RL-powered attack simulation

• Agent goals and reward shaping (high level)

• How Azimuth explores state space and callgraphs

• Differences vs fuzzing and static rule checks

• Interpreting “found exploit” evidence

What this will cover

They learn from outcomes. They adapt to protocol-specific constraints.

Azimuth uses reinforcement learning agents to explore exploit paths.

RL-powered attack simulation

This page is a placeholder. We’ll expand it as the engine’s interfaces and outputs settle.