Anthropic unveils Project Glasswing — Claude Mythos already found "thousands" of zero-days in major software
·Anthropic
Anthropic launched Project Glasswing on April 7 alongside AWS, Apple, Cisco, Google and Microsoft: a closed program distributing a restricted preview of Claude Mythos — a frontier model Anthropic says has already identified thousands of high-severity zero-day vulnerabilities across every major OS and browser. Mythos chains multiple low-severity bugs into single high-impact exploits (sometimes combining 3–5). Access is limited to ~50 partner orgs; Anthropic says the public release risk is too high. Program backed by $100M in Claude credits and $4M in open-source security donations. Sets the template for "AI that is too dangerous to ship".
If Mythos really is finding zero-days at the claimed scale, the offense-defense balance in software security shifts materially within months. The coalition of defenders (AWS/Apple/Cisco/Google/Microsoft) getting restricted access essentially ratifies a new category of "controlled-access AI" — and creates pressure for similar restrictions on OpenAI/Google/Meta cyber models. Bigger governance question: if a Claude-tier model can weaponize chained vulnerabilities at scale, is Anthropic's "too dangerous to ship" bar the new industry norm, or an exception?
First-party Anthropic announcement with partner confirmations from named Fortune-10 companies, plus independent coverage from NPR, TechCrunch, VentureBeat, Fortune. The "thousands of zero-days" claim is self-reported and unverifiable without access to the model — treat as Anthropic's characterization, not a third-party finding. FUD risk moderate: strong vendor-incentive to hype capability + consequence framing.
@hardmaru (David Ha) flagged a paper adapting Sora-style video-diffusion architectures to build a learned world model of an actual Linux desktop. The model ingests 9,000 hours of screen-recording + keyboard/mouse traces and learns to predict next-frame UI state conditioned on user input — effectively a probabilistic operating-system simulator. On a held-out eval of 50 common tasks (opening files, running commands, navigating web UIs), the model achieves 73% next-event accuracy at 2-second horizons and 41% at 30-second horizons, beating the prior SOTA (Meta AI Habitat-UI) by 18pp. Direct application: train agents in fully simulated computer environments without real-system rollouts — cuts RL data costs ~40x and eliminates the safety risk of letting agents touch production systems during training.
EE Times deep-dive on AMD's ROCm 7.0 and whether it can finally dent NVIDIA's CUDA moat. AMD's MI400 (96GB HBM4, 5.2 PFLOPS FP8) now runs PyTorch, vLLM and SGLang out-of-the-box — but reviewers testing MLPerf Inference v5.1 still see 1.6–2.2x gaps vs H200 on representative LLM workloads, driven by kernel-library maturity rather than raw silicon. Breakthrough of the cycle: AMD hiring 600 CUDA-kernel engineers in 12 months, plus open-sourcing HIPify tooling that auto-translates 83% of typical CUDA kernels. AMD claims Meta, Microsoft and OpenAI are all now shipping production MI400 pods. NVIDIA's response: CUDA 13 with tensor-core autotuning targeting the same eval suite, launching Q2.
Anthropic announced the advisor strategy on the Claude Platform: pair Opus 4.6 as a planning/critique advisor with Sonnet 4.6 or Haiku 4.5 as the executing model. The advisor inspects partial outputs, suggests corrections and redirects the executor mid-generation. On SWE-bench Multilingual, Sonnet+Opus-advisor scores 2.7 percentage points higher than Sonnet alone, at roughly 1.3x the cost vs 7x the cost of running Opus end-to-end. General availability today via the Claude Console and CLI; pricing is existing Claude API rates for both models (no advisor premium). Anthropic positions this as the first first-class multi-model inference primitive in any frontier-lab API — not just routing or cascading but explicit advisor/executor roles with shared context.