Trust Assessment
jenkins 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 jobName 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 jobName in curl commands The skill defines `curl` commands that include a `{jobName}` placeholder. If an LLM fills this placeholder with unsanitized user input, it could allow an attacker to inject arbitrary shell commands. For example, if `{jobName}` is replaced with `myjob; rm -rf /`, the `rm -rf /` command would be executed on the host system, leading to remote code execution or data manipulation. The LLM implementation of this skill must ensure that any user-provided input used to fill placeholders like `{jobName}` is strictly validated and properly shell-escaped before being incorporated into the `curl` command. Using a library function that safely escapes shell arguments is recommended. | LLM | SKILL.md:17 |
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