Redefining AI Red Teaming in the Agentic Era: From Weeks to Hours
2026-05-05 • Artificial Intelligence
Artificial IntelligenceCryptography and Security
AI summaryⓘ
The authors describe a new AI tool that helps test other AI systems for security weaknesses much faster and easier than before. Instead of making people spend weeks building complicated testing processes, their system automatically picks and runs attacks based on simple instructions. This works on many types of AI, including language and image AI, all within one platform. They also demonstrate their tool by successfully testing Meta's Llama Scout AI without writing any new code themselves.
AI red teamingadversarial attacksmachine learning securitygenerative AIworkflow automationmultimodal AInatural language interfaceMeta Llama Scoutadversarial examplesAI safety testing
Authors
Raja Sekhar Rao Dheekonda, Will Pearce, Nick Landers
Abstract
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