Detect Scams
Before They Cost You
Analyse suspicious messages, screenshots, phishing links, and fake job offers in seconds.
Experience Live Detection
Select any real-world scam scenario below to test ScamShield's heuristic scoring, threat breakdown, and safety advice in one click.
Amazon ₹50k Prize Scam
Prize Bait / Phishing
Vectors:Urgency countdown + advance registration fee + spoofed domain.
Fake SBI KYC Update
Banking Urgency / Credential Theft
Vectors:Account suspension threat + phishing URL with suspicious .xyz TLD.
Telegram Work-from-Home Scam
Employment Fraud / Advance Fee
Vectors:Unrealistic daily earnings promise + required upfront activation deposit.
Fake Investment Opportunity
Ponzi / High-Yield Fraud
Vectors:Guaranteed impossible returns + high-risk TLD + fake investment fund.
How It Works
Understand threats in seconds without compromising your privacy or data.
Paste Message
Input SMS, job offer, or upload a screenshot to extract text locally.
AI Analyses Risk
Engine matches heuristic triggers, urgency traps, and queries Gemma 4.
Get Scam Score & Safety Advice
Receive instant risk percentage, detected signals, and actionable advice.
Built for Modern Threat Vectors
Engineered with deterministic heuristic rules, computer vision OCR, and transparent scoring to keep citizens safe from digital fraud.
Scam Detection
Detect phishing, fake jobs, investment fraud and payment scams.
Screenshot Analysis
Upload screenshots and identify scam indicators instantly.
URL Intelligence
Analyse suspicious websites and domains before visiting.
Built in Public
ScamShield OSS believes security tools protecting ordinary citizens must never be locked behind proprietary surveillance walls.
Open Source
100% transparent codebase licensed under Apache/MIT. Inspect every heuristic rule, weight, and privacy guarantee yourself.
Community Driven
Continuously improved by cybersecurity researchers, open-source contributors, and digital safety advocates.
Transparent Detection Logic
Zero black-box mystery. Every scam score is traceable to explicit linguistic patterns, URL anomalies, and psychological triggers.
Star us on GitHub • Pull requests & new scam rules welcome