Adversarial robustness engineering for ML/AI—evasion, poisoning, extraction, membership-inference
threat models; robust training, sanitization, detectors; ASR/certified evals; lab model attacks;
data-pipeline integrity; production I/O guardrails (classical ML and LLM/multimodal). Use for
adversarial examples, robustness suites, poison audits, deploy guardrails—not LLM app red team
(ai-redteam), governance (ai-risk-governance), safety classifier R&D (ml-research-engineer-safeguards),
safeguard serving (ml-infrastructure-engineer-safeguards), privacy research
(privacy-research-engineer-safeguards), AppSec pentest (penetration-tester).
