Biblio

A recent research shows that 88 % of Android applications that use cryptographic APIs make at least one mistake. For this reason, several tools have been proposed to detect crypto API misuses, such as CryptoLint, CMA, and CogniCryptSAsT. However, these tools depend heavily on manually designed rules, which require much cryptographic knowledge and could be error-prone. In this paper, we propose an approach based on probabilistic models, namely, hidden Markov model and n-gram model, to analyzing crypto API usages in Android applications. The difficulty lies in that crypto APIs are sensitive to not only API orders, but also their arguments. To address this, we have created a dataset consisting of crypto API sequences with arguments, wherein symbolic execution is performed. Finally, we have also conducted some experiments on our models, which shows that ( i) our models are effective in capturing the usages, detecting and locating the misuses; (ii) our models perform better than the ones without symbolic execution, especially in misuse detection; and (iii) compared with CogniCryptSAsT, our models can detect several new misuses.

Cryptographic APIs are often vulnerable to attacks that compromise sensitive cryptographic keys. In the literature we find many proposals for preventing or mitigating such attacks but they typically require to modify the API or to configure it in a way that might break existing applications. This makes it hard to adopt such proposals, especially because security APIs are often used in highly sensitive settings, such as financial and critical infrastructures, where systems are rarely modified and legacy applications are very common. In this paper we take a different approach. We propose an effective method to monitor existing cryptographic systems in order to detect, and possibly prevent, the leakage of sensitive cryptographic keys. The method collects logs for various devices and cryptographic services and is able to detect, offline, any leakage of sensitive keys, under the assumption that a key fingerprint is provided for each sensitive key. We define key security formally and we prove that the method is sound, complete and efficient. We also show that without key fingerprinting completeness is lost, i.e., some attacks cannot be detected. We discuss possible practical implementations and we develop a proof-of-concept log analysis tool for PKCS\#11 that is able to detect, on a significant fragment of the API, all key-management attacks from the literature.

Cryptographic misuse affects a sizeable portion of Android applications. However, there is only an empirical study that has been made about this problem. In this paper, we perform a systematic analysis on the cryptographic misuse, build the cryptographic misuse vulnerability model and implement a prototype tool Crypto Misuse Analyser (CMA). The CMA can perform static analysis on Android apps and select the branches that invoke the cryptographic API. Then it runs the app following the target branch and records the cryptographic API calls. At last, the CMA identifies the cryptographic API misuse vulnerabilities from the records based on the pre-defined model. We also analyze dozens of Android apps with the help of CMA and find that more than a half of apps are affected by such vulnerabilities.