ai-rag-pipeline
Pass
Audited by Gen Agent Trust Hub on May 14, 2026
Risk Level: SAFEEXTERNAL_DOWNLOADSCOMMAND_EXECUTIONPROMPT_INJECTION
Full Analysis
- [EXTERNAL_DOWNLOADS]: Fetches installation instructions from the inference-sh GitHub repository, which is the official documentation source for the toolset described.
- [COMMAND_EXECUTION]: Provides instructions to install the platform's CLI tool using
npx skills add belt-sh/cli, which is a standard procedure for using this skill's ecosystem. - [PROMPT_INJECTION]: Describes a Retrieval Augmented Generation (RAG) workflow that ingests external data into LLM prompts. This architecture creates an indirect prompt injection surface common to research-oriented AI agents.
- Ingestion points: Search results and extracted web content are stored in shell variables (e.g., $SEARCH, $CONTENT) and interpolated into LLM prompts in the provided examples.
- Boundary markers: The examples employ basic labels like 'Source 1' but do not include explicit delimiters or instructions to ignore embedded commands within the retrieved text.
- Capability inventory: The skill uses the
beltCLI to perform web searches and execute inferences on various LLM models. - Sanitization: The provided pipeline examples do not demonstrate filtering or sanitization of search results before they are processed by the LLM.
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