Security

Problem Statement Your security team triages vulnerabilities by CVSS score. A 9.8 gets patched immediately; a 7.5 waits. But CVSS measures severity, not exploitability. In real-world data, CVSS achieves an AUC of just 0.662 at predicting which CVEs actually get exploited – barely better than a coin flip. You need a model that predicts exploitation likelihood, not just theoretical severity. This tutorial walks you through building an ML-based vulnerability prioritizer using public data. By the end, you will have a Random Forest model that beats CVSS by over 20 percentage points on AUC-ROC, and you will understand exactly which features drive those predictions using SHAP. ...

Problem Statement You are deploying an AI agent that can read files, search the web, or call APIs on behalf of users. Before you ship it, you need to know: what happens when someone tries to make it do something it should not? Existing frameworks like OWASP LLM Top 10 cover the language model layer, but agents have attack surfaces that models do not – tool orchestration, multi-step reasoning, persistent memory, and cross-agent delegation. You need a systematic way to test these surfaces. ...