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Iliana Fayolle

Publications and source records attributed to Iliana Fayolle.

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Practice Makes (Im)Perfect: A Look Back at Benchmarking Practices for Microarchitectural Side-Channel Attacks

Microarchitectural side-channel research has grown at an exceptional pace in recent years, increasing the need for rigorous and meaningful benchmarking. Early attack papers typically relied on indirect proxies, such as covert-channel bandwidth or key-recovery on naive AES and RSA implementations, setting de facto standards that many subsequent works continued to replicate, sometimes by directly comparing against raw numbers from prior work. While these practices offer convenient points of comparison, current benchmarks may not be the most relevant to assess specific properties of new primitives. Even more problematic, microarchitectural attacks are notoriously sensitive to experimental conditions: minimal changes in the target system can significantly alter outcomes and performance. As a result, inadequate evaluation practices undermine reproducibility and cast doubt on the relevance of comparisons, even in top-tier venues where such issues should be identified. This paper tackles the core problem of proper benchmarking for microarchitectural side-channel attacks and examines its broader impact on research quality in the field. We survey 83 attack papers published in top-ranked security and architecture conferences from 2014 to 2024. From this corpus, we identify and define 19 recurrent benchmarking flaws that affect evaluation completeness, relevance, soundness, and reproducibility. These flaws include unfair or absent comparisons, missing code or materials, and the failure to evaluate the key attack properties. On average, each paper exhibits 5.5 such flaws, highlighting how widespread the issue is, even in highly selective venues. Based on our findings, we identify and suggest key properties that are relevant to properly evaluate new attacks. We also highlight trends over time and different practices between security and architecture conferences.

cs.CR

On the Internet, Nobody Knows You're an LLM Bot: Unmasking Web Agents with Multi-Layer Fingerprinting

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.

cs.CR