Intelligent Scam Detection System Based on Large Language Models: Technical Analysis of ScamGuard AI

This article provides an in-depth analysis of the open-source project ScamGuard AI, which uses large language models (such as Google Gemini) and prompt engineering techniques to build a modern scam information detection system. It enables intelligent identification and risk grading of fraudulent content like phishing SMS and fake job postings. Its core innovation lies in the structured reasoning output (Thought-Action-Risk three-layer architecture), which addresses the shortcomings of traditional rule-based detection systems and provides an interpretable dynamic protection solution for the digital security field.

With the popularization of mobile internet, scam methods have become increasingly complex (e.g., SMS phishing, fake job postings, OTP fraud). Traditional rule-based detection systems face problems like static rules being unable to cope with evolving language tactics, lagging blacklists, and keyword matching being easily evaded. AI-based detection solutions have emerged as a result, and ScamGuard AI is a typical representative, using LLM's semantic understanding capabilities to provide new solutions.

ScamGuard AI was created by Neeti Narvekar, and its tech stack includes Python, Google Gemini API, prompt engineering, and Streamlit. The core mechanism is the Thought-Action-Risk three-layer architecture:

1. Thought: Analyze the tone, intent, sense of urgency, and manipulation patterns of the message;
2. Action: Classify into "Possible Scam", "Safe", or "Uncertain";
3. Risk Level: Divide into LOW/MEDIUM/HIGH three levels to enhance interpretability.

ScamGuard AI covers multiple scam scenarios:

• Phishing email detection: Identify fraudulent emails disguised as banks, e-commerce platforms, or government agencies;
• SMS scam identification: Target OTP fraud, prize notifications, impersonating relatives/friends to borrow money, etc.;
• Fake job posting screening: Identify false information such as high-salary lures and requests for advance payment;
• Customer service message filtering: Help enterprises automatically mark fraudulent inquiries. It can be integrated into communication platforms and security systems.

The code adopts a modular design:

• main.py: Application entry;
• client.py: Encapsulates LLM API calls;
• utils/config.py: Manages system prompts and configurations;
• utils/database.py: Handles sample data and rule storage. Prompt engineering uses carefully designed system prompts to guide the model to focus on key scam features (abnormal language, sense of urgency, emotional manipulation, etc.), avoiding expensive fine-tuning while maintaining high accuracy.

Limitations:

• Relies on the Google Gemini API, requiring network access and keys, with data privacy considerations;
• Lacks quantitative evaluation metrics (precision, recall);
• Single language support (mainly English). Future Plans:
• Develop a Streamlit interactive interface;
• Implement real-time email/SMS integration;
• Add model evaluation metrics and performance benchmarks;
• Expand multi-language detection capabilities.

ScamGuard AI represents a trend in security protection: shifting from rule-based static defense to dynamic AI understanding. LLM's semantic understanding can identify subtle manipulations (such as emotional manipulation, social engineering) that are difficult for traditional methods to capture. For developers: It provides a reference implementation, demonstrating the pattern of encapsulating LLMs as security tools; For ordinary users: It serves as an intelligent protection layer, but security awareness remains the first line of defense.

ScamGuard AI is an inspirational open-source project that demonstrates the practical application value of AI in cybersecurity. Through structured reasoning output and modular architecture, it provides a modern and interpretable scam detection solution. With the advancement of LLM technology and the improvement of the project, such intelligent protection tools will play a more important role in the digital security field.