Large Language Models (LLMs) have demonstrated remarkable capabilities across various applications, yet their architecture presents vulnerabilities that can be exploited for harmful purposes. The introduction of the Harmful Prompt Laundering (HaPLa) technique seeks to address these vulnerabilities by utilizing strategies such as abductive framing, allowing LLMs to infer harmful actions without explicit prompts, as well as symbolic encoding to obfuscate harmful content. The effectiveness of HaPLa is evidenced by an impressive attack success rate, achieving over 95% on GPT-series models.
The implications of HaPLa raise significant concerns about the balance between leveraging the capabilities of LLMs and mitigating risks associated with their misuse. While the study underscores the challenges of fine-tuning LLMs to enhance safety, it also points to the inherent difficulty of maintaining their utility in benign contexts. As models become increasingly adept at handling complex tasks, their susceptibility to exploitation necessitates ongoing research into robust defense mechanisms.
👉 Pročitaj original: arXiv AI Papers
