Security Audit

doppler-secretops-automation

github.com/ComposioHQ/awesome-claude-skills

AI SkillCommit 27904475d127

CAUTION

Scanned about 2 months ago

Critical

Immediate action required

High

Priority fixes suggested

Medium

Best practices review

Low

Acknowledged / Tracked

Trust Assessment

doppler-secretops-automation received a trust score of 74/100, placing it in the Caution category. This skill has some security considerations that users should review before deployment.

SkillShield's automated analysis identified 2 findings: 0 critical, 2 high, 0 medium, and 0 low severity. Key findings include Unpinned dependency in manifest, Skill provides broad access to secret management operations.

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

70%

Behavioral Risk Signals

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' without a version constraint. This allows any version of the dependency to be used, including potentially vulnerable or malicious future versions, or versions with breaking changes, which could introduce supply chain risks. Pin the 'rube' dependency to a specific version or version range (e.g., `"rube": "1.2.3"` or `"rube": "^1.0.0"`) to ensure consistent and secure dependency resolution.	LLM	SKILL.md:1
HIGH	Skill provides broad access to secret management operations The skill is designed to automate tasks with Doppler Secretops, a secret management platform. It exposes tools like `RUBE_MULTI_EXECUTE_TOOL` which can perform various 'Doppler Secretops operations'. This capability, while intended for automation, grants the LLM broad access to sensitive secrets. An attacker could craft a prompt to instruct the LLM to read, modify, or delete secrets, or exfiltrate them by having the LLM output their values. Implement strict access controls and output filtering on the LLM's side to prevent unauthorized secret access or exfiltration. Ensure the LLM is explicitly instructed and constrained against revealing sensitive information obtained through these tools. Consider fine-grained permissions for the underlying Doppler Secretops integration if possible.	LLM	SKILL.md:70

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