Red teaming methodology for GenAI

Moving AI security testing from guesswork to a structured discipline

When most people think about testing AI systems for security, they picture someone typing clever prompts to trick a chatbot into saying something it should not. While that kind of testing has its place, it barely scratches the surface of what a thorough security assessment requires. Real-world attackers are methodical, patient, and creative. Your testing needs to match that level of sophistication.

At SnowCrash Labs, we have developed a structured methodology for red teaming generative AI systems that treats the process as an engineering discipline rather than an art form. The goal is repeatable, comprehensive results that give organizations genuine confidence in their AI deployments.

The eight phases of an AI attack

Our research has mapped the typical progression of an attack against an AI system into eight distinct phases. Understanding this progression is the key to building effective defenses because it tells you not just what to look for, but when and where to look for it.

The attack begins with reconnaissance. An attacker probes the AI system to understand what it can do, what tools it has access to, and where its safety boundaries lie. This phase is often invisible to traditional monitoring because the attacker is asking seemingly innocent questions.

Next comes goal manipulation. Once the attacker understands the system, they work to shift its objectives. This can be as simple as overriding the system instructions or as subtle as gradually changing the AI's behavior over multiple interactions so it drifts from its intended purpose.

The third phase involves escalating permissions. AI agents often have access to tools, databases, and other systems. An attacker who can convince the agent to use those tools in unintended ways can reach far beyond what direct access would allow. Imagine convincing an AI assistant with calendar access to also read confidential files it was never meant to touch.

From there, attackers may try to persist in the system's memory, compromise other AI agents in the network, poison the knowledge bases the AI relies on, bypass safety filters, or extract sensitive data through the AI's normal output channels.

Why random testing fails

The traditional approach to AI red teaming involves human testers manually crafting adversarial prompts based on known attack patterns. This has two fundamental problems.

First, it depends entirely on what the testers already know. If an attack pattern is not in their playbook, they will not find it. The space of possible attacks against an AI system is enormous, and no human team can explore it comprehensively through manual effort alone.

Second, the results are not reproducible. Two different testers given the same system will find different vulnerabilities, and neither will find all of them. This makes it impossible to measure whether security is improving over time or to provide the kind of documented evidence that regulators and auditors require.

An algorithmic approach to finding weaknesses

SnowCrash addresses these limitations by treating vulnerability discovery as an optimization problem. Instead of randomly trying inputs and hoping to find something, our testing engine uses mathematical techniques to efficiently search for the specific inputs that are most likely to expose each category of weakness.

Think of it this way: if you were searching for structural weaknesses in a bridge, you would not randomly tap every square inch with a hammer. You would use engineering principles to calculate where stress concentrations are most likely to occur and test those points first. Our approach applies the same logic to AI systems.

This method consistently finds vulnerabilities that manual testing misses, and it does so in a fraction of the time. More importantly, the results are reproducible. Run the same test twice and you get the same findings, which means you can measure progress and demonstrate compliance.

What a thorough assessment looks like

A comprehensive red team engagement against a generative AI system should test across all eight attack phases and all twenty categories of known weakness. It should cover single-turn attacks where the damage happens in one interaction, multi-turn attacks that unfold over a series of conversations, and attacks that target the broader ecosystem including knowledge bases, connected tools, and other AI agents.

The output should not be a list of clever prompts that broke the system. It should be a structured risk assessment that maps every finding to recognized industry standards, quantifies the severity, and provides clear guidance on remediation. This is what turns red teaming from a demonstration into a decision-making tool for leadership.

Organizations that treat AI red teaming as a continuous process rather than a one-time exercise are the ones best positioned to deploy AI safely and confidently at scale.