How attackers hijack AI applications with nothing but text: direct vs indirect injection, real incidents from Bing to EchoLeak, why no complete fix exists, and the layered architecture that actually contains it.
RAG pipelines trust whatever lands in the vector database. Learn how corpus poisoning, retriever manipulation, and injected documents hijack AI answers — and the defenses that actually work in 2026.
A comprehensive analysis of the top 4 AI agent security threats in 2026: tool-use exploitation, supply chain attacks, cross-agent contamination, and model extraction — with actionable defense strategies.
Five years ago, application security meant securing APIs, patching web frameworks, and hunting for SQL injection in input fields. Today, we're handing over SSH keys, database credentials, and deployment pipelines…
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.
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.
A practical guide to agentic AI security covering goal hijacking, tool misuse, identity and privilege abuse, memory poisoning, multi-agent trust issues, and defense frameworks for autonomous AI systems.
How AI agents transform enterprise SOC operations — autonomous triage, incident response, threat hunting, and compliance automation.
Explore multi-agent AI security: A2A protocol hardening, MCP boundary enforcement, cross-agent memory isolation, and trust boundary design patterns.
A comprehensive security assessment of the Model Context Protocol covering threat modeling, attack surface analysis, pentest methodologies, prompt injection test cases, and hardening strategies for MCP servers and clients.