Security Audit

typless-automation

github.com/ComposioHQ/awesome-claude-skills

AI SkillCommit 99e2a2951545

TRUSTED

Scanned about 2 months ago

Critical

Immediate action required

High

Priority fixes suggested

Medium

Best practices review

Low

Acknowledged / Tracked

Trust Assessment

typless-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 Dynamic Tool Execution with Broad Permissions, 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 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

78%

Behavioral Risk Signals

Shell Execution

1 finding

Dynamic Code

1 finding

Excessive Permissions

1 finding

Security Findings2

Severity	Finding	Layer	Location
HIGH	Dynamic Tool Execution with Broad Permissions The skill instructs the LLM to use `RUBE_MULTI_EXECUTE_TOOL` and `RUBE_REMOTE_WORKBENCH`. These tools allow the LLM to dynamically discover and execute arbitrary operations provided by the Typless toolkit via Rube MCP. This grants the LLM broad, unconstrained execution capabilities. A malicious user prompt could leverage these tools to perform unintended actions, potentially leading to data manipulation, unauthorized access, or command injection if the underlying Typless tools expose such functionalities. The `RUBE_MULTI_EXECUTE_TOOL` allows execution of any `TOOL_SLUG_FROM_SEARCH` with schema-compliant arguments, and `RUBE_REMOTE_WORKBENCH` implies a general execution environment for Composio tools. Restrict the scope of tools the LLM can execute. If dynamic execution is necessary, implement strict allow-lists for tool slugs and argument schemas. Ensure the underlying Rube/Typless tools are sandboxed and have minimal necessary permissions. Consider adding human-in-the-loop approval for sensitive operations.	LLM	SKILL.md:50
MEDIUM	Unpinned Rube MCP Dependency The skill's manifest specifies a dependency on 'rube' for the MCP without a version constraint (`"mcp": ["rube"]`). This means that any version of the Rube MCP could be used, including future versions that might introduce vulnerabilities, breaking changes, or malicious code. This exposes the skill to supply chain risks, as updates to the 'rube' MCP could inadvertently or intentionally compromise the skill's security or functionality. Pin the dependency to a specific, known-good version of the Rube MCP (e.g., `"mcp": ["rube@1.2.3"]`) to ensure consistent and secure behavior. Regularly review and update pinned dependencies.	LLM	Manifest (frontmatter JSON)

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