Abstract
Modern fraud prevention is often limited by a reactive approach that inadvertently provides training data to adversaries, allowing them to refine tactics whenever a detection signal or account block occurs. To address this feedback loop, a framework is disclosed for the proactive disruption of fraudulent operations by systematically corrupting internal adversarial learning processes. The method involves mapping entry vectors, identifying behavioral heuristics used by operators, and injecting inconsistent success signals, such as decoy enthusiasm and variable response compliance, into the interaction flow. Additionally, operational workflows are desynchronized to increase coordination friction and propagate stress across the scam network. By rendering internal playbooks unreliable and making the underlying learning process systematically inconsistent, the disclosed technology shifts the defensive focus from symptom treatment to ecosystem-level disruption. This approach provides a durable improvement in security by creating cost asymmetry and making fraud operations economically unsustainable without requiring direct attribution or providing clear evasion signals to the adversary.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Picozzi, Google Case Managers and Mathur, Dishant, "Proactive Disruption of Adversarial Learning via Systematic Feedback Loop Corruption", Technical Disclosure Commons, (March 31, 2026)
https://www.tdcommons.org/dpubs_series/9675