Developer builds sub-2ms LLM security proxy to block PII leaks

By PulseAugur Editorial · [1 sources] · 2026-06-21 12:17

A developer built an open-source security proxy called Tamga to protect against sensitive data exposure when using large language models. The proxy, written in Go, sits between applications and LLM providers like OpenAI and Anthropic, scanning prompts for personally identifiable information (PII) and enforcing security policies in under 2 milliseconds. The developer detailed the architecture and shared that 29 out of 62 adversarial test vectors were still able to bypass the implemented scanners, highlighting the ongoing challenges in securing LLM interactions. AI

IMPACT This tool addresses a critical security gap for organizations using LLMs with sensitive data, potentially influencing how enterprises implement AI.

RANK_REASON The item describes a developer-created tool for LLM security, not a release from a major AI lab or a significant industry event.

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AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Developer builds sub-2ms LLM security proxy to block PII leaks

COVERAGE [1]

dev.to — LLM tag TIER_1 English(EN) · yatuk · 2026-06-21 12:17

Building a sub-millisecond LLM security proxy in Go — lessons from 62 adversarial vectors

<blockquote> TL;DR — I spent 6 months building <a href="https://github.com/yatuk/tamga" rel="noopener noreferrer">Tamga</a>, an open-source reverse proxy that sits between your application and LLM providers (OpenAI, Anthropic, Azure) and enforces…

COVERAGE [1]

Building a sub-millisecond LLM security proxy in Go — lessons from 62 adversarial vectors

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