Apply Old Security Tactics to New AI Threats

Experts are adapting traditional red teaming to find and fix security flaws in generative AI before attackers can exploit them.

Security experts are now adapting a classic offensive strategy, known as red teaming, to uncover vulnerabilities in the rapidly expanding world of generative AI. In a recent presentation covered by InfoQ, security architect Kennedy Torkura detailed how these techniques can safeguard Large Language Models (LLMs) and their underlying knowledge bases. The goal is to proactively simulate attacks to find and fix security flaws before malicious actors can exploit them. This approach specifically targets emerging threats unique to AI, such as data poisoning, where an attacker corrupts the information an AI learns from, and "LLMjacking," where an attacker gains unauthorized control over the model's functions or accesses its sensitive data. By thinking like an adversary, organizations can build more resilient and trustworthy AI systems from the ground up.

The core of this strategy is a method called adversary emulation, which goes beyond simple vulnerability scanning. Instead of just looking for known flaws, red teams mimic the specific tactics, techniques, and procedures of real-world attackers. To bring structure to this process, experts are leveraging the MITRE ATLAS framework. ATLAS is a curated knowledge base of adversary tactics used against artificial intelligence systems, serving as a playbook for red teams. For example, a team might use ATLAS to simulate a "Model Evasion" attack, crafting specific inputs designed to trick a content moderation AI into producing a harmful output. This provides a systematic and repeatable way to test an AI's defenses against a wide range of documented attack patterns, moving security testing from a guessing game to a scientific process.

This shift towards proactive AI security is critical for nearly every team involved in technology, from developers and security engineers to CTOs and business leaders. The rush to integrate generative AI into products has created a new and poorly understood attack surface. Traditional security tools, designed to protect networks and applications, are often blind to AI-specific vulnerabilities like prompt injection or model inversion attacks. Without a structured testing methodology, development teams are essentially flying blind, hoping their models are secure. Adopting a red teaming approach bridges this gap, providing a familiar framework for security professionals to apply their skills to this new domain and giving engineers concrete feedback on how to harden their AI applications.

The business implications of this approach are significant. Treating AI security as an afterthought is a recipe for disaster, potentially leading to data breaches, reputational damage, and the failure of expensive AI initiatives. By integrating adversary emulation into the MLOps (Machine Learning Operations) lifecycle, companies can de-risk their AI investments. This means allocating budget and time for specialized AI security testing and encouraging collaboration between security teams and data scientists. The practical takeaway for organizations is that securing AI is not about finding a magical new tool, but about adapting proven security disciplines. It requires a cultural shift towards viewing AI systems not just as powerful algorithms, but as critical assets that need to be defended with the same rigor as any other part of the IT infrastructure.

Looking ahead, this structured approach to AI security is set to become an industry standard. As AI models become more autonomous and control more critical functions, the demand for verifiable security and robustness will only grow. Frameworks like MITRE ATLAS provide a common language for organizations, researchers, and even regulators to discuss and mitigate AI risks. We can expect to see the emergence of more specialized tools and platforms designed to automate and scale AI red teaming exercises. What is now a cutting-edge practice for well-resourced teams will likely become a fundamental requirement for any organization deploying AI in a responsible and secure manner, ensuring that innovation does not come at the cost of safety.

Standard security tools don't cover AI-specific threats like prompt injection or model poisoning. This approach gives teams a structured way to find and fix these new vulnerabilities before they cause data breaches or system failures.

Companies can't treat AI security as an afterthought. This method integrates security testing directly into the development lifecycle, reducing the risk of costly failures and building trust in AI applications. It requires investment in specialized skills and tools.