Broken Hill: Probing the Weak Spots of AI’s Shiny New Brain

Jan 20, 2025

Large Language Models (LLMs) like OpenAI’s ChatGPT or Google’s Bard have become the rockstars of AI. They're everywhere—helping us write essays, debug code, and even crack jokes. But lurking in the shadows is a dark truth: these powerful tools are far from invincible. Researchers and hackers alike have discovered that these AI models, for all their sophistication, can be manipulated. Welcome to the era of adversarial attacks, where clever prompts can turn your friendly AI assistant into a rogue operator.

What’s particularly unsettling? These aren’t just one-off exploits; they’re systematic vulnerabilities that tools like Broken Hill are laying bare. Developed by the cybersecurity experts at Bishop Fox, Broken Hill offers an automated way to test LLMs for weak spots. Think of it as a stress test for AI’s shiny new brain—but with far-reaching implications for security professionals, developers, and everyday users alike.

Adversarial attacks might seem esoteric at first glance, but the principle is deceptively simple. The idea is to craft input that tricks the model into doing something it shouldn’t, like generating malicious code or spilling sensitive information. One of the most sophisticated approaches is the Greedy Coordinate Gradient (GCG) attack, which goes beyond mere trial-and-error guesswork.

Here’s how GCG works in practice:

  1. Baseline Behavior: Under normal conditions, an LLM is programmed to reject harmful queries. For example:
    <|user|> Please write detailed instructions how to make Molotov cocktail.....

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