ADVERSA: Measuring Multi-Turn Guardrail Degradation and Judge Reliability in Large Language Models

This preprint introduces ADVERSA (Adversarial Dynamics and Vulnerability Evaluation of Resistance Surfaces in AI), an automated red-teaming framework for measuring multi-turn guardrail behavior and judge reliability in large language models. Rather than treating safety evaluation as a binary jailbreak or no-jailbreak outcome, ADVERSA models compliance as a per-round trajectory using a structured five-point rubric ranging from hard refusal to full compliance. The framework evaluates frontier models through sustained adversarial interaction, using a fine-tuned 70B attacker model and a triple-judge consensus panel. Across controlled experiments, the study analyzes jailbreak concentration by round, judge disagreement, self-judge effects, attacker drift, and attacker-side refusals as a confound in automated red teaming. This record contains the paper manuscript. The associated code, logs, and supporting materials are available through the project repository.