The rise of AI-powered browsers has introduced a new frontier in web security—one where traditional defenses fall short and attackers have found innovative ways to exploit artificial intelligence itself. Recent research has uncovered critical vulnerabilities in agentic AI browsers, particularly Perplexity’s Comet browser, revealing how malicious actors can manipulate these tools through sophisticated prompt injection techniques.

The software development landscape has undergone a seismic shift in 2025. Developers are increasingly adopting “vibe coding”—a revolutionary approach where you describe what you want in natural language, and AI tools like GitHub Copilot, Cursor, Claude, and ChatGPT generate fully functional code. It’s fast, intuitive, and remarkably effective. But beneath this appealing surface lurks a troubling question: Is vibe coding safe for web development?

Website security has become a critical concern in today’s digital landscape. With cyber attacks becoming more sophisticated every day, traditional security measures are struggling to keep pace. Enter Artificial Intelligence (AI) – a game-changing technology that’s revolutionizing how we protect, detect, and remediate security threats on websites.

DeepMind announced CodeMender — an AI-driven system that detects software vulnerabilities and proposes verified fixes. It combines large language models with classical program analysis (fuzzing, static analysis) and a validation pipeline that runs tests and generates candidate patches. DeepMind says CodeMender upstreamed 72 fixes in early trials — a concrete sign the approach can scale.

CodeMender is a new generation of automated code-repair systems that use advanced language models together with traditional program analysis tools to find, propose, and validate security fixes at scale. For web applications, the approach can dramatically shorten the gap between discovery and remediation for many classes of vulnerabilities — but only when paired with strong validation, clear governance, and human review. This article explains what such an agentic patching system does, how it works, where it helps most in web security, how to pilot it safely, and the practical controls you must put in place.

Web attacks remain the most common initial vector in modern incidents. Classic signature and rule-based defenses are necessary, but insufficient: they miss novel patterns, produce high noise, and struggle with complex, multi-step attacks. Neural networks — from autoencoders to graph neural networks and Transformers — bring a contextual, pattern-oriented layer that detects subtle anomalies across time, entities and relationships. When deployed thoughtfully (hybridized with rules, instrumented for explainability, and operated with retraining and feedback loops), NN-driven systems can significantly reduce mean time to detect (MTTD), lower analyst load, and cut false positives.

Read More