defense arXiv Aug 19, 2025 · Aug 2025
Zixin Rao, Youssef Mohamed, Shang Liu et al. · University of Georgia · Egypt-Japan University of Science and Technology +1 more
Multi-task framework jointly detects LLM-generated text and attributes authorship to specific LLMs across languages
Output Integrity Attack nlp
Large Language Models (LLMs), such as GPT-4 and Llama, have demonstrated remarkable abilities in generating natural language. However, they also pose security and integrity challenges. Existing countermeasures primarily focus on distinguishing AI-generated content from human-written text, with most solutions tailored for English. Meanwhile, authorship attribution--determining which specific LLM produced a given text--has received comparatively little attention despite its importance in forensic analysis. In this paper, we present DA-MTL, a multi-task learning framework that simultaneously addresses both text detection and authorship attribution. We evaluate DA-MTL on nine datasets and four backbone models, demonstrating its strong performance across multiple languages and LLM sources. Our framework captures each task's unique characteristics and shares insights between them, which boosts performance in both tasks. Additionally, we conduct a thorough analysis of cross-modal and cross-lingual patterns and assess the framework's robustness against adversarial obfuscation techniques. Our findings offer valuable insights into LLM behavior and the generalization of both detection and authorship attribution.
llm transformer University of Georgia · Egypt-Japan University of Science and Technology · University of Louisville
ML Security Papers