Researchers including Dr. Najwa Aaraj developed ML-FEED, a new exploit detection framework using pattern-based techniques. The model is 70x faster than LSTMs and 75,000x faster than Transformers in exploit detection tasks, while also being slightly more accurate. The "ML-FEED" paper won best paper at the 2022 IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications. Why it matters: This research enables more efficient real-time security applications and highlights growing AI expertise in the Arab world.
MBZUAI researchers introduced Droid, a resource suite and detector family, at EMNLP 2025 designed to distinguish between AI-generated and human-written code. The project addresses the challenge of identifying AI-generated code in software development, considering the prevalence of AI-suggested code and the risks of obfuscated backdoors and feedback loops. DroidCollection includes over one million code samples across seven programming languages, three coding domains, and outputs from 43 different code models, including human-AI co-authored code and adversarially humanized machine code. Why it matters: This research is crucial for maintaining software security and integrity in the age of AI-assisted coding, providing a robust tool for detecting AI-generated code across diverse languages and domains.
MBZUAI researchers release LLM-DetectAIve, a tool for fine-grained detection of machine-generated text across four categories: human-written, machine-generated, machine-written then humanized, and human-written then machine-polished. The tool aims to address concerns about misuse of LLMs, especially in education and academia, by identifying attempts to obfuscate or polish content. LLM-DetectAIve is publicly accessible with code and a demonstration video provided.
This paper introduces a framework that combines machine learning for multi-class attack detection in IoT/IIoT networks with large language models (LLMs) for attack behavior analysis and mitigation suggestion. The framework uses role-play prompt engineering with RAG to guide LLMs like ChatGPT-o3 and DeepSeek-R1, and introduces new evaluation metrics for quantitative assessment. Experiments using Edge-IIoTset and CICIoT2023 datasets showed Random Forest as the best detection model and ChatGPT-o3 outperforming DeepSeek-R1 in attack analysis and mitigation.
This paper introduces DetectLLM-LRR and DetectLLM-NPR, two novel zero-shot methods for detecting machine-generated text using log rank information. Experiments across three datasets and seven language models demonstrate improvements of up to 3.9 AUROC points over state-of-the-art methods. The code and data for both methods are available on Github.
Researchers at MBZUAI and other institutions have published a study at ACL 2024 investigating how jailbreak attacks work on LLMs. The study used a dataset of 30,000 prompts and non-linear probing to interpret the effects of jailbreak attacks, finding that existing interpretations were inadequate. The researchers propose a new approach to improve LLM safety against such attacks by identifying the layers in neural networks where the behavior occurs. Why it matters: Understanding and mitigating jailbreak attacks is crucial for ensuring the responsible and secure deployment of LLMs, particularly in the Arabic-speaking world where these models are increasingly being used.
Researchers at MBZUAI have developed LLM-DetectAIve, a tool to classify the degree of machine involvement in text generation. The system categorizes text into four types: human-written, machine-generated, machine-written and machine-humanized, and human-written and machine-polished. A demo website allows users to test the tool's ability to detect machine involvement. Why it matters: This research addresses the growing need to identify and classify AI-generated content in academic and professional settings, particularly in light of increasing LLM misuse.