Security Audit

renderform-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

renderform-automation received a trust score of 82/100, 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_REMOTE_WORKBENCH, Unpinned Dependency in Manifest.

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

Network Access

1 finding

Shell Execution

1 finding

Excessive Permissions

1 finding

Security Findings2

Severity	Finding	Layer	Location
HIGH	Unpinned Dependency in Manifest The skill's manifest specifies a dependency on 'rube' (`"mcp": ["rube"]`) without a version constraint. This means the skill will always fetch the latest available version of 'rube'. If a future version of 'rube' introduces breaking changes, security vulnerabilities, or malicious code, the skill would automatically incorporate it, leading to instability or compromise without explicit review. This is a significant supply chain risk. Pin the 'rube' dependency to a specific, known-good version (e.g., `"mcp": ["rube@1.2.3"]`) to ensure deterministic behavior and prevent unexpected issues from upstream changes. Regularly review and manually update dependencies to newer versions after verifying their integrity and compatibility.	LLM	SKILL.md:2
MEDIUM	Broad Tool Execution Capability via RUBE_REMOTE_WORKBENCH The skill documentation describes the use of `RUBE_REMOTE_WORKBENCH` with `run_composio_tool()` for 'Bulk ops'. This suggests a highly flexible and potentially broad execution capability for any Composio tool. While not a direct vulnerability in the skill's code, this capability grants an agent extensive permissions to interact with Renderform and potentially other services integrated via Composio. A compromised agent could leverage this to perform arbitrary actions, leading to data manipulation, unauthorized access, or service disruption. Review the scope of actions permitted by `RUBE_REMOTE_WORKBENCH` and `run_composio_tool()`. If possible, restrict the types of operations or specific tools that can be executed via this mechanism. Implement granular access controls or require explicit user confirmation for sensitive 'bulk operations'.	LLM	SKILL.md:78

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