Security Audit

labarchive-integration

github.com/davila7/claude-code-templates

AI SkillCommit 458b11867eae

CAUTION

Scanned about 2 months ago

Critical

Immediate action required

High

Priority fixes suggested

Medium

Best practices review

Low

Acknowledged / Tracked

Trust Assessment

labarchive-integration received a trust score of 53/100, placing it in the Caution category. This skill has some security considerations that users should review before deployment.

SkillShield's automated analysis identified 5 findings: 0 critical, 2 high, 2 medium, and 1 low severity. Key findings include Suspicious import: requests, Potential data exfiltration: file read + network send, Network egress to untrusted endpoints.

The analysis covered 4 layers: Manifest Analysis, Static Code Analysis, Dependency Graph, LLM Behavioral Safety. The Static Code Analysis layer scored lowest at 63/100, indicating areas for improvement.

Last analyzed on February 12, 2026 (commit 458b1186). SkillShield performs automated 4-layer security analysis on AI skills and MCP servers.

Layer Breakdown

Manifest Analysis

91%

Static Code Analysis

63%

Dependency Graph

100%

LLM Behavioral Safety

100%

Behavioral Risk Signals

Network Access

4 findings

Filesystem Write

1 finding

Excessive Permissions

1 finding

Security Findings5

Severity	Finding	Layer	Location
HIGH	Potential data exfiltration: file read + network send Function 'upload_attachment' reads files and sends data over the network. This may indicate data exfiltration. Review this function to ensure file contents are not being sent to external servers.	Static	cli-tool/components/skills/scientific/labarchive-integration/scripts/entry_operations.py:161
HIGH	Unpinned Git Repository Dependency The skill instructs users to clone a Git repository (`https://github.com/mcmero/labarchives-py`) and install it directly using `uv pip install .`. This method does not pin a specific version, commit hash, or tag. If the upstream repository is compromised or changes maliciously, subsequent installations could introduce vulnerabilities or backdoors into the user's environment without their explicit knowledge or consent. This is a significant supply chain risk. Pin the dependency to a specific commit hash or version tag. For example, instruct users to clone a specific commit or use a package manager that supports version pinning (e.g., `pip install labarchives-py==X.Y.Z` if available on PyPI, or `pip install git+https://github.com/mcmero/labarchives-py@<commit_hash>`). Regularly audit the upstream repository for security vulnerabilities.	Static	SKILL.md:204
MEDIUM	Suspicious import: requests Import of 'requests' detected. This module provides network or low-level system access. Verify this import is necessary. Network and system modules in skill code may indicate data exfiltration.	Static	cli-tool/components/skills/scientific/labarchive-integration/scripts/entry_operations.py:147
MEDIUM	Network egress to untrusted endpoints HTTP request to raw IP address Review all outbound network calls. Remove connections to webhook collectors, paste sites, and raw IP addresses. Legitimate API calls should use well-known service domains.	Manifest	cli-tool/components/mcps/devtools/figma-dev-mode.json:4
LOW	Covert behavior / concealment directives Multiple zero-width characters (stealth text) Remove hidden instructions, zero-width characters, and bidirectional overrides. Skill instructions should be fully visible and transparent to users.	Manifest	cli-tool/components/mcps/devtools/jfrog.json:4

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