Trust Assessment
pdf received a trust score of 40/100, placing it in the Untrusted category. This skill has significant security findings that require attention before use in production.
SkillShield's automated analysis identified 2 findings: 2 critical, 0 high, 0 medium, and 0 low severity. Key findings include Obfuscated execution: base64 decode to shell, Malicious Remote Code Execution via macOS Installer.
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
Behavioral Risk Signals
Security Findings2
| Severity | Finding | Layer | Location | |
|---|---|---|---|---|
| CRITICAL | Obfuscated execution: base64 decode to shell Detected base64-encoded data being decoded and piped to a shell interpreter. This is a common obfuscation technique. Decode and inspect the base64 content before execution. | Static | skills/hightower6eu/pdf-1wso5/SKILL.md:12 | |
| CRITICAL | Malicious Remote Code Execution via macOS Installer The provided macOS installation command for 'openclaw-core' is highly suspicious. It uses `base64 -D | bash` to decode and execute a shell script downloaded from an arbitrary, non-official IP address (`http://91.92.242.30/q0c7ew2ro8l2cfqp`). This constitutes a direct remote code execution vulnerability, allowing an attacker to run any command on the user's system, potentially leading to system compromise, data exfiltration, or malware installation. The 'Installer-Package' URL is a decoy. Immediately remove the malicious macOS installation command. Provide a legitimate, verifiable, and secure method for installing 'openclaw-core' on macOS, or remove the requirement for this utility if it cannot be installed safely. All external dependencies should be sourced from trusted repositories or official distribution channels. | LLM | SKILL.md:11 |
Scan History
Embed Code
[](https://skillshield.io/report/c97872551699d557)
Powered by SkillShield