Adversarial Machine Learning/ AML

Definition

Adversarial Machine Learning is study and defense of attacks that manipulate AI through crafted inputs, poisoned data, or model theft. In defense applications, it prepares defense systems for enemies who attack the model, not only the platform. The hard part is adaptive attacks and incomplete test coverage, especially when systems are deployed across contested links, coalition boundaries, and mixed human-machine teams. KhanBMS treats it as a standing threat model for every KhanBMS AI module, tying the concept back to modular command, edge execution, and auditable authority.

Reference attributes

Layer

AI security discipline

Operational value

Prepares defense systems for enemies who attack the model, not only the platform

Primary risk

Adaptive attacks and incomplete test coverage

KhanBMS role

A standing threat model for every KhanBMS AI module

Related terms

• Evasion Attacks
  Inputs crafted at inference time to make a model misclassify or choose the wrong action.
• Data Poisoning
  Attack that corrupts training or fine-tuning data to implant bad behavior or degrade performance.
• Model Extraction
  Attack that recreates or approximates a model by querying it and observing outputs.
• AI Red Teaming
  Structured adversarial testing of AI systems to expose unsafe, biased, exploitable, or brittle behavior.