using-vector-databases
Pass
Audited by Gen Agent Trust Hub on Sep 17, 2026
Risk Level: SAFEINDIRECT_PROMPT_INJECTIONREMOTE_CODE_EXECUTIONEXTERNAL_DOWNLOADS
Full Analysis
- [INDIRECT_PROMPT_INJECTION]: The RAG implementation in
examples/qdrant-python/rag_pipeline.py(lines 122-140) demonstrates a standard Retrieval-Augmented Generation pattern where retrieved context is interpolated into an LLM prompt. This constitutes a vulnerability surface for indirect prompt injection if the source documents contain malicious instructions. \n - Ingestion points: Data enters the context via the
generate_answermethod inrag_pipeline.py, which retrieves chunks from the Qdrant collection.\n - Boundary markers: The implementation uses basic text headers ("Context:", "Question:", "Answer:") without robust delimiters or instructions to ignore embedded commands.\n
- Capability inventory: The pipeline has network access to the OpenAI API and the local Qdrant database, and the FastAPI application allows remote query submission.\n
- Sanitization: No sanitization or escaping of the retrieved document content is performed before interpolation.\n- [REMOTE_CODE_EXECUTION]: The
examples/rust-axum-vector/README.mdfile contains a command to install the Rust toolchain viacurl ... | shtargetingsh.rustup.rs. This is the official and trusted installation method for the Rust language infrastructure and is considered safe.\n- [EXTERNAL_DOWNLOADS]: The skill documentation and example projects reference and download dependencies from trusted package registries (npm and PyPI). These include well-known libraries such asqdrant-client,langchain, andopenai. All external references target reputable organizations and official service endpoints.
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