AI AI Red Team Engineering notes, research references, and field notes on AI systems, responsible AI, governance, and compliance.
Topic

Adversarial ML

Adversarial machine learning attacks, taxonomies, and mitigations across the ML lifecycle.

adversarial mlevasionpoisoningprivacy attacknist ai 100-2
Evergreen Overview

Adversarial ML gives useful language for thinking about evasion, poisoning, privacy attacks, and model misuse. It becomes especially valuable when teams need to distinguish classic model risk from the broader application-layer risks found in modern LLM systems.

What this page helps separate
  • Model-centric attacks versus system-centric AI failures
  • Poisoning, evasion, privacy, and misuse in practical terms
  • Where adversarial ML taxonomies still help in generative AI discussions
Why it matters operationally
  • It creates cleaner language for policy, governance, and technical review
  • It helps teams choose the right controls for the right threat model
  • It prevents application-layer AI risk from being described too vaguely
Who this page is for
  • Security teams linking GenAI issues to broader model risk
  • Researchers working across predictive and generative AI
  • Practitioners who need disciplined taxonomy and terminology
References

Current notes, events, and source material

These items are included because they add useful evidence, framing, implementation detail, or upcoming context for teams working in this area.