BlueHat India 2025: Agentic AI: Simulating Autonomous Adversaries with AI-Driven Red Teaming

Summary

In this presentation, Shrea, a Microsoft threat hunter, explores the transformative role of agentic AI in cybersecurity. Using the metaphor of a dynamic escape room, she explains how autonomous AI attackers learn, reason, and adapt in real-time, unlike traditional red teaming. She introduces the Aether research model, which simulates attacker intent and decision-making, leveraging MITER ATT&CK and Atlas frameworks. Through real-world examples like credential stuffing and enterprise penetration, Shrea demonstrates Aether's adaptive tactics, high detection evasion, and predictive reasoning. The talk emphasizes the need for defenses that evolve alongside intelligent threats, preparing for attacks that have never been seen before.

Takeaways

• 🧠 Agentic AI operates like a highly adaptive attacker in cybersecurity simulations, learning and reasoning in real-time rather than following predefined playbooks.
• 🔐 Traditional red teaming relies on static logic and predefined steps, making it less effective against dynamic threats, whereas agentic AI adapts tactics based on environment feedback.
• 🎯 The goal of agentic AI in simulations is to achieve objectives such as data exfiltration or initial access while avoiding detection, mimicking real attacker behavior.
• 👀 Components of agentic AI include Scout Sam (data collector), an analyst (LLM-driven reasoning), a planner (mission brain), and an executor (autonomous actions).
• 🔄 Agentic AI continuously learns from successes and failures, registering outcomes in a feedback loop to improve subsequent operations.
• 📊 Frameworks like MITER ATT&CK and Atlas guide agentic AI by providing real-world and AI-specific threat behaviors, enabling realistic simulations.
• 💡 Aether is a research model developed to simulate not just attacker techniques, but intent, using autonomous reasoning and contextual memory to mimic intelligent attackers.
• 🚀 In real-world simulations, agentic AI can bypass defenses like EDRs, switch tactics mid-operation, and perform sophisticated actions such as credential stuffing and lateral movement.
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• 📈 Performance metrics of Aether show high success rates in executing attack simulations, with strong detection evasion, forecast accuracy, and overall kill chain completion.
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• 🌐 The future vision of agentic AI includes multi-agent collaboration, real-time TTP simulation, and adaptation across endpoints, cloud, and identity-based environments, preparing defenses for unseen threats.
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• ⚡ Key message: Cybersecurity defense strategies must evolve alongside adaptive threats, leveraging intelligent simulations to anticipate attacks rather than just reacting to known exploits.

Q & A

What is the main objective of the agentic AI described in the talk?

The main objective of the agentic AI is to autonomously navigate complex environments, simulate attacker behavior, and achieve specified goals such as accessing targets and exfiltrating data, all while learning and adapting without human intervention.

How does agentic AI differ from traditional red teaming?

Agentic AI differs from traditional red teaming in that it doesn't follow a predefined playbook. It learns, reasons, adapts to defensive measures in real time, and simulates attacker thinking, whereas traditional red teaming relies on static logic, checklists, and manual adaptation.

What are the key components of the agentic AI architecture?

The key components include Scout Sam (data collection), the Analyst (LLM-driven reasoning), the Planner or Mission Brain (goal-oriented strategy), the Executor (task performance), and a fallback learning loop connected to a vector database for continuous adaptation.

What is the purpose of the fallback loop in agentic AI?

The fallback loop registers what tactics succeed or fail, enabling the AI to learn from experience and improve future responses, making the AI faster and smarter with each iteration.

Can you explain how agentic AI adapts during a real-world simulation?

In simulations, agentic AI adapts by changing tactics in response to defensive blocks, such as switching from PowerShell to mshta.exe after detecting EDR intervention, learning from prior exercises, and rerouting its attack autonomously to avoid detection.

What are MITER ATT&CK and ATLAS frameworks, and how are they used with agentic AI?

MITER ATT&CK is a framework that maps real-world attacker techniques, while ATLAS focuses on AI-specific attack behaviors. Together, they guide agentic AI by providing both traditional and AI-adapted attack patterns to improve simulation accuracy and intelligence.

What is Aether, and how does it relate to agentic AI?

Aether is a research model designed to simulate agentic AI in threat hunting and reasoning. It operates autonomously, learns from environment telemetry, applies MITER and ATLAS frameworks, and thinks like an intelligent attacker rather than following predefined scripts.

How does Aether process and reason about its environment?

Aether collects telemetry and defensive logs, executes tasks, observes outcomes, and applies LLM reasoning to determine what worked, what failed, and what tactics to try next, essentially reflecting on its actions like a strategist.

What metrics demonstrate Aether's effectiveness in simulations?

Aether's metrics include an average TTP pivot trans of 3.2%, TTP coverage of 6.7%, detection evasion rate of 58%, forecast accuracy of 74%, and overall kill chain completion of 83%, reflecting its ability to bypass defenses and predict outcomes effectively.

Why is agentic AI considered the next leap in cyber threats?

Agentic AI represents the next leap because it operates autonomously, learns and adapts in real time, reasons like a human attacker, and can execute sophisticated attacks without human guidance, making it more unpredictable and challenging to defend against than traditional methods.

How can Aether or agentic AI potentially be used by defenders in the future?

Aether could be adapted for defensive purposes, enabling simulation of potential attacks on cloud, identity, and endpoint systems, testing security controls proactively, and preparing for threats that have not yet been observed in real environments.

What was the purpose of the 'Operation Lateral Ghost' simulation mentioned in the talk?

'Operation Lateral Ghost' was a demonstration of Aether’s capabilities, where the AI autonomously conducted reconnaissance, privilege escalation, and data exfiltration on a heavily defended machine, showcasing its reasoning, adaptation, and stealth in a real-world-like scenario.