Trust Assessment
surveymonkey received a trust score of 90/100, placing it in the Trusted category. This skill has passed all critical security checks and demonstrates strong security practices.
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 provides `curl` command examples that directly embed variables like `{survey_id}` and `{page_id}` into the command string. If these variables are populated directly from untrusted user input by the LLM without proper sanitization (e.g., escaping shell metacharacters), it could lead to command injection, allowing an attacker to execute arbitrary commands on the host system. This pattern is visible in multiple `curl` examples where path parameters are interpolated. Implement robust input sanitization and validation for all user-provided variables (e.g., `survey_id`, `page_id`) before they are interpolated into shell commands. Ensure that shell metacharacters are properly escaped or that a safer method for constructing commands (e.g., using a dedicated API client library that handles escaping) is used by the LLM's execution environment. | LLM | SKILL.md:17 |
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