JailBreakLLM: An Effective LLaMa Model Designed Specifically to Jailbreak OpenAI GPT

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.