Security Audit

taggun-automation

github.com/ComposioHQ/awesome-claude-skills

AI SkillCommit 27904475d127

TRUSTED

Scanned 4 months ago

Critical

Immediate action required

High

Priority fixes suggested

Medium

Best practices review

Low

Acknowledged / Tracked

Trust Assessment

taggun-automation received a trust score of 80/100, 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, 1 high, 1 medium, and 0 low severity. Key findings include Broad Tool Execution Capability via Rube MCP, Unpinned Rube MCP Dependency.

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

78%

Behavioral Risk Signals

Shell Execution

1 finding

Dynamic Code

1 finding

Security Findings2

Severity	Finding	Layer	Location
HIGH	Broad Tool Execution Capability via Rube MCP The skill instructs the LLM to use `RUBE_SEARCH_TOOLS` to dynamically discover available tools and then `RUBE_MULTI_EXECUTE_TOOL` or `RUBE_REMOTE_WORKBENCH` to execute them. This pattern grants the LLM broad, dynamic execution capabilities within the Taggun ecosystem (and potentially other toolkits if Rube MCP is configured for them). If the LLM is compromised or misinterprets user intent, it could execute sensitive or destructive operations via the Taggun tools without explicit, pre-defined constraints. The `RUBE_MULTI_EXECUTE_TOOL` allows for arbitrary arguments based on discovered schemas, increasing the potential attack surface. Implement stricter access controls or approval mechanisms for sensitive operations. Consider limiting the scope of tools discoverable or executable by the LLM, or requiring human confirmation for high-impact actions. Ensure the underlying Taggun tools have granular permissions and robust input validation.	LLM	SKILL.md:67
MEDIUM	Unpinned Rube MCP Dependency The skill's manifest specifies a dependency on the `rube` MCP without a specific version (`"mcp": ["rube"]`). This means that any future updates to the `rube` MCP could introduce breaking changes, new vulnerabilities, or altered behavior without explicit review or consent, potentially impacting the skill's functionality or security posture. This is a supply chain risk. Pin the `rube` MCP dependency to a specific, known-good version in the skill's manifest to ensure stability and security. Regularly review and update the pinned version to incorporate necessary security patches and features.	LLM	SKILL.md:1

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