If you’ve heard of MITRE ATT&CK, you already know the basic idea: a curated knowledge base of adversary tactics and techniques, built from real-world observations. ATLAS is the same concept, purpose-built for AI systems.
ATLAS stands for Adversarial Threat Landscape for Artificial-Intelligence Systems. It documents the techniques threat actors use to attack AI models, exploit AI-integrated systems, and steal or manipulate AI outputs. The framework is organized around three core areas: ML pipeline attacks, AI model exploitation, and the exfiltration or manipulation of AI-generated content.
The need for this is real and growing. Security teams built entire programs around traditional infrastructure — endpoints, networks, identities. Then came the AI pivot, and suddenly there are new attack surfaces that most teams don’t have mapped, monitored, or defended.
What ATLAS Actually Covers
The framework organizes threats into categories that map to the AI lifecycle: from reconnaissance on ML infrastructure to initial access through model APIs, through privilege escalation via compromised training pipelines, all the way to impact techniques like model corruption or adversarial output generation.
What makes it distinct from standard threat frameworks is the focus on the unique properties of AI systems — things like prompt injection, training data poisoning, model inversion, and the abuse of model APIs as an attack vector. These don’t map cleanly to traditional MITRE ATT&CK techniques.
Real threat actors are already active in this space. Forest Blizzard, a Russian state-sponsored group, has been documented using generative AI for target research. Aquatic, associated with Chinese state interests, has targeted ML development environments. These aren’t theoretical attacks.
Why Your Team Should Pay Attention
The gap between AI deployment and AI security readiness is wide. Most organizations have AI systems in production — whether internal copilots, customer-facing chatbots, or integrated SaaS tools with AI components — that security teams don’t have visibility into.
ATLAS gives you a vocabulary and a reference point. You can use it to assess your current AI exposure, map controls against documented adversary techniques, and build detection logic for the most relevant threats in your environment.
The starting point is simpler than it sounds: take inventory of where AI lives in your stack, pick the ATLAS techniques most relevant to those systems, and ask whether you have logging, monitoring, or controls covering those specific attack paths.
You don’t need to become an ML security expert overnight. But the adversaries are already thinking about your AI systems. ATLAS gives you a way to start thinking about them too.
