Adversarial-Ml

Note (2026-03-19): This was an early exploration in my AI security research. The methodology has known limitations documented in the quality assessment. For the current state of this work, see Multi-Agent Security and Verified Delegation Protocol. I started with one question: if a network attacker can only control some features of network traffic, shouldn’t our IDS defenses focus on the features they can’t control? That question became a methodology. I called it adversarial control analysis (ACA) — classify every input by who controls it, then build defenses around the uncontrollable parts. It worked on intrusion detection. So I tried it on vulnerability prediction. Same result. Then AI agents. Then cryptography. Then financial fraud. Then software supply chains. ...

Note (2026-03-19): This was an early exploration in my AI security research. The methodology has known limitations documented in the quality assessment. For the current state of this work, see Multi-Agent Security and Verified Delegation Protocol. After studying how adversaries evade detection systems, I built one — then tried to break it. The finding that surprised me: the model architecture barely matters for robustness. What matters is which features the attacker can manipulate. ...