Balancing Transparency and Safety: A Framework for Responsible Disclosure of Adversarial Vulnerabilities in Aligned Language Models

Despite their remarkable progress in producing text that resembles that of a person, large language models (LLMs) remain susceptible to adversarial prompts that can circumvent alignment mechanisms and yield undesirable outputs. Although the dual-use dangers of adversarial tools have been addressed in previous studies, including our earlier work on the PAIR tool and its governance implications, a crucial gap remains in the responsible disclosure of transferable vulnerabilities, those that can harm multiple models. To responsibly disclose transferable adversarial vulnerabilities in LLMs, we present a technically sound governance system in this study. Through the use of PAIR, we empirically assess prompt transferability between GPT-4 and Vicuna-13B, demonstrating that even queries optimized for one model frequently succeed in transferring to the other. Our architecture differs from current standards, such as CVSS and CERT, in that it addresses this danger through modular disclosure phases, risk triage methodologies, and stakeholder-specific incentives. Additionally, we suggest dual-use risk taxonomies and gentle enforcement strategies that complement new AI safety guidelines. In the era of foundation models, our study offers a novel, practical approach to bridging the gap between technological vulnerability studies and ethical governance.