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Agatha Duzan

dblp:410/3089 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Human-AI interaction · 67% Human-robot interaction · 33%
Network and information security
1 paper
Security and privacy of machine learning · 87% Usable security · 13%

Topics — the 5 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Human-AI interaction › GUI agent
computer use agents
0.912025
OS-Harm: A Benchmark for Measuring Safety of Computer Use Agents · NeurIPS 2025
Human-AI interaction
LLM-based agents
0.912025
OS-Harm: A Benchmark for Measuring Safety of Computer Use Agents · NeurIPS 2025
Human-robot interaction
safety evaluation
0.912025
OS-Harm: A Benchmark for Measuring Safety of Computer Use Agents · NeurIPS 2025
Security and privacy of machine learning › large language model safety
LLM agent safety
0.912025
OS-Harm: A Benchmark for Measuring Safety of Computer Use Agents · NeurIPS 2025
Security and privacy of machine learning
prompt injection
0.912025
OS-Harm: A Benchmark for Measuring Safety of Computer Use Agents · NeurIPS 2025

Methods — techniques the papers use, named apart from their topics

benchmark construction · 1.7automated judge · 1.7
YearPublicationVenuePosition
2025 OS-Harm: A Benchmark for Measuring Safety of Computer Use Agents
abstract
Computer use agents are LLM-based agents that can directly interact with a graphical user interface, by processing screenshots or accessibility trees. While these systems are gaining popularity, their safety has been largely overlooked, despite the fact that evaluating and understanding their potential for harmful behavior is essential for widespread adoption. To address this gap, we introduce OS-Harm, a new benchmark for measuring safety of computer use agents. OS-Harm is built on top of the OSWorld environment (Xie et al., 2024) and aims to test models across three categories of harm: deliberate user misuse, prompt injection attacks, and model misbehavior. To cover these cases, we create 150 tasks that span several types of safety violations (harassment, copyright infringement, disinformation, data exfiltration, etc.) and require the agent to interact with a variety of OS applications (email client, code editor, browser, etc.). Moreover, we propose an automated judge to evaluate both accuracy and safety of agents that achieves high agreement with human annotations (0.76 and 0.79 F1 score). We evaluate computer use agents based on a range of frontier models—such as o4-mini, Claude 3.7 Sonnet, Gemini 2.5 Pro—and provide insights into their safety. In particular, all models tend to directly comply with many deliberate misuse queries, are relatively vulnerable to static prompt injections, and occasionally perform unsafe actions. The OS-Harm benchmark is available at https://github.com/tml-epfl/os-harm.
Thomas Kuntz, Agatha Duzan, Francesco Croce, J. Zico Kolter, Nicolas Flammarion, Maksym Andriushchenko
NeurIPS2