Security Audit

autobound-automation

github.com/ComposioHQ/awesome-claude-skills

AI SkillCommit 99e2a2951545

TRUSTED

Scanned 2 months ago

Critical

Immediate action required

High

Priority fixes suggested

Medium

Best practices review

Low

Acknowledged / Tracked

Trust Assessment

autobound-automation received a trust score of 78/100, 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 Unpinned Rube MCP dependency, Skill enables broad access to Rube MCP capabilities.

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

78%

Dependency Graph

100%

LLM Behavioral Safety

100%

Behavioral Risk Signals

Shell Execution

1 finding

Excessive Permissions

1 finding

Security Findings2

Severity	Finding	Layer	Location
HIGH	Unpinned Rube MCP dependency The skill's manifest specifies a dependency on the 'rube' MCP without a version constraint. This means that any future version of the 'rube' MCP, including potentially malicious or vulnerable updates, could be automatically used by the skill, introducing a supply chain risk. Pin the 'rube' MCP dependency to a specific, known-good version (e.g., "mcp": ["rube@1.2.3"]) or use a version range with a lower bound.	Static	SKILL.md:1
MEDIUM	Skill enables broad access to Rube MCP capabilities The skill's manifest requires the general 'rube' MCP, which is a powerful tool execution engine. While the skill's documentation focuses on 'Autobound automation', the underlying Rube MCP, if not configured with fine-grained access controls, could allow a compromised LLM to interact with any connected Composio toolkit, not just Autobound. This grants broader permissions than strictly necessary for the stated purpose. If possible within the Composio ecosystem, specify a more granular permission scope for the Rube MCP (e.g., limiting it to only the 'autobound' toolkit). Alternatively, ensure robust LLM security and prompt injection defenses are in place to prevent misuse of the broad Rube capabilities. The skill itself could also explicitly state that it only intends to use the `autobound` toolkit and that any other usage would be out of scope.	Static	SKILL.md:1

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