Rethinking AI Security & Threat Defense

Read time: 5 mins

Last Updated on April 11, 2025

Published March 22, 2025

Deconstructing the 4 Phases of a Real-World AI Attack

When we think of attacks on artificial intelligence, the image that often comes to mind is a clever hacker "tricking" a model—showing a self-driving car a doctored stop sign or fooling a facial recognition system with strange glasses. While these adversarial examples are a real threat, they represent only the final, visible moment of a much longer and more methodical process.

In reality, a sophisticated attack on an AI system is a campaign, following a deliberate adversarial playbook that is nearly identical to a nation-state cyberattack. It rarely begins with the model itself. This post deconstructs that four-phase playbook, revealing how adversaries move from their first quiet foothold to their final, malicious impact.

Phase 1: It Starts with a Break-In, Not a Trick

Preparation, Access, and Environment Exploitation

These tactics focus on the initial reconnaissance, setting up the necessary infrastructure, and gaining the first foothold into the AI system or its environment.

The first stage of a modern AI attack is not about manipulating algorithms; it is about the fundamentals. Before an adversary can influence a model, they must first get inside the house. This initial phase is dedicated to activities like reconnaissance to map out the target environment, setting up the necessary technical infrastructure for the attack, and ultimately gaining initial access to the AI system's host environment.

This initial phase shatters the myth of the AI security specialist operating in a vacuum. It proves that the most advanced model integrity checks are rendered useless if an organization hasn't mastered fundamental cyber hygiene. Your AI security is only as strong as your traditional network security.

Phase 2: The Goal is Stealth and Control, Not Immediate Chaos

Execution, Control, and Evasion

Once initial access is established, the adversary seeks to execute their objective, maintain control, and avoid detection.

Once attackers get inside, their first move isn’t to cause chaos—it’s to settle in quietly. This isn’t a smash-and-grab job; it’s more like setting up camp. They focus on locking down a hidden spot, keeping control of the system, and staying under the radar so security tools don’t notice them.

What this shows is that serious attackers aren’t rushing—they’re playing the long game. Stealth comes first, because it gives them a stable launchpad for bigger moves later on. For defenders, that means shifting focus: instead of waiting for loud alarms, we need to be tuned in to the faintest signs that something’s off.

Phase 3: The Real Prize is the Data, Not Just the Model's Output

Internal Discovery and Data Movement

These tactics focus on gathering intelligence within the compromised environment, moving laterally, and collecting high-value assets (like training data, configurations, or sensitive output).

Once attackers have a safe hiding spot inside, they start poking around the system. The AI model itself isn’t the prize—it’s more like the treasure map. Using their foothold, they move sideways through the network, looking for the real jackpot: the training data that built the model, the secret configurations that make it run smoothly, and the sensitive outputs it produces.

The big takeaway here is that the model isn’t always the main target. More often, it’s just the doorway to the real valuables—the data and intellectual property behind the AI. Locking down the model but leaving its data exposed is basically like locking the front door of a bank while leaving the vault wide open.

Phase 4: The Final Impact is a Carefully Staged Event

Final Stages and Objective Achievement

The final stages involve staging the attack, establishing communication channels for command, exfiltrating stolen data, and achieving the final malicious impact.

This last stage is where everything comes together—the attacker finally cashes in on all the sneaky prep work they’ve done. At this point, they roll out the main attack, set up channels to stay in touch with their systems, steal the data, and deliver the damage they’ve been aiming for.

It’s important to see that things like adversarial examples or model manipulation aren’t the whole attack—they’re just the payload. The real effort was all the setup: the scouting, the break-in, the quiet moves behind the scenes. What you see on the surface is just the tip of a carefully aimed spear.