Trust Assessment
hello received a trust score of 73/100, placing it in the Caution category. This skill has some security considerations that users should review before deployment.
SkillShield's automated analysis identified 1 finding: 1 critical, 0 high, 0 medium, and 0 low severity. Key findings include Untrusted content attempts to manipulate LLM instructions.
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 | |
|---|---|---|---|---|
| CRITICAL | Untrusted content attempts to manipulate LLM instructions The skill's `SKILL.md` file, which is marked as untrusted input, contains direct instructions intended for the host LLM. This attempts to override or influence the LLM's behavior, which is a form of prompt injection. The untrusted content dictates how the LLM should respond. Ensure that untrusted skill descriptions or manifest fields are sanitized or processed in a way that prevents them from being interpreted as instructions by the host LLM. Treat all content within untrusted delimiters as data, not commands. | LLM | SKILL.md:5 |
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