🤖 AI Summary
Traditional AI red-teaming relies on manual, task-specific procedures that are time-consuming and difficult to reuse, thereby limiting the efficiency of security evaluations. This work proposes a new red-teaming paradigm for the agent era: a natural language–driven AI red teaming agent built on the Dreadnode SDK that automatically orchestrates and executes end-to-end testing workflows encompassing attacks, transformations, and scoring. The framework unifies security assessment for both traditional machine learning and generative AI systems, supporting multi-agent, multilingual, and multimodal targets. It enables access to over 45 attack strategies, 450 transformations, and 130 scorers without requiring manual coding. In a case study with Meta’s Llama Scout, natural language instructions alone achieved an 85% attack success rate (severity 1.0), reducing testing cycles from weeks to hours.
📝 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