qinghu-model-outfit-restore
Fail
Audited by Gen Agent Trust Hub on Sep 21, 2026
Risk Level: HIGHPERSISTENCEEXTERNAL_DOWNLOADSINDIRECT_PROMPT_INJECTIONCOMMAND_EXECUTION
Full Analysis
- [PERSISTENCE]: The skill provides instructions for the agent to modify the user's
~/.bashrcfile by appending anexportcommand. This ensures the Node.js binary path remains in thePATHenvironment variable across future shell sessions, which is necessary for the manually installed runtime to function correctly. - [EXTERNAL_DOWNLOADS]: The skill automates the retrieval of the Node.js binary and its SHA256 checksums from
nodejs.orgto facilitate environment bootstrapping when the runtime is missing. It also makes use ofregistry.npmmirror.comas a fallback registry for installing the required npm packages. Both sources are recognized as well-known and established services. - [INDIRECT_PROMPT_INJECTION]: The skill ingests untrusted data from external sources, which could lead to indirect prompt injection if the inputs contain adversarial instructions.
- Ingestion points: User-provided image file paths for '模特图' (model) and '替换衣物图' (clothing), and the '提示词' (prompt) field in
SKILL.md. - Boundary markers: None identified in the prompt interpolation logic to distinguish user data from system instructions.
- Capability inventory: The skill executes the
qhkitCLI tool, which interacts with the local file system and performs network operations to the vendor's API. - Sanitization: The skill utilizes a JSON parameter file (
params.json) to pass data to the CLI tool, which effectively mitigates shell command injection but does not prevent logic-based injection within the AI workflow. - [COMMAND_EXECUTION]: The skill's primary functionality is driven by executing the
qhkitCLI tool to perform various operations, including retrieving workflow options, calculating credit costs, and generating the final image output.
Recommendations
- HIGH: Downloads and executes remote code from: https://nodejs.org/dist/v22.22.3/SHASUMS256.txt - DO NOT USE without thorough review
Audit Metadata