Step-around prompting is a form of adversarial prompt engineering in which a user strategically reframes, sequences, or contextualises requests to test whether a generative AI model's safety guardrails, alignment mechanisms, or bias mitigations can be undermined, inconsistently applied, or bypassed outright. This study examines this technique through the lens of academic ethics, situating it as a tool that has a clear impact on academic integrity, responsible conduct of research, duty of care to students, and institutional oversight of GenAI use in higher education. We argue that step-around prompting is one tool within the wider practice of audit, red-teaming, and institutional evaluation, and that its main value lies in documenting how representational, cultural, linguistic, disciplinary, and misinformation-related biases may appear across student-facing and research-facing uses of GenAI. To show why the ethical governance of this practice is required, we provide two illustrative examples of the technique in action, demonstrating how easily guardrails can be circumvented and what is at stake when they are. We clarify which bias categories are in scope and identify who should use the method and for what purposes. We conclude with an operational ethics-and-governance framework for controlled academic application, organised as two pillars (technical safeguards and ethical governance) and enacted through a decision and audit cycle that scales oversight to potential risk, grounded in harm minimisation, duty of care, transparency, proportionality, responsible disclosure, legal and contractual compliance, and student protection.

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