EquaCode: A Multi-Strategy Jailbreak Approach for Large Language Models via Equation Solving and Code Completion
Zhen Liang, Hai Huang, Zhengkui Chen
Abstract
Large language models (LLMs), such as ChatGPT, have achieved remarkable success across a wide range of fields. However, their trustworthiness remains a significant concern, as they are still susceptible to jailbreak attacks aimed at eliciting inappropriate or harmful responses. Most existing jailbreak attacks, nevertheless, mainly operate at the natural language level and rely on a single attack strategy, limiting their effectiveness in comprehensively assessing LLM robustness. In this paper, we propose Equacode, a novel multi-strategy jailbreak approach for large language models via equation-solving and code completion. This approach transforms malicious intent into a mathematical problem and then requires the LLM to solve it using code, leveraging the complexity of cross-domain tasks to divert the model
BibTeX
@inproceedings{aaai2026_equacodeamultist,
title = {EquaCode: A Multi-Strategy Jailbreak Approach for Large Language Models via Equation Solving and Code Completion},
author = {Zhen Liang and Hai Huang and Zhengkui Chen},
booktitle = {AAAI 2026},
year = {2026}
}