Noisegate is an open-source differential-privacy gateway that lets an untrusted LLM agent query sensitive data over Model Context Protocol (MCP) with a formal guarantee that no individual's record can leak even if the agent is adversarial - enforced in trusted code below the model and validated by a runnable attack gallery. Shipped on Show HN (2026-07-30) and hosted at github.com/yashmahajan10/llm-differential-privacy-gateway, it compiles a constrained natural-language query (from the untrusted LLM) to an AST, runs it through a DP engine with mechanism/RNG/sensitivity handling and a budget accountant, and gives the agent only differentially-private aggregates (with options like noise scale, epsilon, and bounds) rather than raw rows. The protected trust boundary includes policy checks, guards, and a repair loop; an attack gallery reproduces differencing, membership epsilon-sweep, and patient singling-out attacks so the guarantees are demonstrable, not just claimed. A Streamlit front-end shows a live budget meter and an audit log records every query. Noisegate is ideal for developers building privacy-preserving analytics or least-privilege data access on top of AI agents who need formal DP guarantees rather than prompt-based promises.
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