Security Audit

retellai-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

retellai-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 access to Retellai operations via Rube MCP, 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 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

78%

Behavioral Risk Signals

Shell Execution

1 finding

Excessive Permissions

1 finding

Security Findings2

Severity	Finding	Layer	Location
HIGH	Broad access to Retellai operations via Rube MCP The skill instructs the LLM to use `RUBE_MULTI_EXECUTE_TOOL` and `RUBE_REMOTE_WORKBENCH` which allow for executing a wide range of operations on Retellai via the Composio Rube MCP. This grants the AI agent broad, potentially unrestricted, access to Retellai functionalities, including those that could lead to data modification or deletion, depending on the underlying tool capabilities. While this is the intended function of an automation skill, it represents a significant permission scope that could be misused if the LLM is compromised or given malicious instructions. Implement granular access controls within the Rube MCP or Retellai integration to limit the scope of actions the AI agent can perform. Ensure the LLM's prompts are carefully constrained to prevent misuse of these broad capabilities.	LLM	SKILL.md:48
MEDIUM	Unpinned Rube MCP dependency The skill's manifest declares a dependency on the 'rube' MCP (`"mcp": ["rube"]`) without specifying a version. This unpinned dependency means that any future changes or malicious updates to the `rube` MCP could be automatically incorporated, potentially introducing vulnerabilities or unexpected behavior without explicit review or control. Pin the `rube` MCP dependency to a specific, known-good version in the skill's manifest to ensure consistent and secure behavior. Regularly review and update pinned dependencies.	LLM	SKILL.md:1

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