Trust Assessment
audio-reply 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 Potential Command Injection via unsanitized text in `uv run`, Potential Server-Side Request Forgery (SSRF) via WebFetch.
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 | |
|---|---|---|---|---|
| HIGH | Potential Command Injection via unsanitized text in `uv run` The skill's design describes executing `uv run mlx_audio.tts.generate` with a `--text` argument. In 'Mode 1: Read URL Content', this text is derived from content fetched from a user-provided URL. If the extracted text is directly interpolated into the shell command without proper sanitization or escaping of shell metacharacters (e.g., backticks, semicolons, or dollar signs), a malicious user could inject arbitrary commands, leading to remote code execution. The `SKILL.md` explicitly illustrates this pattern of embedding potentially untrusted text into a shell command. Ensure all text derived from user input or external sources (like fetched URLs) is rigorously sanitized and escaped before being passed as an argument to a shell command. Prefer using a safe command execution library or API that handles argument quoting automatically, or explicitly use functions like `shlex.quote()` in Python to prevent shell injection. | LLM | SKILL.md:39 | |
| HIGH | Potential Server-Side Request Forgery (SSRF) via WebFetch The skill's design describes fetching content from user-provided URLs using a `WebFetch` tool. If the `WebFetch` implementation does not include strict input validation and access controls, a malicious user could provide internal IP addresses, local file paths, or sensitive internal domains. This could lead to Server-Side Request Forgery (SSRF), allowing an attacker to scan internal networks, access sensitive internal services, or exfiltrate data from the host environment. Implement strict input validation for all URLs passed to `WebFetch`. This should include an allow-list for permitted domains and protocols, and explicit blocking of private IP ranges (e.g., 127.0.0.1, 10.0.0.0/8, 172.16.0.0/12, 192.168.0.0/16). Ensure `WebFetch` operates within a sandboxed environment with minimal network and filesystem permissions. | LLM | SKILL.md:25 |
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