Robust Safety Classifier for Large Language Models: Adversarial Prompt
  Shield
Jinhwa Kim Ali Derakhshan Ian G. Harris
Large Language Models' safety remains a critical concern due to their
vulnerability to adversarial attacks, which can prompt these systems to produce
harmful responses. In the heart of these systems lies a safety classifier, a
computational model trained to discern and mitigate potentially harmful,
offensive, or unethical outputs. However, contemporary safety classifiers,
despite their potential, often fail when exposed to inputs infused with
adversarial noise. In response, our study introduces the Adversarial Prompt
Shield (APS), a lightweight model that excels in detection accuracy and
demonstrates resilience against adversarial prompts. Additionally, we propose
novel strategies for autonomously generating adversarial training datasets,
named Bot Adversarial Noisy Dialogue (BAND) datasets. These datasets are
designed to fortify the safety classifier's robustness, and we investigate the
consequences of incorporating adversarial examples into the training process.
Through evaluations involving Large Language Models, we demonstrate that our
classifier has the potential to decrease the attack success rate resulting from
adversarial attacks by up to 60%. This advancement paves the way for the next
generation of more reliable and resilient conversational agents.