Just How Resilient Are Large Language Models?

Summary

Picture this: you're holding a device containing billions of precisely calibrated numbers, each one crucial to its operation. Now imagine a cosmic ray streaks through the atmosphere, passes through your roof, through your computer, and flips a single bit in one of those numbers. Now imagine that the device is Large Language Model - what happens next? Most likely, nothing at all. This isn't science fiction. Cosmic rays flip bits in computer memory all the time (Cosmic Rays are something I worried about a lot when first launching Sigstore's Transparency Log), and yet when impacting large language models running on servers around the world, they continue to function perfectly. The reason why reveals something really interesting about the similarities between artificial neural networks and biological brains. The Architecture of Redundancy When we think about precision engineering, we usually imagine systems where every component matters. Remove one gear from a Swiss watch, and it stops ticking. Change one line of code in a program, and it might crash entirely. But neural networks operate on entirely different principles, and understanding why requires us to peek inside the mathematical machinery that powers modern AI. A large language model like GPT-5 contains somewhere between hundreds of billions and trillions of parameters. These aren't just storage slots for data, they're the learned connections between artificial neurons, each one encoding a tiny fragment of knowledge about language, reasoning, and the patterns hidden in human communication. When you ask a model to complete a sentence or solve a problem, you're watching these billions of numbers collaborate in ways that even their creators don't fully understand. But here's the fascinating part: most of these parameters aren't irreplaceable specialists. They're more like members of a vast crowd, where losing any individual voice barely affects the overall conversation. When Numbers Go Wrong To understand just how r...