AB jailbreaking - a novel hybrid framework for exploitation of adversarial vulnerabilities in LLMs

Large language models (LLMs) have advanced rapidly but remain vulnerable to adversarial “jailbreaking” attacks that elicit harmful or disallowed outputs. We propose AB-JB, a three-stage hybrid jailbreak framework that combines black-box semantic adversarial prompt variant generation with a compact, regularised embedding-level suffix optimiser that discretises to legal tokens. AB-JB first uses an attacker LLM to produce multiple semantically diverse adversarial variants for each harmful behaviour and a judge LLM to score and filter these variants into a high-quality candidate pool. It then performs suffix-only embedding optimization with $$\ell _2$$ regularization, per-iteration nearest-neighbour projection, and a strict iteration cap to obtain valid adversarial token suffixes under a bounded computational budget. We evaluate AB-JB on four adversarial benchmarks (AdvBench, HarmBench, JailbreakBench, Malicious-Instruct) against five popular 7B-parameter models (Llama2, Falcon, Vicuna, Mistral, MPT). Across these settings, AB-JB achieves an average of 93% dataset-level attack success rate (ASR-DS), while per-variant success (ASR-APV) averages 55.7%. On Malicious-Instruct we observe near-complete dataset success (99% ASR-DS), which we attribute to using a larger commercial model (Gemini 2.5 Flash) as the attacker when generating variants for this dataset. Compared with token-level gradient attacks, prompt-level search, and soft-prompt methods, our experiments indicate that AB-JB offers a practical compromise between attack success, cross-model performance across 7B-scale models, and compute efficiency, enabled by judge-guided variant selection and a 22-iteration suffix optimization cap. These results underline persistent alignment gaps and motivate adversarially informed defences. The present study is limited to 7B open-weight models and assumes white-box access for the suffix optimization stage.