
Article Brief
Why this article matters
This post covers the first three vulnerabilities in the OWASP Top 10 for LLMs—Prompt Injection (direct and indirect), Insecure Output Handling, and Training Data Poisoning—with fictional but realistic attack scenarios, a real CVE (CVE-2023-29374, LangChain RCE scoring 9.8), and hands-on challenge links from PortSwigger and Lakera's Gandalf game. You'll walk away with a practical understanding of why LLM outputs must be treated as untrusted input and how each vulnerability can chain into real-world impact.
Continue Reading
Next steps in the archive
Newer article
Deep Dive into HTTP Request Smuggling Attacks
Learn the technical aspects of HTTP Request Smuggling, from identifying and exploiting vulnerabilities to applying defences, to ensure your online infrastructure remains secure.
Older article
Pentesting with Kali by David Santo Orcero
A beginner review for a beginner oriented penetration testing book.
Keep Exploring
Related reading
Continue through adjacent topics with the strongest tag overlap.

Introduction to AI Security Course by Lakera AI
Dive into the essentials of AI security and learn about the AI threat landscape and how we can secure Large Language Models (LLMs) with this free 10 days introductory course

The Technical Anatomy of Model Extraction in 2026 (The Great AI Theft of the Century?)
A deep technical dive into Model Extraction attacks. We dissect the mathematics of Knowledge Distillation, logit harvesting pipelines, and the cryptographic failures of LLM watermarking.

Rules vs. Skills: Creating Secure AI Context in Engineering Teams
At my company we ran into a familiar question while scaling AI coding assistants: when should context live in a Rule or `CLAUDE.md`, and when does it deserve a Skill...

