Web3 systems expose a fundamentally different security landscape from centralized platforms, characterized by composability, pseudonymous identities, decentralized governance, and rapidly evolving attack strategies that span social, application, and protocol layers. Existing security mechanisms, such as static smart contract analysis, blacklist-based phishing detection, and network-level mitigation, operate in isolation and assume fixed threat models, limiting their effectiveness against adaptive, cross-layer adversaries. This position paper argues that securing Web3 requires a shift from static, tool-centric defenses to learning-driven security primitives capable of continuous reasoning, adaptation, and actuation. We introduce AI-powered smart certificates as a new security abstraction: programmable, continuously updated trust artifacts that integrate on-chain verifiability with off-chain machine learning signals derived from user behavior, transaction dynamics, and social context. Unlike traditional certificates or audits, these certificates maintain state, learn under distribution shift, and support automated policy enforcement and revocation in response to evolving threats. We argue that existing paradigms, formal verification, threat modeling, and isolated anomaly detection, are structurally limited in capturing the non-stationary and socio-technical nature of Web3 attacks. We outline an architecture in which AI-powered smart certificates serve as cross-layer sentinels that coordinate heterogeneous security signals in real time, and position smart certificates as a research direction, raising questions around learning under partial observability, adversarial adaptation, and trustworthy ML deployment in decentralized systems.

翻译：Web3系统呈现出与中心化平台根本不同的安全格局，其核心特征包括可组合性、假名身份、去中心化治理，以及跨越社交层、应用层和协议层的快速演变的攻击策略。现有安全机制，例如静态智能合约分析、基于黑名单的钓鱼检测以及网络级缓解措施，均孤立运行且假设了固定的威胁模型，这限制了它们针对自适应、跨层对手的有效性。本立场论文认为，保障Web3安全需要从静态、以工具为中心的防御转向具备持续推理、自适应和执行能力的学习驱动的安全原语。我们提出将AI驱动的智能证书作为一种新的安全抽象：一种可编程、持续更新的信任工件，它将链上可验证性与链下的机器学习信号（来自用户行为、交易动态和社交上下文）相结合。与传统的证书或审计不同，这些证书能维护状态、在分布偏移下学习，并支持针对不断演变的威胁自动执行策略和撤销。我们认为，现有范式——形式化验证、威胁建模和孤立异常检测——在捕捉Web3攻击的非平稳性和社会-技术本质方面存在结构性局限。我们勾勒了一种架构，其中AI驱动的智能证书充当跨层哨兵，实时协调异构安全信号，并将智能证书定位为一个研究方向，提出了关于部分可观测性下的学习、对抗性适应以及去中心化系统中可信机器学习部署等问题。