading via 'present' and 'navigate' actions.

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 18, 2026 (commit b62bd290). SkillShield performs automated 4-layer security analysis on AI skills and MCP servers.

Layer Breakdown

Manifest Analysis
70%
Static Code Analysis
93%
Dependency Graph
100%
LLM Behavioral Safety
70%

Behavioral Risk Signals

Network Access
2 findings
Shell Execution
1 finding
Dynamic Code
1 finding
Excessive Permissions
1 finding

Security Findings4

SeverityFindingLayerLocation

Scan History

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