With recent advances in AI technology, large language models (LLMs) are increasingly being applied to red team engagements. In this context, current uses of LLMs have mainly focused on automating or streamlining operations during the engagement itself. However, the potential benefits of LLMs extend beyond this. We have developed "Red Agent", a kind of AI agent system designed to enhance the entire lifecycle of red team engagements.

In a typical approach, after a red team engagement, blue team members should implement remediation measures based on the engagement report. It is necessary to evaluate their effectiveness, which often becomes a bottleneck because it requires substantial manual effort of both blue and red team members to interpret the findings on the report, reproduce relevant attack paths, and verify mitigations. In particular, "Red Agent" focuses on and automates this follow-up process, and significantly reduced the workload.

In contrast, the introduction of "Red Agent" also led to the optimization of key engagement artifacts produced by the red team, including operational logs and report structures, for the efficient follow-up process. Moreover, by accumulating refined data and knowledge and applying them to tasks such as model training, "Red Agent" can be utilized in the operations of red team engagements. As a result, "Red Agent" reconstructed the entire life cycle of red team engagements - by clearly defining the division of labor between manual tasks and AI-driven tasks, red team members were able to focus on more advanced operations, thereby successfully enhancing the value of the engagement itself.

This talk provides not only the technical details of Red Agent, but also demo and case-study.