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This paper introduces an AI red teaming agent built on the Dreadnode SDK that automates the construction of adversarial workflows, significantly reducing the time required for red teaming from weeks to hours. The agent leverages a unified framework encompassing 45+ adversarial attacks, 450+ transforms, and 130+ scorers, enabling probing of diverse AI systems, including multi-agent, multilingual, and multimodal targets. A case study red teaming Meta Llama Scout demonstrates an 85% attack success rate, highlighting the agent's effectiveness.
Forget weeks of manual scripting: this AI red teaming agent lets you launch sophisticated attacks with natural language, slashing vulnerability discovery time.
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
