Red teaming LLM applications requires fundamentally different techniques than traditional penetration testing. This playbook covers the complete methodology: reconnaissance, attack execution across 5 categories, advanced adversarial ML techniques, and a reporting framework for AI security assessments.
Google's TurboQuant is a breakthrough AI compression method targeting memory bottlenecks in large language models. This guide explains how TurboQuant achieves 6x KV-cache reduction and 8x speedup on H100 GPUs with training-free quantization — no retraining required.
Model Context Protocol (MCP) is an open standard enabling AI assistants to communicate with external tools and data sources seamlessly. Learn how MCP works, its architecture, and why it matters.
Prompt injection is the SQL injection of the AI era. In 2026, direct injection, indirect injection, and jailbreaking techniques are evolving rapidly. Learn real-world case studies, from Bing Chat's "Sydney" incident to SEO manipulation via AI summarizers, and proven defense strategies.
How AI models get stolen through extraction and distillation attacks. Explore techniques, defenses, and real-world ML security case studies.
The OWASP Top 10 for Agentic Applications defines the most critical security risks for autonomous AI agents in 2026. From prompt injection and tool hijacking to supply chain poisoning and multi-agent attacks, this comprehensive guide covers every vulnerability with real attack scenarios and proven defense strategies.
How AI agents transform enterprise SOC operations — autonomous triage, incident response, threat hunting, and compliance automation.
Discover CAI (Cybersecurity AI Framework), the open-source toolkit revolutionizing bug bounties and CTF competitions with autonomous AI agents.