全球AI：2026年6月3日最重要的事件

今天的《每日AI世界简报》汇集了来自全球关键地区的最新人工智能新闻。重点关注商业应用、监管、安全以及AI模型的开发。 欧洲 AI模型提取攻击：绕过防御中的单客户端假设 arXiv:2606.03381v1 Announce Type: cross Abstract: Ensuring the protection of Artificial Intelligence (AI) models deployed in military Command and Control (C2) systems and critical infrastructure is essential for maintaining information superiority.

Model Extraction Attacks (MEAs) pose a significant threat, as they enable adversaries to replicate proprietary models, compromise protected information, and prepare offline adversarial attacks.

However, current defense strategies predominantly rely on the Single Client Assumption (SCA), which is the implicit assumption that attacks originate from isolated identities.

This work systematically demonstrates that the SCA is fundamentally invalid in the presence of coordinated threat actors, such as Advanced Persistent Threats (APTs).

We introduce a modular, open-source framework called CerberusAI for reproducible model-stealing research, and use i 为什么重要： 有必要关注该信息对市场、监管和AI用户的影响。 来源： arXiv AI (3.06.2026) AlignAtt4LLM：用于IWSLT 2026同步语音翻译任务的仅解码器大型语言模型（LLMs）快速对齐攻击 arXiv:2606.03967v1 Announce Type: cross Abstract: We describe AlignAtt4LLM, an IWSLT 2026 simultaneous speech translation system for English to German, Italian, and Chinese.

The system is a synchronous cascade: Qwen3-ASR with forced alignment produces an incrementally updated source transcript, and Gemma-4 E4B-it translates that prefix under an MT-side AlignAtt policy.

To our knowledge, this is the first application of AlignAtt to a decoder-only LLM, where the encoder-decoder cross-attention used by earlier AlignAtt systems is absent.

We recover a usable policy by proposing (1) an explicit source span in the prompt, (2) offline selection of translation-specific alignment heads, (3) selective qk-fast replay of the draft-to-source attention block, and (4) runtime query/key capture that preserves model outputs bit-identically.