Prompt injection is the most critical vulnerability in enterprise LLM deployments. Learn how direct and indirect prompt injection attacks work, explore the OWASP LLM Top 10, and implement multi-layer defense strategies including input validation, output filtering, and architectural isolation.
AI red teaming is the practice of adversarially testing AI systems to discover vulnerabilities before attackers do. Learn the methodologies (NIST 600-1, Microsoft AI Red Team), attack types to test, and how to build a continuous adversarial testing program for enterprise LLM deployments.
Third-party and open-source AI models introduce supply chain risks that most enterprises overlook. Learn about model provenance verification, serialization attacks like pickle exploits, model card requirements, and how to build a secure model vetting process for enterprise deployments.
A comprehensive guide to the 10 most dangerous attack vectors targeting large language models in 2026. From prompt injection and data poisoning to model extraction and agent tool misuse, learn how each attack works, its real-world impact, and enterprise defense strategies.
Data poisoning attacks corrupt AI model behavior by manipulating training and fine-tuning data. Learn about backdoor attacks, clean-label attacks, fine-tuning data risks, detection techniques including anomaly detection and provenance tracking, and enterprise defense strategies.