Red Teaming AI Systems: Attack LLMs Ethically with Sreekanth Kanuparthi

Red Teaming AI Systems: Attack LLMs Ethically is a compact, high-impact technical guide for developers, security engineers, and AI practitioners who need hands-on methods to safely evaluate LLM security.

This guide walks you from easy prompt-injection tests to advanced model extraction & poisoning techniques — and, critically, how to defend against each attack.

What you’ll get:

• Clear threat models for modern LLM apps (APIs, RAG, agents, tool-enabled LLMs).
• Attack chain walkthroughs: direct prompt injection, role/ system-prompt attacks, RAG/ data poisoning, model extraction, tool/code injection, and privacy extraction.
• Practical Python snippets and pseudocode you can run in a sandbox (examples for Hugging Face, OpenAI, LangChain patterns).
• Step-by-step red team workflows (test → observe → measure → patch → retest).
• A worked case study (chatbot with password reset API) showing exact prompts that worked, how we fixed them, and secure-by-default design patterns.
• 2025-relevant references: OWASP LLM Top-10, NIST AI guidance, and known CVE-style examples to watch.
• Checklists, CI/CD integration tips, and verification methods so your team can automate red-team tests.

Why this matters: LLMs now power automation and can access tools & data — a small prompt or poisoned doc can lead to data leaks, unsafe outputs, or unauthorized actions. This guide helps you find those gaps before attackers do.

Format: downloadable PDF (20–30 pages), code snippets, one worked example, checklists, and mitigation playbook.

Price: $5 — instant download after purchase.