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LLM security gateway red-teamed, revealing and fixing 15 vulnerabilities

The author of a security gateway for LLM traffic conducted a four-pass red-teaming exercise to identify and fix vulnerabilities. The gateway, which acts as an OpenAI-compatible proxy, scans requests and responses for sensitive information, PII, and prompt injection attempts using deterministic pattern detection. While acknowledging that regex-based filtering is not a foolproof solution, the author detailed specific gaps found, including ASCII smuggling via Unicode tag characters and false positives on legitimate security questions. Fixes were implemented with accompanying tests to ensure they did not block valid user queries, with some issues noted as unresolvable by regex alone. AI

IMPACT Highlights the ongoing challenges in securing LLM interactions and the limitations of deterministic pattern detection.

RANK_REASON Author red-teams their own LLM security gateway product.

Read on dev.to — LLM tag →

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

LLM security gateway red-teamed, revealing and fixing 15 vulnerabilities

COVERAGE [1]

  1. dev.to — LLM tag TIER_1 English(EN) · akavlabs ·

    I red-teamed my own LLM security gateway in four passes. Here's every gap I found.

    <p>I build a security gateway for LLM traffic. It sits in front of the model as a transparent OpenAI-compatible proxy — one env var, <code>OPENAI_BASE_URL</code> — and scans every request (and response) for leaked secrets, PII, jailbreaks, prompt injection, and exfiltration attem…