AI supply chain attacks introduce entirely new attack vectors: poisoned training data, compromised base models, malicious plugins, and model extraction. This guide covers the full spectrum of attacks with real case studies and a practical security framework.
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.
AI-driven autonomous attacks adapt in real time: agent persistence, supply chain poisoning, AI social engineering, autonomous lateral movement, and adversarial ML. The five most dangerous 2026 vectors and the defense strategy that matches them.
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 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.
A deep dive into securing AI agent supply chains — MCP server vetting, dependency integrity, sandboxing, and trust anchors for AI infrastructure.
MCP is not one trust boundary - it is four: transport, tool, data and agent. This guide maps the Model Context Protocol attack surface and gives a hardening checklist for every layer, from stdio server credentials to tool-description review.
Your IAM system answers 'who has access' — AI agents ask 'what will this identity do next.' The authority gap, three real attack scenarios, and a four-layer framework to close it.
A practical blueprint for AI agent authorization: task-scoped credentials, tool allow-lists, approval gates, workload identity, and full audit trails - mapped to OWASP and NIST.
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.
Zero Trust for AI systems requires rethinking identity, data flows, and access control. Learn the five pillars of AI Zero Trust — identity verification, input validation, least privilege, monitoring, and encryption — with practical architecture patterns and implementation roadmap.