How I Built an AI That Breeds Its Own Jailbreaks Using Genetic Algorithms

Static jailbreak lists are dead. Every time a model provider patches their safety filters, your entire payload library becomes obsolete. Manual red teaming doesn't scale. And most AI security tools are just payload databases with a UI. So I built something different. The Problem I tested 6 major LLM deployments last year. Every single one had a bypass within 5 prompts. The problem isn't that LLMs are insecure — it's how the industry tests them. Most red teaming today looks like this: Copy a jailbreak from a GitHub list Paste it into the target If it works, report it If it doesn't, try the next one That's not security testing. That's pattern matching. And it stops working the moment the model gets patched. The Idea What if adversarial prompts could evolve? Not manually crafted. Not randomly generated. Actually evolved — like organisms under selection pressure. The strong prompts survive. The weak ones die. The survivors mutate and reproduce. Each generation gets better at bypassing the