我们如何在 WhatsApp 上利用端到端加密和可验证性保障构建诈骗警报
WhatsApp is committed to helping people stay safe while protecting the privacy of their messages. As scam tactics evolve — from impersonation to social engineering to AI-generated lures — we’re always evolving as well, so that our protections stay ahead of scammers while protecting people’s personal messages with end-to-end encryption.
WhatsApp致力于在保护用户消息隐私的同时帮助人们保持安全。随着诈骗策略的不断演变——从冒充到社会工程学，再到AI生成的诱饵——我们也在不断发展，以便我们的保护措施能够领先于诈骗者，同时通过端到端加密保护人们的个人消息。
Today, we’re sharing an early look at Scam Alert, a new, optional feature that runs an on-device machine learning model to alert a user about potential scam messages. No message content leaves the device for classification or is auto-reported to WhatsApp, Meta, or anyone else. The feature complements end-to-end encryption while enabling a user-controlled, optional scam alert when the model believes there’s a likely scam.
今天，我们分享对Scam Alert的早期了解，这是一项新的可选功能，可在设备上运行机器学习模型，以向用户发出潜在诈骗消息的警报。没有消息内容会离开设备进行分类，也不会自动报告给WhatsApp、Meta或任何其他方。该功能是对端到端加密的补充，同时在模型认为可能存在诈骗时，启用用户控制的可选诈骗警报。
Before we make this feature available to all WhatsApp users, we are publishing this early technical overview alongside the feature’s limited rollout in Beta, and will continue working with our Bug Bounty community to stress-test this system. To help validate our implementation, we welcome feedback from the broader security research community.
在向所有 WhatsApp 用户推出此功能之前，我们发布了这篇早期技术概述，并伴随该功能在 Beta 版中的有限推出，同时我们将继续与我们的 Bug Bounty 社区合作，对该系统进行压力测试。为了帮助验证我们的实现，我们欢迎更广泛的安全研究社区提供反馈。
Design Principles
设计原则
Recent advances in on-device machine learning models make it possible to run accurate text classification entirely on mobile hardware without the performance, battery, or model-size tradeoffs that previously made on-device classification less practical. Scam Alert is well-suited to this approach: The model is small enough to run on-device, simple enough to publish for independent review, and effective without server-side components. The architecture we chose reflects a set of deliberate choices about what this system...