arXiv subjects

Lieven Desmet

Publications and source records attributed to Lieven Desmet.

5 recordsLinked to original sources

arXiv2026

The Right Tool for the Job: On the Selection of Mitigations for GenAI Privacy Threats

Generative Artificial Intelligence (GenAI) has rapidly evolved from an experimental technology into a foundational component of modern software systems. However, as its adoption grows, protecting sensitive personal data becomes increasingly challenging. Specifically, GenAI systems not only amplify traditional privacy threats but also introduce new inference-based risks, such as constructing detailed user profiles from seemingly harmless inputs. In response, privacy threat modeling frameworks are beginning to capture GenAI-specific privacy threats with finer granularity. At the same time, a growing number of mitigation techniques have been proposed to address these threats. However, although knowledge of both threats and mitigations continues to mature, the problem- and solution-space have developed largely independently. This position paper argues that the primary challenge in GenAI privacy engineering is not the lack of knowledge about privacy threats or mitigation techniques, but the missing bridge between them. We decompose this gap into three sub-problems: (i) lack of fine-grained threat-to-mitigation mapping for GenAI systems, (ii) inapplicable solution-space assumptions in the GenAI context, and (iii) prioritization difficulty under GenAI constraints. We derive four recommendations for future mitigation-selection approaches, and outline a suggested approach that extends established threat-to-mitigation mapping methods to GenAI-specific threat characteristics. We propose a research agenda toward more systematic privacy mitigation selection for GenAI-based systems.

cs.CR↗

arXiv2026

A history of GDPR cookie banner compliance: the roles of publishers, regulators and CMPs

Since the introduction of the GDPR in 2018, cookie banners have become the primary mechanism for users to express preferences on online tracking and advertising. Consequently, their visual design and the options they present significantly influence user choice. Over time, the cookie banner landscape has evolved under the influence of key players, including publishers (website owners), regulators, and Consent Management Platforms (CMPs). This paper presents an in-depth analysis of the roles of these three key actors and an examination of their impact on cookie banners' design and implementation within the context of EU law. Our results, based on a historical evaluation of 11364 websites across 30 countries, indicate a positive evolution in the privacy landscape, with the compliance rate for websites featuring a "reject all" button increasing from 2.94% in 2018 to 30.66% in 2024. We analyze Data Protection Authority (DPA) activity and find a clear correlation between higher compliance rates and stronger regulatory action and guidance. Our experiments further show that compliance improvements are primarily driven by website owners, with CMPs showing little response to regulatory action or (indirect) influence on compliance rates. Our findings highlight the importance of more uniform collaboration and guidance among EU-level regulators to reduce interpretive divergence and simplify cookie banner compliance, as well as the need for regulatory oversight of CMPs, which in turn could significantly enhance privacy for many websites and users. Our work provides a foundation for academics, regulators, and industry to develop more effective strategies to motivate key players and promote greater user privacy.

cs.CY↗

arXiv2026

MATRA: Modeling the Attack Surface of Agentic AI Systems -- OpenClaw Case Study

LLMs are increasingly deployed as autonomous agents with access to tools, databases, and external services, yet practitioners (across different sectors) lack systematic methods to assess how known threat classes translate into concrete risks within a specific agentic deployment. We present MATRA, a pragmatic threat modeling framework for agentic AI systems that adapts established risk assessment methodology to systematically assess how known LLM threats translate into deployment-specific risks. MATRA begins with an asset-based impact assessment and utilizes attack trees to determine the likelihood of these impacts occurring within the system architecture. We demonstrate MATRA on a personal AI agent deployment using OpenClaw, quantifying how architectural controls such as network sandboxing and least-privilege access reduce risk by limiting the blast radius of successful injections.

cs.AI↗

arXiv2026

FlowPure: Continuous Normalizing Flows for Adversarial Purification

Despite significant advances in the area, adversarial robustness remains a critical challenge in systems employing machine learning models. The removal of adversarial perturbations at inference time, known as adversarial purification, has emerged as a promising defense strategy. To achieve this, state-of-the-art methods leverage diffusion models that inject Gaussian noise during a forward process to dilute adversarial perturbations, followed by a denoising step to restore clean samples before classification. In this work, we propose FlowPure, a novel purification method based on Continuous Normalizing Flows (CNFs) trained with Conditional Flow Matching (CFM) to learn mappings from adversarial examples to their clean counterparts. Unlike prior diffusion-based approaches that rely on fixed noise processes, FlowPure can leverage specific attack knowledge to improve robustness under known threats, while also supporting a more general stochastic variant trained on Gaussian perturbations for settings where such knowledge is unavailable. Experiments on CIFAR-10 and CIFAR-100 demonstrate that our method outperforms state-of-the-art purification defenses in preprocessor-blind and white-box scenarios, and can do so while fully preserving benign accuracy in the former. Moreover, our results show that not only is FlowPure a highly effective purifier but it also holds strong potential for adversarial detection, identifying preprocessor-blind PGD samples with near-perfect accuracy. Our code is publicly available at https://github.com/DistriNet/FlowPure.

cs.LG↗

arXiv2026

A LINDDUN-based Privacy Threat Modeling Framework for GenAI

As generative AI (GenAI) systems become increasingly prevalent across various technological stacks, the question of how such systems handle sensitive and personal data flows becomes increasingly important. Specifically, both the ability to harness and process large swaths of information as well as their stochastic nature raise key concerns related to both security and privacy. Unfortunately, while some of the traditional security threat modeling can effectively identify certain violations, privacy-related issues are often overlooked. To respond to these challenges, we introduce a novel domain-specific privacy threat modeling framework to support the privacy threat analysis of GenAI-based applications. This framework is constructed through a two-pronged approach: (1) a systematic review of the emerging literature on GenAI privacy threats, and (2) a case-driven application to a representative Chatbot system. These efforts yield a foundational GenAI privacy threat modeling framework built on LINDDUN. The new framework affects three out of the seven privacy threat types of LINDDUN and introduces 100 new GenAI examples to the knowledge base. Its effectiveness is validated on an AI Agent system, which demonstrates that a comprehensive privacy analysis can be supported by the new framework.

cs.CR↗