Executive Summary Traditional software vulnerability scanners search for known bugs in dependencies, memory leaks, and weak authorization policies. However, deep learning models are opaque, meaning standard security scans cannot identify logic flaws or safety failures in a model’s weights. To uncover vulnerabilities in an AI system before hackers do, organizations must employ AI Red Teaming.…
Executive Summary Deploying traditional software involves pushing containers, managing databases, and securing APIs. Deploying AI applications—particularly those containing Large Language Models (LLMs) and autonomous agents—introduces a unique infrastructure risk. AI applications are non-deterministic, generate code on the fly, and execute actions (tool calling) in real-time. If an agent is hijacked via prompt injection, it could…
Executive Summary The integration of Large Language Models (LLMs) and autonomous agents into enterprise software has expanded the cyber-attack surface. Traditional cybersecurity protects the network, databases, and application endpoints using firewalls, input validation, and access controls. However, AI models introduce a new category of vulnerability: natural language is the interface, and the code execution path…