AI Security
From Cryptojacking to Model Theft: How Attackers Exploit GenAI Systems
Security researchers are revealing the tactics attackers use to hijack AI models and poison training data—and how organizations can test their defenses through red teaming.
InfoQ
GenAI applications present a new frontier for attackers, and organizations that deploy them must think like adversaries to protect against emerging threats. Red teaming—the practice of simulating attacker behavior to uncover vulnerabilities—is becoming essential for anyone building or securing AI-powered systems.
The Growing Attack Surface#
As enterprises integrate large language models and AI services into critical workflows, they introduce risks that go beyond traditional cybersecurity. These systems interact with sensitive company data, connect to external services, and generate content that represents the organization. A single compromise can expose proprietary information, damage reputation, or result in massive unexpected costs.
The regulatory landscape amplifies these concerns. Upcoming standards like the EU AI Act and ISO frameworks are creating compliance obligations, making security testing no longer optional but a business requirement.
Red Teaming for AI: A Mindset Shift#
Traditional red teaming has focused on finding technical exploits—brute-force attacks, vulnerability chains, credential theft. AI red teaming requires a different approach. Beyond technical attacks, defenders must now consider fairness, bias, and consistency in model outputs. The non-deterministic nature of generative AI also complicates testing: the same prompt may produce different responses on successive attempts, forcing teams to test more rigorously and frequently.
A key challenge is shifting from a defender's perspective to an attacker's. When a red team successfully compromises a system, defenders sometimes struggle to understand whether the exercise revealed a genuine gap or simply tested known vulnerabilities. This requires cultural buy-in and clear objectives.
Frameworks for Testing AI Systems#
The MITRE ATLAS framework serves as a foundational reference, documenting attack techniques specific to AI systems. Unlike traditional frameworks, ATLAS includes AI-specific tactics such as "AI attack staging," recognizing that AI infrastructure is compromised differently than conventional IT. Organizations can combine ATLAS with cloud and system-specific frameworks to create unified threat models tailored to their context.
The OWASP GenAI Red Teaming Blueprint offers a practical roadmap across the AI system lifecycle—from the model itself, through implementation guardrails, to runtime monitoring after deployment.
