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FIRST LOOK Microsoft Copilot Gains Local File Access via Hybrid Intelligence // FIRST LOOK AWS Adds Native Access Controls for RAG via Amazon Quick and Bedrock // HIGH DPRK npm Supply Chain Worm Uses Web3 C2 to Steal Cloud Keys // FIRST LOOK Rein Security Launches $25M Runtime Guard for AI Agents // HIGH APT Uses AI-Generated Lures in Google AitM Phishing on Taiwan // CRITICAL Tensorlake npm Package Hijacked by Shai-Hulud Worm // FIRST LOOK Meta Launches Open Standard to Authenticate AI Agents on the Web // FIRST LOOK Anthropic Launches 3-Tier Cyber Verification Program for AI Access // FIRST LOOK CrowdStrike Maps LLM Safety Classifier Evasion for Defenders // HIGH OpenAI Adds Training Monitors After Medicare Data Breach //
MOONSHOT-AI

Moonshot-Ai

2 reports
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Anthropic Exposes 200M-Exchange Model Distillation Attacks

Anthropic Exposes 200M-Exchange Model Distillation Attacks

ATLAS OWASP CRITICAL Active exploitation · Immediate action required ▲ 9.2 TechCrunch AI

Anthropic has published a detailed report attributing nearly 200 million adversarial API exchanges to coordinated model distillation campaigns conducted by Alibaba, Moonshot AI, and DeepSeek. Attackers used prompt obfuscation techniques — including fake translation requests — to bypass Claude's summarised-thinking safeguards and extract raw chain-of-thought traces for use as supervised fine-tuning data. One Moonshot AI campaign was assessed as routing requests directly through Chinese military infrastructure, adding a significant geopolitical dimension to what is otherwise an IP-theft threat.

Moonshot AI Releases Kimi K3 Open-Weight 2.8T Model Weights

Moonshot AI Releases Kimi K3 Open-Weight 2.8T Model Weights

FIRST LOOK ATLAS OWASP MEDIUM Moderate risk · Monitor closely ▲ 5.8 Simon Willison

Moonshot AI has released the weights for Kimi K3, a 2.8 trillion parameter mixture-of-experts model (1.56TB), distributed under a restrictive 'open weight' licence that requires a separate commercial agreement for large MaaS operators. The public availability of weights at this scale materially lowers the barrier for adversarial fine-tuning, jailbreak research, and model-theft-adjacent supply chain attacks. Defenders deploying or downstream of K3 should assess licence compliance risk alongside the standard open-weight threat model.

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