Security Audit

google-drive

github.com/openclaw/skills

AI SkillCommit 13146e6a3d46

CAUTION

Scanned 2 months ago

Critical

Immediate action required

High

Priority fixes suggested

Medium

Best practices review

Low

Acknowledged / Tracked

Trust Assessment

google-drive received a trust score of 73/100, placing it in the Caution category. This skill has some security considerations that users should review before deployment.

SkillShield's automated analysis identified 1 finding: 1 critical, 0 high, 0 medium, and 0 low severity. Key findings include Potential Command Injection via unsanitized curl arguments.

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 14, 2026 (commit 13146e6a). 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

Network Access

1 finding

Filesystem Write

1 finding

Shell Execution

1 finding

Dynamic Code

1 finding

Security Findings1

Severity	Finding	Layer	Location
CRITICAL	Potential Command Injection via unsanitized curl arguments The skill defines several `curl` commands that use placeholders (e.g., `{fileId}`, `name`, `emailAddress`, `localfile.txt`). If these placeholders are populated directly from untrusted user input without proper sanitization or escaping, an attacker could inject arbitrary shell commands. For example, providing `{fileId}` as `12345; rm -rf /` could lead to arbitrary code execution on the host system. Similarly, if the output filename for `-o` (e.g., `downloaded_file.pdf`) is user-controlled, it could lead to path traversal or command injection. If `file=@localfile.txt` allows user-controlled `localfile.txt`, it could lead to arbitrary file reading. Implement robust input sanitization and escaping for all dynamic values interpolated into shell commands. Use functions like `shlex.quote` (Python) or equivalent for shell arguments, or pass arguments as a list to `subprocess.run` to avoid shell interpretation. Ensure that filenames for `-o` are also sanitized to prevent path traversal or command injection. For JSON payloads, ensure proper JSON escaping.	LLM	SKILL.md:24

Scan History

Embed Code

[![SkillShield](https://skillshield.io/api/v1/badge/f858627ed8a2e42f.svg)](https://skillshield.io/report/f858627ed8a2e42f)