An Explainable Multi-Agent System for Real-Time SMS Smishing Detection with LangGraph Orchestration

SMS has become a target for cyberattacks. Users may get one-time passwords, transaction notifications, banking alerts and other service announcements.These messages can be quite familiar, urgent, making it difficult to determine fake messages, putting users at risk of being victims of smishing. Once successful, information and authentication credentials can be stolen from victims.Current defenses are typically designed with static rules or individual AI models. They struggle to catch evolving scam messages. They also lack real-time analysis, a combined threat check and clear reasons for why a message's dangerous. To solve these problems we introduce ScamShield AI. ScamShield AI is a multi-agent AI system that works in time to detect smishing. It contains three agents: an OTP Message Analysis Agent, a Contact Trust and Pattern Recognition Agent and a URL Analysis Agent. Each agent looks at a part of an incoming SMS. A LangGraph-based Coordinator Agent brings the agents together combines their results and outputs a label of Safe, Suspicious or Scam along with a risk score. The system first cleans messages and creates semantic embeddings for similarity-based threat detection. A MongoDB community database uses vector search to find messages that're semantically similar to known scam patterns. If the message matches known harmful patterns, the system doesn't have to analyze it. New messages go through the LangGraph multi-agent pipeline. Security controls keep the threat data trustworthy. In addition the system uses Explainable AI to give users an explanation of why a message is determined to be suspicious. the app can suggest steps that users can take, for instance, refraining from clicking on links or blocking the sender, which can help them make better security decisions.