Security Audit

azure-eventhub-ts

github.com/sickn33/antigravity-awesome-skills

AI SkillCommit e36d6fd3b3f6

TRUSTED

Scanned 1 day ago

Critical

Immediate action required

High

Priority fixes suggested

Medium

Best practices review

Low

Acknowledged / Tracked

Trust Assessment

azure-eventhub-ts 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 2 findings: 0 critical, 0 high, 1 medium, and 1 low severity. Key findings include Unpinned npm dependencies, Potential sensitive data logging in event processing.

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 e36d6fd3). 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

91%

Behavioral Risk Signals

Shell Execution

2 findings

Excessive Permissions

1 finding

Security Findings2

Severity	Finding	Layer	Location
MEDIUM	Unpinned npm dependencies The `npm install` commands specify package names without version pinning (e.g., `@azure/event-hubs` instead of `@azure/event-hubs@^5.0.0`). This can lead to non-deterministic builds and introduce vulnerabilities if a new version of a dependency contains malicious code or critical bugs, as the latest version will always be pulled. This is a common supply chain risk. Pin all dependencies to specific versions or use a lock file (e.g., `package-lock.json`) to ensure deterministic builds. For example, `npm install @azure/event-hubs@^5.0.0 @azure/identity@^3.0.0`.	LLM	SKILL.md:10
LOW	Potential sensitive data logging in event processing The `processEvents` callback logs the entire `event.body` using `console.log(JSON.stringify(event.body))`. If the event body contains sensitive information (e.g., PII, financial data, secrets), this could lead to data exfiltration through logs, which might be accessible to unauthorized personnel or systems. This occurs in both simple and checkpointed receive examples. Avoid logging raw event bodies in production environments. Instead, log only necessary metadata or sanitized versions of the event body. Implement robust log management and access control policies to protect sensitive data.	LLM	SKILL.md:55

Scan History

Embed Code

[![SkillShield](https://skillshield.io/api/v1/badge/79b38477c2c525d5.svg)](https://skillshield.io/report/79b38477c2c525d5)