building-inferencesh-apps
Fail
Audited by Gen Agent Trust Hub on Aug 7, 2026
Risk Level: HIGHREMOTE_CODE_EXECUTIONEXTERNAL_DOWNLOADSCOMMAND_EXECUTIONPROMPT_INJECTION
Full Analysis
- [REMOTE_CODE_EXECUTION]: The skill provides instructions to download and execute remote scripts directly in the shell using
curl | shand similar patterns. - Evidence:
curl -fsSL https://cli.inference.sh | shinSKILL.mdandreferences/cli.md. - Evidence:
curl -LsSf https://astral.sh/uv/install.sh | shinreferences/cli.md. - Evidence:
curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.40.0/install.sh | bashinreferences/cli.md. - [COMMAND_EXECUTION]: The skill recommends bypassing Windows PowerShell execution policies to run an installation script from the internet.
- Evidence:
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"inreferences/cli.md. - [EXTERNAL_DOWNLOADS]: The skill fetches resources from external domains that are not part of the established trusted vendor list.
- Evidence: Fetches tools and scripts from
inference.sh,astral.sh, andraw.githubusercontent.com(fornvm-sh). - [PROMPT_INJECTION]: The application templates provided in the skill create a surface for indirect prompt injection by ingesting untrusted user input and passing it directly to AI models without boundary markers or sanitization.
- Ingestion points:
input_data.promptininference.py(Python) andRunInputininference.js(Node.js). - Boundary markers: None present in the code templates to delimit user content.
- Capability inventory: The apps have capabilities including network access via
httpx, file system access in/tmp, and access to platform-managed secrets. - Sanitization: No sanitization or validation of the input strings is implemented in the provided examples.
Recommendations
- HIGH: Downloads and executes remote code from: https://cli.inference.sh - DO NOT USE without thorough review
Audit Metadata