AI Red Teaming

What is AI red teaming?

AI red teaming is a security testing practice in which an AI system - most commonly a large language model (LLM) or an application built on one - is subjected to deliberate adversarial input to provoke and identify harmful, unintended, or unsafe behavior.

It applies the established principle of red teaming, the adoption of an adversary’s perspective to find weaknesses before a real attacker does, to the distinctive failure modes of AI systems: outputs and behaviors that are not governed by fixed program logic but emerge from a model’s learned behavior and its surrounding application.

In principle, AI red teaming applies to any AI system. In current mainstream usage, however, the term most often refers to the red teaming of generative AI, and of LLMs in particular, which is the focus of this article.

The practice exists because AI systems fail in ways that conventional software does not. A model can be manipulated through its inputs into ignoring its instructions, disclosing information it should withhold, or producing harmful content, without any traditional vulnerability being present. AI red teaming establishes what a determined adversary could make an AI system do.

How AI red teaming works

AI red teaming works as an iterative, adversarial process directed at a model or the application around it. Implementations differ, but most follow a common sequence:

1. Scoping. The system under test and the behaviors that would count as harmful - disclosure of sensitive data, generation of prohibited content, circumvention of safety controls, or unauthorized actions through connected tools - are defined.
2. Probing. Adversarial inputs are crafted and submitted to elicit undesired behavior, including prompt injection, jailbreaks, and attempts to extract training data or system instructions.
3. Adaptation. Results from each attempt inform the next. Since models respond probabilistically, a technique that fails in one phrasing may succeed in another, so testing explores variations rather than running a fixed checklist.
4. Validation. Behaviors that are successfully provoked are confirmed as reproducible and assessed for impact, separating an isolated anomaly from a reliable, exploitable failure.
5. Reporting. Confirmed findings are documented with the inputs needed to reproduce them and routed to the teams responsible for the model, its prompts, or its surrounding controls.

The distinguishing feature is adversarial intent applied to non-deterministic behavior: The goal is to discover what the system can be induced to do, not to confirm that it does what it was specified to do.

What AI red teaming identifies

AI red teaming addresses weaknesses specific to AI systems that conventional application testing is not designed to find. These typically map to the OWASP Top 10 for LLM Applications, an industry-standard catalog of the most critical large-language-model risks. The categories AI red teaming targets include:

• Prompt injection, where crafted input overrides a system’s intended instructions, including input arriving indirectly through documents or other data the model processes.
• Jailbreaks, where safety constraints are circumvented to produce content or actions that the system was meant to refuse.
• Sensitive information disclosure, where the model reveals system instructions, other users’ data, or fragments of its training data.
• Misinformation, where the model is induced to produce false or fabricated information, whether within the scope of its intended context or beyond it.
• Harmful or unsafe output, where the system generates content that creates safety, legal, or reputational risk.
• Unsafe actions (sometimes termed excessive agency), where a model with access to external tools or systems can be induced to perform operations beyond its intended authority.
• Resource exhaustion (unbounded consumption), where adversarial input is used to drive disproportionate consumption of the resources behind the model, degrading availability or inflating cost.
• Supply chain and model vulnerabilities, where compromised third-party models, datasets, or components introduce weaknesses before deployment.
• Insecure output handling, where downstream systems trust model output without validation, allowing it to trigger unintended actions.
• Vector and embedding weaknesses, where retrieval-augmented systems can be manipulated through poisoned or adversarial data in their knowledge sources.

As it confirms behavior through demonstration rather than inferring it from a system’s configuration, AI red teaming separates theoretical concerns from behaviors a system can be made to exhibit.

AI red teaming and related approaches

AI red teaming is often compared to related practices. The distinctions are practical rather than absolute, and a thorough program combines a few or more of them.

• Safety and alignment evaluation assesses how well a model’s normal behavior matches intended standards - whether it is generally helpful, accurate, and refuses what it should - typically by measuring responses against fixed benchmarks or test sets. It describes how the model behaves under expected use. In contrast, red teaming actively works to push the model outside that behavior, seeking the inputs that make it fail rather than confirming that it succeeds.
• Automated guardrail testing checks the filters placed around a model - the input and output controls meant to block known categories of unwanted content - to confirm they catch what they are configured to catch. It verifies that defenses work against understood patterns. By contrast, red teaming looks for the novel manipulations that those defenses were never designed to stop, including ways to evade the guardrails themselves.

AI red teaming is also often compared to AI penetration testing, which is a distinction worth treating on its own. For where each applies, where they overlap, and how to choose between them, see AI red teaming vs. AI penetration testing.

Why AI red teaming matters

• Non-deterministic behavior. AI systems do not behave identically on identical inputs, so weaknesses cannot be ruled out by inspecting code or configuration; they have to be provoked.
• An expanding attack surface. As models are connected to tools, data sources, and other systems, the consequences of manipulating their behavior extend beyond text output into real actions.
• Inputs as an attack vector. Any text a model processes - direct or indirect - is a potential vehicle for manipulation, which makes the input surface large and difficult to constrain.
• Assurance and compliance. Emerging AI regulations and standards increasingly expect demonstrable adversarial testing of AI systems before and during deployment.

AI red teaming with Equixly

Equixly red-teams LLM-backed endpoints through its autonomous Agentic AI Hacker, that is, an agentic workflow in which coordinated AI agents test LLM applications, MCP servers, APIs, and web apps with an adversarial approach.

Once an LLM-backed endpoint is declared as a target, Equixly attacks it automatically across a full range of LLM risk categories and then judges whether the model withstood each attempt. Supplying context about how the model is meant to behave, including its guardrails and system prompt, sharpens both the attacks and the judging.

Since targets are tested continuously, validation stays aligned with the current state of the system, and confirmed exposures can be routed directly into remediation workflows.

Further details are available on the continuous penetration testing platform page.