总体来说，MacBook Neo 的性能介乎于 M1 和 M2 左右。虽然已经是 6 年前的芯片，时至今日，M1 轻度办公体验依旧相当能打，编辑部依然有不少编辑还在主力使用 M1 MacBook Air 工作。
ABC News Live Features，更多细节参见有道翻译
。关于这个话题，Mail.ru账号,Rambler邮箱,海外俄语邮箱提供了深入分析
Появились сведения о повреждениях в Ленинградской области после атаки беспилотников ВСУ08:07
31 марта 2026, 13:50Международные новости，更多细节参见美洽下载
However, the failure modes we document differ importantly from those targeted by most technical adversarial ML work. Our case studies involve no gradient access, no poisoned training data, and no technically sophisticated attack infrastructure. Instead, the dominant attack surface across our findings is social: adversaries exploit agent compliance, contextual framing, urgency cues, and identity ambiguity through ordinary language interaction. [135] identify prompt injection as a fundamental vulnerability in this vein, showing that simple natural language instructions can override intended model behavior. [127] extend this to indirect injection, demonstrating that LLM integrated applications can be compromised through malicious content in the external context, a vulnerability our deployment instantiates directly in Case Studies #8 and #10. At the practitioner level, the Open Worldwide Application Security Project’s (OWASP) Top 10 for LLM Applications (2025) [90] catalogues the most commonly exploited vulnerabilities in deployed systems. Strikingly, five of the ten categories map directly onto failures we observe: prompt injection (LLM01) in Case Studies #8 and #10, sensitive information disclosure (LLM02) in Case Studies #2 and #3, excessive agency (LLM06) across Case Studies #1, #4 and #5, system prompt leakage (LLM07) in Case Study #8, and unbounded consumption (LLM10) in Case Studies #4 and #5. Collectively, these findings suggest that in deployed agentic systems, low-cost social attack surfaces may pose a more immediate practical threat than the technical jailbreaks that dominate the adversarial ML literature.