204 citations · 509 across the 65 of their papers we have counts for
8 papers · 1 filter
Securing Computer-Use Agents Against Branch Steering Attacks
Giulio Zingrillo, Hanna Foerster, Ilia Shumailov +2
Modern Computer Use Agents (CUAs) directly interact with graphical user interfaces and execute third-party web tools, exposing them to indirect prompt injection across every render…
Quantamination: Dynamic Quantization Leaks Your Data Across the Batch
Hanna Foerster, Ilia Shumailov, Cheng Zhang +3
Dynamic quantization emerged as a practical approach to increase the utilization and efficiency of the machine learning serving flow. Unlike static quantization, which applies quan…
Reasoning Introduces New Poisoning Attacks Yet Makes Them More Complicated
Hanna Foerster, Ilia Shumailov, Yiren Zhao +4
Early research into data poisoning attacks against Large Language Models (LLMs) demonstrated the ease with which backdoors could be injected. More recent LLMs add step-by-step reas…
Locking Machine Learning Models into Hardware
Eleanor Clifford, Adhithya Saravanan, Harry Langford +5
Modern machine learning (ML) models are expensive IP and business competitiveness often depends on keeping this IP confidential. This in turn restricts how these models are deploye…
Architectural Neural Backdoors from First Principles
Harry Langford, Ilia Shumailov, Yiren Zhao +2
While previous research backdoored neural networks by changing their parameters, recent work uncovered a more insidious threat: backdoors embedded within the definition of the netw…
Flareon: Stealthy any2any Backdoor Injection via Poisoned Augmentation
Tianrui Qin, Xianghuan He, Xitong Gao +3
Open software supply chain attacks, once successful, can exact heavy costs in mission-critical applications. As open-source ecosystems for deep learning flourish and become increas…
Nudge Attacks on Point-Cloud DNNs
Yiren Zhao, Ilia Shumailov, Robert Mullins +1
The wide adaption of 3D point-cloud data in safety-critical applications such as autonomous driving makes adversarial samples a real threat. Existing adversarial attacks on point c…
To compress or not to compress: Understanding the Interactions between Adversarial Attacks and Neural Network Compression
Yiren Zhao, Ilia Shumailov, Robert Mullins +1
As deep neural networks (DNNs) become widely used, pruned and quantised models are becoming ubiquitous on edge devices; such compressed DNNs are popular for lowering computational…