Breaking Bot: Hacking and Defending LLM-Based Applications
The article discusses the vulnerabilities of Large Language Models (LLMs) and how they can be exploited. It highlights various methods used to bypass safety protocols, including Adversarial Prompting and encoding techniques. The piece emphasizes the importance of resilient design in AI applications to prevent catastrophic failures after a breach.
- ▪Large Language Models can be tricked into revealing harmful information despite safety protocols.
- ▪Techniques like Adversarial Prompting and encoding requests can bypass LLM safety filters.
- ▪Hackers can use mathematical triggers embedded in images to override model safety protocols.
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Breaking Bot: Hacking & Defending LLM-based ApplicationsMarton Antal SzelDec 24, 202512 min readUpdated: 4 days agoCover Photo: Breaking Bad's title image modified by GeminiLet's say your "super-intelligent" agentic chatbot - the one with access to sensitive customer data - is hijacked. You've effectively welcomed a genius-level saboteur behind your own defense lines.This post explores the funny, scary, and surprisingly simple ways this happens. Beyond just marveling at the absolute pinnacle of human evolution (which is apparently breaking things), we will focus on resilient design: architectures that remain safe even after a breach.
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