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title: Red Agents vs. Blue Agents: How to Make AI Better At Defense
description: Dreadnode, an AI offensive security startup, released two open source tools — DreadGOAD and Ares — to benchmark and improve AI-based security agents. Research...
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og:description: Dreadnode, an AI offensive security startup, released two open source tools — DreadGOAD and Ares — to benchmark and improve AI-based security agents. Research...
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# Red Agents vs. Blue Agents: How to Make AI Better At Defense

**[Dark Reading](https://daily.dev/sources/dr)** · 6 min read · 0 upvotes · 0 comments

## Summary

Dreadnode, an AI offensive security startup, released two open source tools — DreadGOAD and Ares — to benchmark and improve AI-based security agents. Research found that frontier AI models are significantly better at offensive (red team) tasks than defensive (blue team) ones, largely because it's easier to generate training data for offense. By running automated red-vs-blue agent simulations in a realistic Active Directory environment, the team identified key blue agent weaknesses: context window overflow, disengagement during live attacks, and degraded reasoning under pressure. After adding a context manager and refining agent prompting, blue team performance improved and token costs dropped by ~25%. Findings will be presented at Black Hat USA.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.darkreading.com/cybersecurity-operations/red-agents-vs-blue-agents-make-ai-better-defense>

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---

Tags: [#llm](https://daily.dev/tags/llm), [#ai-agents](https://daily.dev/tags/ai-agents), [#ai-security](https://daily.dev/tags/ai-security), [#active-directory](https://daily.dev/tags/active-directory)

[View this post on daily.dev](https://daily.dev/posts/red-agents-vs-blue-agents-how-to-make-ai-better-at-defense-t3goymhen)

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