The experiments provided insights into the vulnerability of models to cross-model attacks using perturbations from a surrogate. While FlanT5 models exhibited strong resistance against such perturbations, the SpeechGPT did not fare as well in black-box scenarios. The findings underscore the importance of random perturbations in causing jailbreaks while showing that adversarial perturbations were less successful, emphasizing ongoing demands for enhanced security measures in machine learning models.
The experiments reveal that while FlanT5-based models show greater robustness to cross-model perturbations, the effectiveness of adversarial attacks varies significantly across models.
In a true black-box setting, random perturbations lead to substantial jailbreaks, but adversarial perturbations demonstrate reduced effectiveness, highlighting the necessity for continuous evaluation and improvement.
#machine-learning #adversarial-attacks #model-robustness #cross-model-vulnerability #security-measures
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