On June 12, 2026, a US export control directive forced Anthropic to disable its two most capable models, Claude Fable 5 and Mythos 5, for all users because it could not filter foreign nationals in real time. The order followed a claim by another company that it had jailbroken Mythos, but Anthropic disputes this as a narrow, non-universal jailbreak. Fable 5's safety system uses classifiers that route risky queries (cybersecurity, bio-chem, distillation) to Opus 4.8 in under 5% of sessions; the model had been publicly available since June 9. All other Claude models, including Opus 4.8, remain unaffected. This appears to be the first government-forced takedown of a publicly deployed frontier AI model.
Anthropic released two models, Claude Fable 5 and Claude Mythos 5, on June 9, 2026. Both belong to the new Mythos class, positioned above the Opus tier, and share the same underlying model. Fable 5 is generally available with safety classifiers that fall back to Opus 4.8 on flagged requests, while Mythos 5 has lifted cyber safeguards and is limited to Project Glasswing. The models offer a 1M-token context window and 128k output tokens, priced at $10/M input and $50/M output. Anthropic reports Fable 5 achieves state-of-the-art results across nearly all benchmarks, including software engineering, finance, vision, and long-context tasks, with Stripe demonstrating a 50-million-line code migration in one day. Classifiers activate in under 5% of sessions, and over 95% of Fable sessions experience no fallback, effectively matching Mythos 5 performance.
This tutorial provides a complete workflow for analyzing the ClawHub Security Signals dataset, covering data loading, exploratory analysis, and machine learning. It examines how different security scanners (VirusTotal, static analysis, SkillSpector) assess AI skills and their agreement patterns. A logistic regression pipeline is built combining SKILL.md text features with numerical scanner signals to predict the ClawScan verdict. The model is evaluated on a test set with a confusion matrix and misclassification analysis. The approach demonstrates a practical end-to-end security signal analysis in a Colab-friendly environment.
This tutorial demonstrates how to use NVIDIA garak for defensive LLM red-teaming. It covers setting up the framework, discovering plugins, running scans, and analyzing safety scores. Users learn to create custom probes and detectors to extend functionality. The workflow includes exporting results in AVID format for structured vulnerability reporting.