Practical articles on AI-powered WAF, API security, GraphQL protection, secure coding, and modern application threats.
Most WAF failures are not missed detections. They are requests the WAF never fully read, or parsed differently from your application — and neither one shows up on the dashboard
Most WAF failures are not missed detections. They are requests the WAF never fully read, or parsed differently from your application — and neither one shows up on the dashboard
A signature-based WAF caught maybe a third of forty AI-generated SQL injection variants — not because the rules were wrong, but because the strings kept changing while the intent didn't. That gap is the case for moving web application firewalls from pattern matching to intent classification, and what that actually looks like in a real Node.js middleware stack.
Traditional WAFs are useful, but many still depend heavily on rules, signatures, and constant tuning. GladiosWAF takes a different approach by focusing on AI-based request intent classification — understanding what a request is trying to do, not just what pattern it matches.
Static WAF rules work — until attackers stop using the patterns the rules expect. Here's why obfuscation, payload mutation, and AI-generated variations are exposing a fundamental gap in rule-based web security, and what a more intent-aware approach actually looks like.
GladiosWAF started from a specific frustration: rewriting the same security validation logic across every new project and every new stack. This is the founder's honest account of why that repetition is a real security risk, why rules alone aren't enough, and what a more intelligent reusable WAF layer is actually trying to solve.
Web Application Firewalls have been built around the same idea for decades: inspect the request, match it against known attack patterns, block it if it matches.
GladiosWAF uses behavioral intent analysis to detect evasive, obfuscated, and AI-generated attack traffic in real time.
