On January 23, 2025, OpenAI released research highlighting that longer inference times can enhance AI's defenses against adversarial attacks.

A heat map included in the research illustrated that longer inference times lead to a higher likelihood of attack failure, even when attackers deploy more resources.

Despite the overall positive findings regarding inference time, one specific type of attack related to harmful information maintained a consistent success rate, regardless of the inference time.

The research specifically involved OpenAI's models, o1-preview and o1-mini, and tested various attack types, including providing incorrect answers to math problems and a technique known as 'many-shot jailbreaking.'

'Many-shot jailbreaking' is a method where the AI is overwhelmed with numerous questions before being presented with a problematic one, aiming to exploit its ethical vulnerabilities.

Additionally, attackers can exploit inference time by causing the AI to waste time on irrelevant tasks, which undermines its defenses.

The research underscores the importance of inference time in improving AI robustness, particularly as these models are increasingly utilized in critical applications and decision-making roles.

Findings from the study indicate that as inference time increases, the success rate of attacks generally decreases, suggesting a strong correlation between longer processing times and enhanced robustness.

Adversarial attacks are designed to confuse AI models, which can result in unintended consequences or harm by developers.