Security Audit

scrapingbee-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

scrapingbee-automation received a trust score of 95/100, 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 Potential for Arbitrary Code Execution via RUBE_REMOTE_WORKBENCH.

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

Shell Execution

1 finding

Dynamic Code

1 finding

Excessive Permissions

1 finding

Security Findings1

Severity	Finding	Layer	Location
MEDIUM	Potential for Arbitrary Code Execution via RUBE_REMOTE_WORKBENCH The skill documentation mentions `RUBE_REMOTE_WORKBENCH` with `run_composio_tool()` for 'Bulk ops'. The term 'workbench' and 'run_tool' often imply capabilities for executing arbitrary code or commands. If `run_composio_tool()` allows execution of unconstrained code or shell commands, and its arguments can be influenced by untrusted input from the LLM or user, this could lead to command injection or excessive permissions, allowing an attacker to execute arbitrary code on the host system or interact with resources beyond the intended scope. The skill does not provide details on the security model or input sanitization for this specific tool. Clarify the exact capabilities and security implications of `RUBE_REMOTE_WORKBENCH` and `run_composio_tool()`. If it allows arbitrary code execution, implement strict input validation and sanitization for all arguments. Ensure the tool operates within a least-privilege environment and consider if such a powerful primitive should be directly exposed to an LLM agent without additional guardrails or human-in-the-loop approval for sensitive operations.	LLM	SKILL.md:70

Scan History

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