Adel Bibi is a senior researcher in machine learning and computer vision at the Department of Engineering Science of the University of Oxford, a Research Fellow (JRF) at Kellogg College, and a member of the ELLIS Society. Bibi is an R&D Distinguished Advisor with Softserve. Previously, Bibi was a senior research associate and a postdoctoral researcher with Philip H.S. Torr since October 2020. He received his MSc and PhD degrees from King Abdullah University of Science & Technology (KAUST) in 2016 and 2020, respectively, advised by Bernard Ghanem. Bibi was awarded an Amazon Research Award in 2022 in the Machine Learning Algorithms and Theory track, the Google Gemma 2 Academic Award in 2024, and the Systemic AI Safety grant of ~$250,000 by the UK AI Security Institute in 2025. Bibi received four best paper awards; a NeurIPS23 workshop, an ICML23 workshop, a 2022 CVPR workshop, and one at the Optimization and Big Data Conference in 2018. His contributions include over 30 papers published in top machine learning and computer vision conferences. He also received four outstanding reviewer awards (CVPR18, CVPR19, ICCV19, ICLR22) and a Notable Area Chair Award in NeurIPS23.
Currently, Bibi is leading a group in Oxford focusing on the intersection between AI safety of large foundational models in both vision and language (covering topics such as robustness, certification, alignment, adversarial elicitation, etc.) and the efficient continual update of these models.
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[Note!] I am always looking for strong self-motivated PhD students. If you are interested in AI Safety, Trustworthy, and Security of AI models and Agentic AI, reach out!
[Consulting Expertise] I have consulted in the past on projects spanning core machine learning and data science, computer vision, certification and AI safety, optimization formulations for matching and resource allocation problems, among other areas.
PhD in Electrical Engineering (4.0/4.0); Machine Learning and Optimization Track, 2020
King Abdullah University of Science and Technology (KAUST)
MSc in Electrical Engineering (4.0/4.0); Computer Vision Track, 2016
King Abdullah University of Science and Technology (KAUST)
BSc in Electrical Engineering (3.99/4.0), 2014
Kuwait University
~~ End of 2023 ~~
~~ End of 2022 ~~
~~ End of 2021 ~~
~~ End of 2020 ~~
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~~ End of 2015 ~~
Recent advances in operating system (OS) agents have enabled vision-language models (VLMs) to directly control a user’s computer. Unlike conventional VLMs that passively output text, OS agents autonomously perform computer-based tasks in response to a single user prompt. OS agents do so by capturing, parsing, and analysing screenshots and executing low-level actions via application programming interfaces (APIs), such as mouse clicks and keyboard inputs. This direct interaction with the OS significantly raises the stakes, as failures or manipulations can have immediate and tangible consequences. In this work, we uncover a novel attack vector against these OS agents: Malicious Image Patches (MIPs), adversarially perturbed screen regions that, when captured by an OS agent, induce it to perform harmful actions by exploiting specific APIs. For instance, a MIP can be embedded in a desktop wallpaper or shared on social media to cause an OS agent to exfiltrate sensitive user data. We show that MIPs generalise across user prompts and screen configurations, and that they can hijack multiple OS agents even during the execution of benign instructions. These findings expose critical security vulnerabilities in OS agents that have to be carefully addressed before their widespread deployment.
Evaluating large language models (LLMs) is crucial for both assessing their capabilities and identifying safety or robustness issues prior to deployment. Reliably measuring abstract and complex phenomena such as ‘safety’ and ‘robustness’ requires strong construct validity, that is, having measures that represent what matters to the phenomenon. With a team of 29 expert reviewers, we conduct a systematic review of 445 LLM benchmarks from leading conferences in natural language processing and machine learning. Across the reviewed articles, we find patterns related to the measured phenomena, tasks, and scoring metrics which undermine the validity of the resulting claims. To address these shortcomings, we provide eight key recommendations and detailed actionable guidance to researchers and practitioners in developing LLM benchmarks.
Watermarking, the practice of embedding imperceptible information into media such as images, videos, audio, and text, is essential for intellectual property protection, content provenance and attribution. The growing complexity of digital ecosystems necessitates watermarks for different uses to be embedded in the same media. However, to detect and decode all watermarks, they need to coexist well with one another. We perform the first study of coexistence of deep image watermarking methods and, contrary to intuition, we find that various open-source watermarks can coexist with only minor impacts on image quality and decoding robustness. The coexistence of watermarks also opens the avenue for ensembling watermarking methods. We show how ensembling can increase the overall message capacity and enable new trade-offs between capacity, accuracy, robustness and image quality, without needing to retrain the base models.