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Anthropic Revenue Surges Ahead of Planned IPO: The company behind Claude reported quarterly revenue exceeding $11.5 billion, a 14-fold year-over-year increase, as it prepares to go public and compete directly with OpenAI for enterprise AI adoption.
AI Firms Suspected of Covert Data Acquisition Through Book Purchases: Secondhand booksellers across the UK and Ireland report unusual bulk orders believed to be AI companies acquiring physical texts for training data, with Anthropic previously confirmed to have spent millions on such acquisitions.
Researchers created SOOM, a defense method that obfuscates (hides or disguises) deep learning operators to protect against model extraction attacks, where attackers reverse-engineer compiled neural network code to recreate trainable models. Built on TVM (a deep learning compiler), SOOM uses a machine learning cost model to scramble how operators work while keeping inference fast, achieving a 89% failure rate against extraction attacks with minimal performance slowdown.
Fix: The source proposes SOOM itself as the mitigation: a schedule-search-based operator obfuscation method built on TVM that constructs an obfuscation space for deep learning operators and uses a security-aware learned cost model based on XGBoost gradient boosted trees to generate obfuscated executable code for various deep learning operators, balancing security objectives with performance requirements.
IEEE Xplore (Security & AI Journals)