Security Audit

chaser-automation

github.com/ComposioHQ/awesome-claude-skills

AI SkillCommit 27904475d127

TRUSTED

Scanned about 2 months ago

Critical

Immediate action required

High

Priority fixes suggested

Medium

Best practices review

Low

Acknowledged / Tracked

Trust Assessment

chaser-automation received a trust score of 85/100, placing it in the Mostly Trusted category. This skill has passed most security checks with only minor considerations noted.

SkillShield's automated analysis identified 1 finding: 0 critical, 1 high, 0 medium, and 0 low severity. Key findings include Excessive Permissions 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 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

85%

Behavioral Risk Signals

Network Access

1 finding

Filesystem Write

1 finding

Shell Execution

1 finding

Dynamic Code

1 finding

Excessive Permissions

1 finding

Security Findings1

Severity	Finding	Layer	Location
HIGH	Excessive Permissions via RUBE_REMOTE_WORKBENCH The skill's 'Quick Reference' section suggests using `RUBE_REMOTE_WORKBENCH` with `run_composio_tool()` for 'Bulk ops'. This implies the ability to execute any tool available within the Composio ecosystem via Rube MCP. Without explicit constraints on which Composio tools can be run, or a clear understanding of the full scope of operations these tools can perform (e.g., file system access, arbitrary network requests, environment variable access), this grants excessive permissions to the LLM. This broad access could be exploited for data exfiltration, unauthorized actions, or command injection if any underlying Composio tool has such capabilities. 1. Restrict `RUBE_REMOTE_WORKBENCH` usage: If `RUBE_REMOTE_WORKBENCH` is necessary, explicitly define and enforce a whitelist of allowed `composio_tool()` calls and their parameters. 2. Granular Permissions: Implement more granular permissions within the Rube MCP or Composio system to limit the scope of tools accessible to this specific skill. 3. Documentation: Clearly document the security implications and capabilities of `RUBE_REMOTE_WORKBENCH` and `run_composio_tool()` for users and the LLM. 4. Avoid generic execution: Prefer specific tool calls (like `RUBE_MULTI_EXECUTE_TOOL` with specific slugs) over generic execution mechanisms like `RUBE_REMOTE_WORKBENCH` for routine operations.	LLM	SKILL.md:80

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