Security Audit

benzinga-automation

github.com/ComposioHQ/awesome-claude-skills

AI SkillCommit 27904475d127

TRUSTED

Scanned about 2 months ago

Critical

Immediate action required

High

Priority fixes suggested

Medium

Best practices review

Low

Acknowledged / Tracked

Trust Assessment

benzinga-automation received a trust score of 85/100, placing it in the Mostly Trusted category. This skill has passed most security checks with only minor considerations noted.

SkillShield's automated analysis identified 1 finding: 0 critical, 0 high, 1 medium, and 0 low severity. Key findings include Unpinned dependency in manifest.

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 20, 2026 (commit 27904475). 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

93%

Security Findings1

Severity	Finding	Layer	Location
MEDIUM	Unpinned dependency in manifest The 'rube' dependency within the 'mcp' ecosystem is specified without a version constraint in the skill's manifest. This can lead to non-deterministic builds, unexpected behavior if the dependency changes, and potential supply chain vulnerabilities if a malicious or incompatible version is introduced. It makes the skill susceptible to breaking changes or security issues in future versions of 'rube'. Pin the 'rube' dependency to a specific version or a version range (e.g., `"rube@1.2.3"` or `"rube@^1.0.0"`) in the manifest to ensure stability and security.	LLM	SKILL.md

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