Vulnerability Scanning

The digital landscape is in a perpetual state of evolution, and with it, the sophistication of cyber threats. Traditional vulnerability scanning methods, while foundational, are increasingly strained by the sheer volume and complexity of modern software. Artificial Intelligence (AI) is emerging as a powerful ally, offering transformative capabilities in how we identify, analyze, and mitigate security weaknesses. This article delves into the burgeoning field of AI-driven vulnerability scanning, providing a comprehensive overview and, crucially, a hands-on guide to leveraging Lambda’s Inference API for a practical vulnerability analysis task. ...

Introduction AI-generated code from tools like GitHub Copilot and Cursor accelerates development but introduces hidden risks: 62% of AI-generated solutions contain security flaws, including hardcoded secrets, SQLi, and insecure dependencies. Traditional SAST tools struggle with probabilistic code patterns, creating a critical gap in modern DevSecOps pipelines. Endor Labs’ $93M-funded platform addresses this with AI-native static/dynamic analysis, scanning LLM outputs for context-aware vulnerabilities. This guide walks through local setup, CI/CD integration (with GitHub Actions examples), and custom rule creation to secure AI-generated code before deployment. ...