Trust Assessment
webflow received a trust score of 88/100, placing it in the Mostly Trusted category. This skill has passed most security checks with only minor considerations noted.
SkillShield's automated analysis identified 1 finding: 0 critical, 1 high, 0 medium, and 0 low severity. Key findings include Potential Command Injection via Unsanitized Placeholders.
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 12, 2026 (commit 13146e6a). SkillShield performs automated 4-layer security analysis on AI skills and MCP servers.
Layer Breakdown
Behavioral Risk Signals
Security Findings1
| Severity | Finding | Layer | Location | |
|---|---|---|---|---|
| HIGH | Potential Command Injection via Unsanitized Placeholders The skill documentation provides `curl` command examples that include dynamic placeholders such as `{site_id}`, `{collection_id}`, `{form_id}`, and fields within the JSON body for `Create CMS Item`. If an LLM directly interpolates user-provided input into these placeholders without proper sanitization or escaping of shell metacharacters, it could lead to command injection. An attacker could craft malicious input (e.g., `123; rm -rf /; echo `) that, when executed, performs arbitrary commands on the host system. When constructing and executing shell commands based on user input, ensure all dynamic parts (like IDs, URLs, or JSON content) are rigorously sanitized and shell-escaped. It is highly recommended to use a robust HTTP client library in a programming language (e.g., `requests` in Python) instead of direct `subprocess.run('curl ...')` calls, as these libraries typically handle parameter encoding and request construction more securely. If shell execution is unavoidable, use language-specific functions like `shlex.quote()` in Python to escape arguments. | LLM | SKILL.md:16 |
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