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Calzavara, Stefano, Focardi, Riccardo, Squarcina, Marco, Tempesta, Mauro.  2018.  Surviving the Web: A Journey into Web Session Security. Companion Proceedings of the The Web Conference 2018. :451-455.
We survey the most common attacks against web sessions, i.e., attacks which target honest web browser users establishing an authenticated session with a trusted web application. We then review existing security solutions which prevent or mitigate the different attacks, by evaluating them along four different axes: protection, usability, compatibility and ease of deployment. Based on this survey, we identify five guidelines that, to different extents, have been taken into account by the designers of the different proposals we reviewed. We believe that these guidelines can be helpful for the development of innovative solutions approaching web security in a more systematic and comprehensive way.
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Squarcina, Marco, Calzavara, Stefano, Maffei, Matteo.  2021.  The Remote on the Local: Exacerbating Web Attacks Via Service Workers Caches. 2021 IEEE Security and Privacy Workshops (SPW). :432—443.
Service workers boost the user experience of modern web applications by taking advantage of the Cache API to improve responsiveness and support offline usage. In this paper, we present the first security analysis of the threats posed by this programming practice, identifying an attack with major security implications. In particular, we show how a traditional XSS attack can abuse the Cache API to escalate into a personin-the-middle attack against cached content, thus compromising its confidentiality and integrity. Remarkably, this attack enables new threats which are beyond the scope of traditional XSS. After defining the attack, we study its prevalence in the wild, finding that the large majority of the sites which register service workers using the Cache API are vulnerable as long as a single webpage in the same origin of the service worker is affected by an XSS. Finally, we propose a browser-side countermeasure against this attack, and we analyze its effectiveness and practicality in terms of security benefits and backward compatibility with existing web applications.
Squarcina, Marco, Calzavara, Stefano, Maffei, Matteo.  2021.  The Remote on the Local: Exacerbating Web Attacks Via Service Workers Caches. 2021 IEEE Security and Privacy Workshops (SPW). :432—443.
Service workers boost the user experience of modern web applications by taking advantage of the Cache API to improve responsiveness and support offline usage. In this paper, we present the first security analysis of the threats posed by this programming practice, identifying an attack with major security implications. In particular, we show how a traditional XSS attack can abuse the Cache API to escalate into a personin-the-middle attack against cached content, thus compromising its confidentiality and integrity. Remarkably, this attack enables new threats which are beyond the scope of traditional XSS. After defining the attack, we study its prevalence in the wild, finding that the large majority of the sites which register service workers using the Cache API are vulnerable as long as a single webpage in the same origin of the service worker is affected by an XSS. Finally, we propose a browser-side countermeasure against this attack, and we analyze its effectiveness and practicality in terms of security benefits and backward compatibility with existing web applications.
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Calzavara, Stefano, Conti, Mauro, Focardi, Riccardo, Rabitti, Alvise, Tolomei, Gabriele.  2019.  Mitch: A Machine Learning Approach to the Black-Box Detection of CSRF Vulnerabilities. 2019 IEEE European Symposium on Security and Privacy (EuroS P). :528–543.

Cross-Site Request Forgery (CSRF) is one of the oldest and simplest attacks on the Web, yet it is still effective on many websites and it can lead to severe consequences, such as economic losses and account takeovers. Unfortunately, tools and techniques proposed so far to identify CSRF vulnerabilities either need manual reviewing by human experts or assume the availability of the source code of the web application. In this paper we present Mitch, the first machine learning solution for the black-box detection of CSRF vulnerabilities. At the core of Mitch there is an automated detector of sensitive HTTP requests, i.e., requests which require protection against CSRF for security reasons. We trained the detector using supervised learning techniques on a dataset of 5,828 HTTP requests collected on popular websites, which we make available to other security researchers. Our solution outperforms existing detection heuristics proposed in the literature, allowing us to identify 35 new CSRF vulnerabilities on 20 major websites and 3 previously undetected CSRF vulnerabilities on production software already analyzed using a state-of-the-art tool.

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Calzavara, Stefano, Rabitti, Alvise, Bugliesi, Michele.  2016.  Content Security Problems?: Evaluating the Effectiveness of Content Security Policy in the Wild Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security. :1365–1375.

Content Security Policy (CSP) is an emerging W3C standard introduced to mitigate the impact of content injection vulnerabilities on websites. We perform a systematic, large-scale analysis of four key aspects that impact on the effectiveness of CSP: browser support, website adoption, correct configuration and constant maintenance. While browser support is largely satisfactory, with the exception of few notable issues, our analysis unveils several shortcomings relative to the other three aspects. CSP appears to have a rather limited deployment as yet and, more crucially, existing policies exhibit a number of weaknesses and misconfiguration errors. Moreover, content security policies are not regularly updated to ban insecure practices and remove unintended security violations. We argue that many of these problems can be fixed by better exploiting the monitoring facilities of CSP, while other issues deserve additional research, being more rooted into the CSP design.