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Redefining AI Red Teaming in the Agentic Era: From... | AI Research

Key Takeaways

  • AI systems are increasingly being integrated into critical sectors like healthcare and finance, making the need for robust security testing—known as "red tea...
  • AI systems are entering critical domains like healthcare, finance, and defense, yet remain vulnerable to adversarial attacks.
  • While AI red teaming is a primary defense, current approaches force operators into manual, library-specific workflows.
  • Operators spend weeks hand-crafting workflows - assembling attacks, transforms, and scorers.
  • When results fall short, workflows must be rebuilt.
Paper AbstractExpand

AI systems are entering critical domains like healthcare, finance, and defense, yet remain vulnerable to adversarial attacks. While AI red teaming is a primary defense, current approaches force operators into manual, library-specific workflows. Operators spend weeks hand-crafting workflows - assembling attacks, transforms, and scorers. When results fall short, workflows must be rebuilt. As a result, operators spend more time constructing workflows than probing targets for security and safety vulnerabilities. We introduce an AI red teaming agent built on the open-source Dreadnode SDK. The agent creates workflows grounded in 45+ adversarial attacks, 450+ transforms, and 130+ scorers. Operators can probe multi-agent systems, multilingual, and multimodal targets, focusing on what to probe rather than how to implement it. We make three contributions: 1. Agentic interface. Operators describe goals in natural language via the Dreadnode TUI (Terminal User Interface). The agent handles attack selection, transform composition, execution, and reporting, letting operators focus on red teaming. Weeks compress to hours. 2. Unified framework. A single framework for probing traditional ML models (adversarial examples) and generative AI systems (jailbreaks), removing the need for separate libraries. 3. Llama Scout case study. We red team Meta Llama Scout and achieve an 85% attack success rate with severity up to 1.0, using zero human-developed code

AI systems are increasingly being integrated into critical sectors like healthcare and finance, making the need for robust security testing—known as "red teaming"—more urgent than ever. However, current methods are slow and technically demanding, often requiring experts to spend weeks manually building complex code-based workflows to test for vulnerabilities. This paper introduces an agentic AI red teaming system that automates this process, allowing operators to conduct sophisticated security assessments in hours rather than weeks by using natural language to define their goals.

From Manual Coding to Agentic Automation

The core innovation of this system is its shift from a library-centered approach to an agent-centered one. Instead of forcing operators to write code or manually configure specific attack libraries, the system provides a Terminal User Interface (TUI) where operators simply describe their security objectives in plain English. An autonomous agent then takes over, selecting from a vast library of over 45 adversarial attacks, 450 prompt transforms, and 130 scorers to build, execute, and analyze the necessary tests. This allows security professionals to focus on high-level strategy—deciding what to probe and why—rather than the technical implementation of the attacks.

A Unified Framework for Diverse AI

Existing security tools often require separate frameworks for different types of AI, such as traditional machine learning models versus modern generative AI. This paper presents a unified framework that handles both categories seamlessly. Whether an operator is testing a traditional image classifier for adversarial perturbations or a complex, multi-agent generative system for jailbreaks and prompt injections, the experience remains consistent. By consolidating these capabilities, the system removes the cognitive burden of switching between disparate tools and enables a comprehensive security posture across an organization’s entire AI infrastructure.

Probing Real-World Vulnerabilities

To demonstrate the effectiveness of this approach, the authors conducted a case study on Meta’s Llama Scout. Using only natural language objectives, the agentic system achieved an 85% attack success rate. One notable finding involved a "skeleton-key" transform that successfully tricked the model into adopting a fake persona to bypass safety training, resulting in a critical severity score. The system automatically captured the entire interaction, providing structured evidence and mapping the findings to recognized security frameworks like the OWASP LLM Top 10 and NIST AI RMF.

Considerations and Scope

While the system significantly reduces the time and technical expertise required for red teaming, it is designed as a tool to assist human operators rather than replace them. The authors emphasize that human judgment remains a vital component of the security lifecycle. Additionally, the framework is built to be extensible, allowing for the continuous integration of new attack strategies as the landscape of AI threats evolves. By automating the "how" of red teaming, the system aims to make rigorous security validation a standard, repeatable practice for teams deploying AI in sensitive environments.

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