Vinh Nguyen, a former chief data scientist at the NSA, said the WeChat worm was one of the most troubling and potentially severe cyberattacks he had ever seen, capable of reaching hundreds of millions of devices within hours. nytimes.com/2026/09/08/us/po…

During my time at @QED_Audit I circled back and took another quick look at browser/JSE vulnerability research. AI agents flying high, seemingly finding & exploiting bugs everywhere, and there's this: a **constant integer overflow** in V8, alive for over 3.5 years. (1/5)

Root cause of Google Chrome V8 ITW (CVE-2026-11645)[506689381]: TryFastAddDataProperty[1] needs a fast MAP[5]. But "after" checking[2], Map::PrepareForDataProperty[3] manages to normalize the map. The patch[4] rechecks for slow dictionary MAP afterwards. chromium-review.googlesource…

seems twitter missed the ExploitBench paper? few observations: we finally got good data on Mythos security capabilities and it's very impressive. Mythos got full exploit chain on 18/41 v8 n-days, while gpt 5.5 only got 1 and open source models are mostly useless.

📣📢 Calling all Android and Chrome bug hunters 🧑‍💻🔎! We're updating our Android & Chrome VRP programs to ensure we can continue to reward the most challenging and impactful vulnerabilities researchers find in our products. For details, 👇 bughunters.google.com/blog/e…

I packaged up the "autoresearch" project into a new self-contained minimal repo if people would like to play over the weekend. It's basically nanochat LLM training core stripped down to a single-GPU, one file version of ~630 lines of code, then: - the human iterates on the prompt (.md) - the AI agent iterates on the training code (.py) The goal is to engineer your agents to make the fastest research progress indefinitely and without any of your own involvement. In the image, every dot is a complete LLM training run that lasts exactly 5 minutes. The agent works in an autonomous loop on a git feature branch and accumulates git commits to the training script as it finds better settings (of lower validation loss by the end) of the neural network architecture, the optimizer, all the hyperparameters, etc. You can imagine comparing the research progress of different prompts, different agents, etc. github.com/karpathy/autorese… Part code, part sci-fi, and a pinch of psychosis :)

My experience on how to play with openclaw Managing AI agents is a management problem, not a prompt engineering problem. atum.li/en/blog/openclaw_pri…