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20242026
most citedAI Agents Enable Adaptive Computer Worms

2 citations · 2 across the 7 of their papers we have counts for

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cs.CR2026

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…

cs.CR2026★ 2 cited

AI Agents Enable Adaptive Computer Worms

Jonas Guan, Tom Blanchard, Hanna Foerster +3

A computer worm is malware that spreads on a network by replicating itself from one machine to another. Traditional worms, like WannaCry, exploited predetermined vulnerabilities, a…

cs.CR2026

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…

cs.CR2026

Thought-Transfer: Indirect Targeted Poisoning Attacks on Chain-of-Thought Reasoning Models

Harsh Chaudhari, Ethan Rathbun, Hanna Foerster +5

Chain-of-Thought (CoT) reasoning has emerged as a powerful technique for enhancing large language models' capabilities by generating intermediate reasoning steps for complex tasks.…

cs.CR2025

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…