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Web browsers use HTTP caches to reduce the amount of data to be transferred over the network and allow Web pages to load faster. Content such as scripts, images, and style sheets, which are static most of the time or shared across multiple websites, are stored and loaded locally when recurring requests ask for cached resources. This behaviour can be exploited if the cache is based on a naive implementation. This paper summarises possible attacks on the browser cache and shows through extensive experiments that even modern web browsers still do not provide enough safeguards to protect their users. Moreover, the available built-in as well as addable cache controls offer rather limited functionality in terms of protection and ease of use. Due to the volatile and inhomogeneous APIs for controlling the cache in modern browsers, the development of enhanced user-centric cache controls remains -until further notice- in the hands of browser manufacturers.
Online services such as social networks, online shops, and search engines deliver different content to users depending on their location, browsing history, or client device. Since these services have a major influence on opinion forming, understanding their behavior from a social science perspective is of greatest importance. In addition, technical aspects of services such as security or privacy are becoming more and more relevant for users, providers, and researchers. Due to the lack of essential data sets, automatic black box testing of online services is currently the only way for researchers to investigate these services in a methodical and reproducible manner. However, automatic black box testing of online services is difficult since many of them try to detect and block automated requests to prevent bots from accessing them.
In this paper, we introduce a testing tool that allows researchers to create and automatically run experiments for exploratory studies of online services. The testing tool performs programmed user interactions in such a manner that it can hardly be distinguished from a human user. To evaluate our tool, we conducted - among other things - a large-scale research study on Risk-based Authentication (RBA), which required human-like behavior from the client. We were able to circumvent the bot detection of the investigated online services with the experiments. As this demonstrates the potential of the presented testing tool, it remains to the responsibility of its users to balance the conflicting interests between researchers and service providers as well as to check whether their research programs remain undetected.