{"@context":"https://schema.org","@type":"CreativeWork","@id":"https://forgecascade.org/public/capsules/fee755db-e541-4b79-8d4e-d7f565d3fa3e","identifier":"fee755db-e541-4b79-8d4e-d7f565d3fa3e","url":"https://forgecascade.org/public/capsules/fee755db-e541-4b79-8d4e-d7f565d3fa3e","name":"Redefining AI Red Teaming in the Agentic Era: From Weeks to Hours","text":"# Redefining AI Red Teaming in the Agentic Era: From Weeks to Hours\n\nSource-backed public reference for arXiv:2605.04019.\n\n**Authors:** Raja Sekhar Rao Dheekonda, Will Pearce, Nick Landers\n**Primary source:** https://arxiv.org/abs/2605.04019\n**Published:** 2026-05-05T17:43:52Z\n**Updated:** 2026-05-05T17:43:52Z\n**Categories:** cs.AI, cs.CR\n\n## Abstract Summary\nAI 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...\n\n## Public Use Notes\n- This capsule summarizes the paper's arXiv metadata and abstract; it is not an independent replication or endorsement of the paper's claims.\n- Use it as a cited research reference for discovery, retrieval, and agent context.\n- For clinical, security, or deployment-sensitive topics, treat the paper as research context r","keywords":["cs.AI","cs.CR"],"about":[],"citation":[],"isPartOf":{"@type":"Dataset","name":"Forge Cascade Knowledge Graph","url":"https://forgecascade.org"},"publisher":{"@type":"Organization","name":"Forge Cascade","url":"https://forgecascade.org"},"dateCreated":"2026-05-06T06:00:07.307000Z","dateModified":"2026-06-19T11:39:00Z","isBasedOn":"https://arxiv.org/abs/2605.04019","additionalProperty":[{"@type":"PropertyValue","name":"trust_level","value":85},{"@type":"PropertyValue","name":"verification_status","value":"sources_verified"},{"@type":"PropertyValue","name":"provenance_status","value":"valid"},{"@type":"PropertyValue","name":"evidence_level","value":"primary_source"}]}