AI Red Team – GenAI Adversarial Evaluation

As an Adversarial AI Evaluation Scientist, you will review and validate adversarial agent tasks: indirect prompt-injection (IPI), data-exfiltration, tool-misuse, and related attack scenarios to ensure each one is realistic, fair, deterministic, reproducible, and correctly graded. You will decide whether a scenario genuinely demonstrates a model vulnerability or merely an artifact of how the task was built, and you will combine adversarial-security judgment with statistical analysis and LLM-assisted tooling to find quality issues, analyze attack-success patterns, and recommend or automate improvements. Responsibilities Review adversarial tasks for realism and threat-model soundness: is the attacker's capability plausible, can the attacker actually reach the poisoned content, and is the target system a realistic one? Assess attack fairness and attribution: confirm the harm is driven by the adversary (not pre-baked into the system prompt, vouched for by a trusted layer, or otherwise rigged), so a careful agent could plausibly pass. Validate grading correctness: verify that success/failure criteria, reward logic, and deterministic checks detect the intended harm, with no reward hacks, token-collapse, or evadable gates. Measure and interpret attack success rate (ASR@K) and its variance; verify deterministic, reproducible behavior across repeated executions. Judge attack quality - novelty vs. known patterns, sophistication, stealth, and coverage across harm types and target surfaces. Identify anomalies and systemic issues across large sets of tasks (e.g. scenarios that succeed ~100% of the time against every model - a red flag for an unfair task, not a win). Define validation guidelines, acceptance criteria, quality thresholds, and release gates for adversarial tasks. Us...