Adversarial ML coverage for engineers shipping ML systems. Membership inference, model extraction, evasion attacks, training-data extraction, backdoors — focused on what's exploitable against deployed models and what defenders can actually do about it. PoCs against open models, behavioral analysis for closed ones.
The math, the cost curve, and why optimization-based attacks are now within reach of solo practitioners. With reproducible setup and what defenders actually need to do.
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