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ALPACA: Application Layer Protocol Confusion - Analyzing and Mitigating Cracks in TLS Authentication
(2021)
Vulnerabilities in private networks are difficult to detect for attackers outside of the network. While there are known methods for port scanning internal hosts that work by luring unwitting internal users to an external web page that hosts malicious JavaScript code, no such method for detailed and precise service identification is known. The reason is that the Same Origin Policy (SOP) prevents access to HTTP responses of other origins by default. We perform a structured analysis of loopholes in the SOP that can be used to identify web applications across network boundaries. For this, we analyze HTML5, CSS, and JavaScript features of standard-compliant web browsers that may leak sensitive information about cross-origin content. The results reveal several novel techniques, including leaking JavaScript function names or styles of cross-origin requests that are available in all common browsers. We implement and test these techniques in a tool called CORSICA. It can successfully identify 31 of 42 (74%) of web services running on different IoT devices as well as the version numbers of the four most widely used content management systems WordPress, Drupal, Joomla, and TYPO3. CORSICA can also determine the patch level on average down to three versions (WordPress), six versions (Drupal), two versions (Joomla), and four versions (TYPO3) with only ten requests on average. Furthermore, CORSICA is able to identify 48 WordPress plugins containing 65 vulnerabilities. Finally, we analyze mitigation strategies and show that the proposed but not yet implemented strategies Cross-Origin Resource Policy (CORP)} and Sec-Metadata would prevent our identification techniques.
Due to the increasing connectivity of modern vehicles, collected data is no longer only stored in the vehicle itself but also transmitted to car manufacturers and vehicle assistant apps. This development opens up new possibilities for digital forensics in criminal investigations involving modern vehicles. This paper deals with the digital forensic analysis of vehicle assistant apps of eight car manufacturers. We reconstruct the driver’s activities based on the data stored on the smartphones and in the manufacturer’s backend.
For this purpose, data of the Android and iOS apps of the car manufacturers Audi, BMW, Ford, Mercedes, Opel, Seat, Tesla, and Volkswagen were extracted from the smartphone and examined using digital forensic methods following forensics guidelines. Additionally, manufacturer data was retrieved using Subject Access Requests. Using the extensive data gathered, we reconstruct trips and refueling processes, determine parking positions and duration, and track the locking and unlocking of the vehicle.
Our findings show that the digital forensic investigation of smartphone applications is a useful addition to vehicle forensics and should therefore be taken into account in the strategic preparation of future digital forensic investigations.