Pierre Laperdrix

Iliana Fayolle, Sihem Bouhenniche, Samuel Pélissier et al.

Since 2023, a new class of bots has emerged: Web Agents. They can automate complex tasks on the Web, going beyond traditional browser automation tools such as Selenium, Puppeteer, or Playwright. Leveraging large language models (LLMs), these agents are capable of solving anti-bot mechanisms, mimicking human behavior, and, in some cases, operating directly from the local machine of the user configuring them. As a result, it is becoming increasingly difficult for website administrators to detect and block these LLM-based bots. Modern Web Agents commonly integrate stealth and anti-detection techniques, while numerous proprietary and open-source anti-bot mechanisms have emerged recently, specifically to block them. However, despite their growing prevalence, there is little evaluation of the effectiveness of state-of-the-art anti-bot mechanisms against these LLM-based bots and their stealth capabilities. Likewise, no prior work has comprehensively studied how to characterize and distinguish Web Agents deployed either in the cloud or locally. This paper addresses these open questions by deploying multiple honeysites protected by one or more anti-bot mechanisms (e.g., robots.txt, CAPTCHAs, proof-of-work, and Cloudflare's free proprietary solutions). We integrated network-, HTTP-, and browser-level fingerprinting techniques, and prompted six LLM-based Web Agents to visit the deployed honeysites. Our analysis reveals three main findings: (i) some Web Agents were able to bypass all evaluated anti-bot mechanisms; (ii) all evaluated Web Agents can be distinguished both from humans and from one another using multi-layer fingerprinting techniques across network, HTTP and browser layers; (iii) stealth and anti-detection mechanisms often increase detectability rather than decrease it.

Tomer Laor, Naif Mehanna, Antonin Durey et al.

Browser fingerprinting aims to identify users or their devices, through scripts that execute in the users' browser and collect information on software or hardware characteristics. It is used to track users or as an additional means of identification to improve security. In this paper, we report on a new technique that can significantly extend the tracking time of fingerprint-based tracking methods. Our technique, which we call DrawnApart, is a new GPU fingerprinting technique that identifies a device based on the unique properties of its GPU stack. Specifically, we show that variations in speed among the multiple execution units that comprise a GPU can serve as a reliable and robust device signature, which can be collected using unprivileged JavaScript. We investigate the accuracy of DrawnApart under two scenarios. In the first scenario, our controlled experiments confirm that the technique is effective in distinguishing devices with similar hardware and software configurations, even when they are considered identical by current state-of-the-art fingerprinting algorithms. In the second scenario, we integrate a one-shot learning version of our technique into a state-of-the-art browser fingerprint tracking algorithm. We verify our technique through a large-scale experiment involving data collected from over 2,500 crowd-sourced devices over a period of several months and show it provides a boost of up to 67% to the median tracking duration, compared to the state-of-the-art method. DrawnApart makes two contributions to the state of the art in browser fingerprinting. On the conceptual front, it is the first work that explores the manufacturing differences between identical GPUs and the first to exploit these differences in a privacy context. On the practical front, it demonstrates a robust technique for distinguishing between machines with identical hardware and software configurations.

Javier Cabrera-Arteaga, Pierre Laperdrix, Martin Monperrus et al.

Edge-cloud computing offloads parts of the computations that traditionally occurs in the cloud to edge nodes,e.g., CDN servers, in order to get closer to the users and reduce latency. To improve performance even further, WebAssembly is increasingly used in this context. Edge-cloud computing providers, such as Fastly or Cloudflare, let their clients deploy stateless services in the form of WebAssembly binaries, which are then translated to machine code and sandboxed for a safe execution at the edge. In this context, we propose a technique that (i) automatically diversifies WebAssembly binaries that are deployed to the edge and (ii) randomizes execution paths at runtime, turning the execution of the services into a moving target. Given a service tobe deployed at the edge, we automatically synthesize functionally equivalent variants for the functions that implement the service.All the variants are then wrapped into a single multivariant WebAssembly binary. When the service endpoint is executed,every time a function is invoked, one of its variants is randomly selected. We implement this technique in the MEWE tool and we validate it with 7 services for cryptography and QR encoding. MEWE generates multivariant binaries that embed hundreds of function variants. We execute the multivariant binaries on the worldwide edge platform provided by Fastly. We show that,at runtime, the multivariant exhibit a remarkable diversity ofexecution traces, across the whole edge platform.

Antonin Durey, Pierre Laperdrix, Walter Rudametkin et al.

Browser fingerprinting is a stateless identification technique based on browser properties. Together, they form an identifier that can be collected without users' notice and has been studied to be unique and stable. As this technique relies on browser properties that serve legitimate purposes, the detection of this technique is challenging. While several studies propose classification techniques, none of these are publicly available, making them difficult to reproduce. This paper proposes a new browser fingerprinting detection technique. Based on an incremental process, it relies on both automatic and manual decisions to be both reliable and fast. The automatic step matches API calls similarities between scripts while the manual step is required to classify a script with different calls. We publicly share our algorithm and implementation to improve the general knowledge on the subject.

Pierre Laperdrix, Nataliia Bielova, Benoit Baudry et al.

With this paper, we survey the research performed in the domain of browser fingerprinting, while providing an accessible entry point to newcomers in the field. We explain how this technique works and where it stems from. We analyze the related work in detail to understand the composition of modern fingerprints and see how this technique is currently used online. We systematize existing defense solutions into different categories and detail the current challenges yet to overcome.