we have a year to fix security everywhere

GLM 5.3-flash released last week, and that means Project Glasswing and Daybreak are running out of time. Cheap models capable of dangerous hacking are now available to anyone, without the normal safeguards for refusing malicious actions. We need to fix vulnerabilities across the industry so that we aren't caught unawares. And for one of the first times in computing history, we have the ability to! We can use frontier LLMs that move faster than a human to find and fix these issues in the time we have left. The hard remaining part is deploying the fixes. This probably sounds like nonsense words or hysterical overreacting to most people, so here's what that means: "GLM" is a kind of LLM (AI). The GLM family is open-weight, which means anyone can download and run the models. "flash" means that it is cheap and fast to run, compared to most "frontier" models. "cheap" is relative, but think around 5-15k USD in hardware to run it locally. "frontier" here means that the LLM is "close to the frontier of what AI is currently able to achieve". Project Glasswing and Daybreak are initiatives to use LLMs to fix security issues across the tech industry. "malicious actions" includes things like hacking infrastructure and telling people how to build pipe bombs. The rest of this post is about what makes me so sure this is an imminent threat, and what we can do in response. GLM GLM 5.3-flash can be downloaded and modified by anyone in the world. The GLM ("General Language Model") family is developed by Z.ai Co. (formerly Zhipu AI), which is a Chinese AI lab. When the model is hosted by Z.ai, it comes with restrictions required by law: Z.ai releases its models publicly on the internet ("open-weight" models). Once it does so, organizations such as DeAlignAI release "abliterated" models with their task refusals surgically removed. DealignAI says the abliterated model scores 0% on Harmbench-320, which tests whether models refuse to complete tasks about disinformation, cybercrime, biological weapons, and other illegal acts such as building a pipe bomb. In other words, this model is willing to do basically anything. Flash GLM 5.3-flash is possible to run locally on stock consumer hardware. "Flash" is mostly an advertising term—it's relative to other models, not a specific technical approach. Various people online have run benchmarks of GLM 5.3-flash locally. Here's one example showing around 20 tokens/second on a ~6k USD NVIDIA GPU. On September 22, Apple is releasing the M5 Mac Studio with 256 GB of unified memory. "Unified memory" means it can be shared between the host operating system and the GPU. That's more than enough to run 5.3-flash, and it will probably get around 30 tokens/second once it releases. For 256 GB, the price starts at around $9,500. Further improvements in software can get half-again the throughput through changes to the model decoder. If we extrapolate that to the M5, that would put the total throughput at around 45 tokens/second. 45 tokens/second is enough to write this snippet of code in 3 seconds: ⚠ LLM generated code from pathlib import Path import hashlib def digest(path: Path) -> str: hasher = hashlib.sha256() with path.open("rb") as file: while chunk := file.read(1024 * 1024): hasher.update(chunk) return hasher.hexdigest() def main() -> None: import sys if len(sys.argv) < 2: raise SystemExit("usage: hash.py FILE...")