Adversarial Prompt Optimization in LLMs: HijackNet’s Approach to Robustness and Defense Evasion

This paper introduces HijackNet, an adversarial prompt hijacking framework designed to improve hijacking success rates and generalization in multi-lingual tasks. Combining Neural Architecture Search (NAS), Reinforcement Learning (RL), and Multi-Objective Optimization (MOO), HijackNet generates adversarial prompts that adapt to target tasks while bypassing model defense mechanisms. This framework optimizes hijacking success, defense evasion, and task completion through a combination of a prompt generator and adversarial optimizer. We introduce a Defense Robustness Score (DRS) to evaluate the stability of prompts in adversarial settings. HijackNet improves adversarial prompt generation, advancing the security and robustness of large language models in adversarial environments.