constructive-ai

No explicit malicious payload (e.g., exfiltration, reverse shells, persistence, or data theft routines) is present in the schema/function logic; the PL/pgSQL code is focused on chunking and similarity search. However, the script contains multiple high-impact execution and supply-chain risk factors: it uses eval on the output of pgpm env (turning external tool output into arbitrary shell execution), it starts Docker images based on an environment-controlled image reference without digest pinning/allowlisting, and it interpolates environment-derived identifiers into SQL without quoting/validation. It also prints default database credentials in plaintext connection details.

BENIGN. The code fragment implements a focused, well-structured client library for interacting with a locally hosted Ollama server, covering embeddings, generation (including streaming), chat, and model discovery. No evident credential harvesting or data leakage is present in the code path. Operational risks relate to deployment practices (secure host exposure, trusted model sources) rather than code-level vulnerabilities.