Trust Assessment
renderful-ai 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 user input in Bash(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 user input in Bash(curl) commands The skill documentation demonstrates `curl` commands where user-provided values for fields like `prompt`, `model`, and `task_id` are intended to be embedded within shell commands. If the skill's implementation constructs these `curl` commands by directly interpolating user input into a shell command string without proper shell or JSON escaping, a malicious user could inject shell metacharacters. This could lead to arbitrary command execution on the host system. For example, injecting `", "evil": "value" }' ; rm -rf / ; #` into the `prompt` field, or `abc' ; rm -rf / ; #` into the `task_id` path parameter, could break the command structure and execute arbitrary commands. Ensure all user-provided input, especially for fields like `prompt`, `negative_prompt`, `model`, `task_id`, etc., is properly escaped before being embedded into shell commands or JSON strings. For shell commands, use a library function that correctly escapes arguments (e.g., `shlex.quote` in Python). For JSON payloads, ensure the input is treated as a string and properly JSON-escaped before being included in the JSON structure. Alternatively, use a `curl` library in the skill's language that handles argument construction safely instead of direct shell execution. | LLM | SKILL.md:22 |
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