Observed Signal · Jun 19, 2026 · Technical Release · Source: DEV Community · Impact: 2/5 · Sentiment: Positive
Weekend-built PII Firewall Blocks LLM Data Leaks
An author built and open-sourced a pre-request governance stack that prevents personally identifiable information (PII) from being sent to LLM providers. Motivated by an incident where a real credit card number was accidentally sent to GPT-4o during benchmarking, the project implements a FastAPI enforcement dependency that scans prompts with Microsoft Presidio before any model call, evaluates YAML-defined policies (block/warn/alert), and short-circuits requests (HTTP 403) when blocking rules fire. The system logs every inference to a PostgreSQL audit vault, sends CloudEvents-compatible webhook alerts (Slack/Teams/PagerDuty), and supports multi-provider routing (OpenAI, Groq, Google Gemini, Anthropic, local Ollama). Code is published on GitHub (sochaty/llm-governance-engine) and the stack runs via docker compose.
Open-source technical governance for LLM privacy and pre-call enforcement is practically useful for teams handling sensitive data, but it is not a major platform policy change or industry-shifting announcement.
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Key Takeaways & Evidence Grounding
- Author accidentally sent real customer PII (credit card) to OpenAI's GPT-4o while benchmarking and then built a firewall to prevent recurrence.
- The firewall scans prompts before model calls using Microsoft Presidio (local execution) and produces per-entity confidence scores.
- Governance is driven by a YAML policy DSL with rules that can block (HTTP 403), warn, or alert based on conditions like pii_detected and safety_score_below.
- Every inference and policy violation is audited to a PostgreSQL vault (pii flag, safety score, cost, latency) and webhook alerts are sent via CloudEvents payloads.
- The orchestrator supports multiple providers (OpenAI, Groq, Google Gemini, Anthropic, Ollama local) and stores API keys encrypted in PostgreSQL; the project is available on GitHub (governance-post-1 tag) and runnable with docker compose.
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LLM Gateway Proxy with Security and Observability
A developer built an open LLM Gateway Proxy that sits between client applications and the OpenAI API to centralize security, compliance, and observability. The gateway applies layered checks — PII sanitization, heuristic prompt-injection detection, and response validation — before forwarding safe requests to the model. It records request-level metrics (latency, token usage, estimated cost) to a CSV ledger and exposes an interactive Streamlit dashboard for an experimental playground and operational metrics. The project is containerized with Docker and includes a GitHub Actions CI workflow; the full source code is published on GitHub. The author outlines trade-offs and future improvements including NER-based PII detection, embedding-based semantic guardrails, caching, persistent storage, distributed tracing, and production-grade monitoring.
Red‑teaming an LLM security gateway: four‑pass findings
The author describes building and red‑teaming a transparent OpenAI‑compatible LLM security gateway that inspects requests and responses for leaked secrets, PII, jailbreaks, prompt injection and exfiltration. Over four iterative passes (ingress evasion, harder request techniques, response/egress, and streaming egress) the author cataloged detection gaps, implemented fixes and validated benign‑guard tests to avoid false positives. Key fixes include Unicode tag‑character normalization, intent‑gated exfil rules, reuse of request‑side secret format rules on egress, an opt‑in RESPONSE_BLOCK mode that strips/blocks leaked content, and a rolling-window SSE streaming scanner that blocks fragmented streamed secrets. The article is explicit about remaining limitations (regex limits, streaming cannot retract already-streamed prefixes, domain‑list maintenance, and that this does not solve prompt injection architecture issues). The gateway repo is published under Apache‑2.0.
Deterministic Tokenization Preserves LLM Output; NoPII Launched
The author ran 109 structured tests across GPT-4o, Claude 3.5 Sonnet, and Gemini 1.5 Pro to measure how PII protection methods affect LLM output quality. They compared raw prompts, generic placeholder masking (e.g., [PERSON]), and deterministic opaque tokenization (unique tokens per entity). Tokenization preserved 91–96% of raw output quality while placeholder masking reduced quality to 54–68%, with steep drops in entity consistency and reasoning coherence for multi-entity prompts. Tests also revealed a safety refusal mode: models declined ~15–20% of tokenized prompts when labels like “SSN” remained next to opaque tokens; replacing labels (context phrase neutralization) resolved the refusals. Based on the research, the team built NoPII, a reverse-proxy privacy layer that tokenizes PII in transit, detokenizes responses, supports streaming, uses a PCI Level 1 / SOC2 vault, and integrates with major LLM providers with one base_url change.
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