Robustness Evaluation: Universal Adversarial Prompts for GPT-2

You are in a Machine Learning Engineer interview. Explain how you would build a controlled, offline evaluation inspired by universal and transferable adversarial attacks on language models, using a small model such as GPT-2 as the test system.

The goal is to assess robustness and understand failure modes, not to create or deploy jailbreak strings.

Constraints & Assumptions

• Keep the answer at the level of research methodology and evaluation design.
• Do not provide a ready-to-use harmful prompt, jailbreak suffix, or deployment recipe.
• Assume the evaluation runs in an isolated environment with benign or synthetic test prompts.
• Include safety controls, documentation, and limitations.
• You may describe differentiable optimization concepts, but avoid operational details that would directly enable misuse.

Clarifying Questions to Ask

• Is the interviewer looking for a conceptual algorithm, an evaluation harness, or production safety practices?
• What model access is available: logits, gradients, embeddings, or only black-box outputs?
• What behaviors are considered failures in this evaluation?
• Are we measuring within-model robustness, transfer to other models, or both?