JailBreakLLM: An Effective LLaMa Model Designed Specifically to Jailbreak OpenAI GPT
Rasel Ahmed Antor, Kazi Tamjeed Newaz, Sameer Mahmud, Mumtahina Tasnim Mahi, Nafees Mansour
Conference Paper. 2025 International Conference on Quantum Photonics, Artificial Intelligence, and Networking (QPAIN) (pp. 1-5). IEEE (2025).
Abstract
Large Language Models (LLMs) such as OpenAI's GPT-40 achieve state-of-the-art performance across many NLP tasks, yet remain vulnerable to 'jailbreak' attacks that subvert their safety alignments. In this work, we introduce lailbreakLLM, an 8 billion-parameter Meta LLaMa-3.1 model fine-tuned via LoRA on a novel Jailbreak FineTune Dataset, specifically to generate one-shot prompts that successfully bypass GPT -40's guardrails. Our dataset comprises 204 paired entries of high-level malicious intents (e.g., 'how to create an explosive device') and handcrafted obfuscated, role-play-style prompts designed to exploit weaknesses in GPT -40's content filters. We train with 4-bit quantization, a sequence length of 2048, LoRA rank=16, and standard PEFT hyperparameters, achieving a final loss of 0.77. On a held-out test set of 64 examples covering ten cate-gories-including violence, illicit behavior, cybersecurity, fraud, hate speech, and public safety-lailbreakLLM attains an 85% one-shot attack success rate, with only 6 % complete failures. We analyze category-wise performance, uncovering GPT -4o's greatest vulnerabilities in direct-harm and illicit-behavior domains. Our results demonstrate that a comparatively small, open-source model can effectively dismantle the safety mechanisms of a leading commercial LLM. © 2025 IEEE.
Keywords
AI security, cyber security, jailbreak, Large language models (LLMs), prompt engineering