Adversarial Pattern Recognition in AI Systems: A Red-Team Framework for Emerging Web Exploitation

The integration of AI systems into web applications has created entirely new categories of vulnerabilities that traditional penetration testing methodologies fail to address. This paper presents a structured red-team framework specifically designed for identifying and exploiting adversarial patterns in AI-powered web systems. Covering prompt injection, model extraction, training data poisoning via web interfaces, and context manipulation attacks, the framework provides security teams with practical methodologies for assessing AI system resilience.

The paper introduces novel attack taxonomies, demonstrates real-world exploitation scenarios across common AI architectures, and proposes defensive countermeasures. Particular attention is given to emerging attack vectors that exploit the intersection of traditional web vulnerabilities and AI-specific weaknesses.