We propose a new approach to infer state machine models from protocol implementations. Our new tool, StateInspector, learns protocol states by using novel program analyses to combine observations of run-time memory and I/O. It requires no access to source code and only lightweight execution monitoring of the implementation under test. We demonstrate and evaluate StateInspector's effectiveness on numerous TLS and WPA/2 implementations. In the process, we show StateInspector enables deeper state discovery, increased learning efficiency, and more insight compared to existing approaches. Our method led us to discover several concerning deviations from the standards and vulnerabilities in IWD and WolfSSL, both of which were assigned CVEs.

Certificate verification is a crucial stage in the establishment of a TLS connection. A common security flaw in TLS implementations is the lack of certificate hostname verification but, in general, this is easy to detect. In security-sensitive applications, the usage of certificate pinning is on the rise. This paper shows that certificate pinning can (and often does) hide the lack of proper hostname verification, enabling MITM attacks. Dynamic (black-box) detection of this vulnerability would typically require the tester to own a high security certificate from the same issuer (and often same intermediate CA) as the one used by the app. We present Spinner, a new tool for black-box testing for this vulnerability at scale that does not require purchasing any certificates. By redirecting traffic to websites which use the relevant certificates and then analysing the (encrypted) network traffic we are able to determine whether the hostname check is correctly done, even in the presence of certificate pinning. We use Spinner to analyse 400 security-sensitive Android and iPhone apps. We found that 9 apps had this flaw, including two of the largest banks in the world: Bank of America and HSBC. We also found that TunnelBear, one of the most popular VPN apps was also vulnerable. These apps have a joint user base of tens of millions of users.