Security Audit

tapfiliate-automation

github.com/ComposioHQ/awesome-claude-skills

AI SkillCommit 99e2a2951545

TRUSTED

Scanned 6 days ago

Critical

Immediate action required

High

Priority fixes suggested

Medium

Best practices review

Low

Acknowledged / Tracked

Trust Assessment

tapfiliate-automation received a trust score of 94/100, placing it in the Trusted category. This skill has passed all critical security checks and demonstrates strong security practices.

SkillShield's automated analysis identified 1 finding: 0 critical, 0 high, 1 medium, and 0 low severity. Key findings include Broad access to external tools via Rube MCP.

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 17, 2026 (commit 99e2a295). 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%

Behavioral Risk Signals

Network Access

1 finding

Shell Execution

1 finding

Dynamic Code

1 finding

Excessive Permissions

1 finding

Security Findings1

Severity	Finding	Layer	Location
MEDIUM	Broad access to external tools via Rube MCP The skill provides an interface to Rube MCP, allowing the LLM to discover and execute a wide range of operations on Tapfiliate via `RUBE_MULTI_EXECUTE_TOOL`. The documentation explicitly states 'Always search first: Tool schemas change. Never hardcode tool slugs or arguments without calling `RUBE_SEARCH_TOOLS`', indicating dynamic and potentially unrestricted tool execution. Furthermore, the mention of `RUBE_REMOTE_WORKBENCH` with `run_composio_tool()` suggests the capability to execute arbitrary Composio tools, potentially extending beyond Tapfiliate. This grants the LLM broad permissions to interact with external services, limited primarily by the permissions of the connected accounts to those services, rather than by granular restrictions within the skill's definition. Implement granular access controls within the Rube MCP or the platform hosting the LLM to restrict the specific Tapfiliate operations or Composio tools that the LLM is permitted to execute. Ensure the connected Tapfiliate account operates with the principle of least privilege. If the platform supports it, define more specific tool access within the skill's manifest rather than relying on dynamic discovery and execution of all available tools.	LLM	SKILL.md:49

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