Trust Assessment
loom received a trust score of 86/100, 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 user input in `curl` commands.
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 13, 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 user input in `curl` commands The skill defines `curl` commands that include placeholders such as `{video_id}`. If an LLM directly substitutes untrusted user input into these placeholders without proper sanitization or escaping, it could lead to arbitrary command execution on the host system. An attacker could inject shell commands (e.g., `123; rm -rf /`) into the `{video_id}` parameter, leading to data loss, system compromise, or credential exfiltration. This risk applies to all `curl` commands in the skill that use such placeholders. The LLM execution environment must ensure that any user-provided input used to fill placeholders in shell commands is properly sanitized and escaped to prevent command injection. For example, by quoting the variable or using a dedicated API client that handles parameterization securely instead of raw shell commands. | LLM | SKILL.md:20 |
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