Security Audit

hig-components-system

github.com/sickn33/antigravity-awesome-skills

AI SkillCommit e36d6fd3b3f6

CAUTION

Scanned about 2 months ago

Critical

Immediate action required

High

Priority fixes suggested

Medium

Best practices review

Low

Acknowledged / Tracked

Trust Assessment

hig-components-system received a trust score of 72/100, placing it in the Caution category. This skill has some security considerations that users should review before deployment.

SkillShield's automated analysis identified 1 finding: 1 critical, 0 high, 0 medium, and 0 low severity. Key findings include Prompt injection attempting local file system access and behavior modification.

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

70%

Behavioral Risk Signals

Filesystem Write

1 finding

Dynamic Code

1 finding

Excessive Permissions

1 finding

Security Findings1

Severity	Finding	Layer	Location
CRITICAL	Prompt injection attempting local file system access and behavior modification The skill contains an instruction within the untrusted input block that attempts to manipulate the host LLM's behavior. Specifically, it instructs the LLM to 'Check for `.claude/apple-design-context.md` before asking questions.' This is a direct prompt injection attempt designed to make the LLM interact with the local file system (checking for a specific file) and modify its subsequent conversational flow (conditional questioning). This could lead to information disclosure (e.g., confirming file existence or reading its content) and unauthorized modification of the LLM's operational instructions, bypassing its intended safety mechanisms. Remove or sanitize instructions that attempt to control the LLM's internal behavior or access local files from untrusted input. If file access is genuinely needed, it should be explicitly defined as a tool call with appropriate permissions and sandboxing, not as a direct instruction to the LLM.	LLM	SKILL.md:5

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