AI Red Teaming: NIST Frameworks vs. Garak Tools in 2026

The Dual Nature of AI Red Teaming

AI red teaming in 2026 has evolved into a sophisticated practice, yet many discussions flatten its components into a single category. This oversight is critical because effective red teaming relies on a clear understanding of two distinct, yet complementary, layers: the strategic frameworks used for reporting and board-level communication, and the practical tools deployed for hands-on testing and vulnerability discovery. These are not interchangeable; one is for citing in high-level risk assessments, the other for executing on a Tuesday.

The distinction is crucial for anyone tasked with securing AI systems. Without this separation, efforts can become unfocused, leading to superficial assessments that fail to identify genuine vulnerabilities or to implement actionable mitigations. The frameworks provide the language and structure for understanding risk, while the tools provide the means to uncover and exploit those risks in practice.

Taxonomies for Reporting and Remediation

The layer that performs the heavy lifting in terms of actionable insight is the classification and taxonomy layer. This is where abstract AI misbehavior is translated into concrete, addressable issues for security and development teams. It provides a common language for describing vulnerabilities, their impact, and potential mitigation strategies.

MITRE ATLAS stands as a prime example in this category, serving as the adversarial AI counterpart to the well-established MITRE ATT&CK framework. ATLAS provides a structured taxonomy of adversarial techniques targeting AI systems, with a particular focus on Large Language Models (LLMs). Techniques like LLM Prompt Injection are not just mentioned but are precisely categorized, allowing for detailed reporting and targeted defense development. For instance, prompt injection is split into direct and indirect variants (AML.T0051.000 and AML.T0051.001 respectively), offering granular detail that aids in understanding the attack vector and building specific defenses.

Similarly, the NIST AI Risk Management Framework (AI RMF), specifically AI 100-1, offers a comprehensive, yet more generalized, approach. It guides organizations through the lifecycle of AI systems, from design and development to deployment and monitoring, emphasizing risk identification, assessment, and mitigation. While it doesn't detail specific exploits like ATLAS, it provides the overarching structure for an AI risk management program. It helps organizations understand what categories of risk to look for—like bias, robustness, and security—and how to integrate AI risk management into existing enterprise risk frameworks. Citing NIST guidelines in board-level discussions lends credibility and demonstrates a commitment to responsible AI deployment.

These frameworks are essential for establishing a common vocabulary and a structured approach to AI risk. They enable organizations to articulate risks to stakeholders, prioritize mitigation efforts, and track progress over time. They are the documents you reference when building a compliance report or presenting your AI security posture to leadership.

Tools for Hands-On Vulnerability Discovery

Distinct from the strategic frameworks are the practical tools that security professionals use to actively probe AI systems for weaknesses. These are the engines that drive the red teaming process, enabling the discovery of vulnerabilities that might be missed by purely conceptual analysis. These tools are not typically cited in board decks; they are run on servers, often via command-line interfaces, to generate adversarial examples and uncover emergent behaviors.

Garak is a prominent example of such a tool. This Python-based scanner is designed to test LLMs against a battery of known adversarial attacks. It automates the process of sending carefully crafted prompts to an LLM and analyzing the responses for signs of compromise. Garak can identify vulnerabilities related to prompt injection, data leakage, denial of service, and more. Its strength lies in its ability to execute a wide range of tests rapidly and consistently, providing concrete evidence of a system's susceptibility to specific attack vectors. Running Garak is akin to performing a penetration test on a traditional application; it’s about actively seeking out flaws.

Other tools operate on similar principles but may focus on different aspects or AI models. For instance, tools might be developed to test the robustness of computer vision models against adversarial image perturbations, or to probe the fairness of recommendation systems by generating biased inputs. The common thread is their active, programmatic approach to uncovering vulnerabilities. They are the hands-on instruments that translate the abstract risks identified by frameworks into tangible security findings.