Biblio

Most modern cloud and web services are programmatically accessed through REST APIs. This paper discusses how an attacker might compromise a service by exploiting vulnerabilities in its REST API. We introduce four security rules that capture desirable properties of REST APIs and services. We then show how a stateful REST API fuzzer can be extended with active property checkers that automatically test and detect violations of these rules. We discuss how to implement such checkers in a modular and efficient way. Using these checkers, we found new bugs in several deployed production Azure and Office365 cloud services, and we discuss their security implications. All these bugs have been fixed.

While the standard language XACML is very expressive for specifying fine-grained access control policies, defects can get into XACML policies for various reasons, such as misunderstanding of access control requirements, omissions, and coding errors. These defects may result in unauthorized accesses, escalation of privileges, and denial of service. Therefore, quality assurance of XACML policies for real-world information systems has become an important issue. To address this issue, this paper presents a family of coverage criteria for XACML policies, such as rule coverage, rule pair coverage, decision coverage, and Modified Condition/Decision Coverage (MC/DC). To demonstrate the assurance levels of these coverage criteria, we have developed methods for automatically generating tests, i.e., access requests, to satisfy the coverage criteria using a constraint solver. We have evaluated these methods through mutation analysis of various policies with different levels of complexity. The experiment results have shown that the rule coverage is far from adequate for revealing the majority of defects in XACML policies, and that both MC/DC and decision coverage tests have outperformed the existing methods for testing XACML policies. In particular, MC/DC tests achieve a very high level of quality assurance of XACML policies.

Interconnect opens are known to be one of the predominant defects in nanoscale technologies. Automatic test pattern generation for open faults is challenging, because of their rather unstable behavior and the numerous electrical parameters which need to be considered. Thus, most approaches try to avoid accurate modeling of all constraints like the influence of the aggressors on the open net and use simplified fault models in order to detect as many faults as possible or make assumptions which decrease both complexity and accuracy. Yet, this leads to the problem that not only generated tests may be invalidated but also the localization of a specific fault may fail - in case such a model is used as basis for diagnosis. Furthermore, most of the models do not consider the problem of oscillating behavior, caused by feedback introduced by coupling capacitances, which occurs in almost all designs. In [1], the Robust Enhanced Aggressor Victim Model (REAV) and in [2] an extension to address the problem of oscillating behavior were introduced. The resulting model does not only consider the influence of all aggressors accurately but also guarantees robustness against oscillating behavior as well as process variations affecting the thresholds of gates driven by an open interconnect. In this work we present the first diagnostic classification algorithm for this model. This algorithm considers all constraints enforced by the REAV model accurately - and hence handles unknown values as well as oscillating behavior. In addition, it allows to distinguish faults at the same interconnect and thus reducing the area that has to be considered for physical failure analysis. Experimental results show the high efficiency of the new method handling circuits with up to 500,000 non-equivalent faults and considerably increasing the diagnostic resolution.

Given the ever increasing number of research tools to automatically generate inputs to test Android applications (or simply apps), researchers recently asked the question "Are we there yet?" (in terms of the practicality of the tools). By conducting an empirical study of the various tools, the researchers found that Monkey (the most widely used tool of this category in industrial settings) outperformed all of the research tools in the study. In this paper, we present two signi cant extensions of that study. First, we conduct the rst industrial case study of applying Monkey against WeChat, a popular  messenger app with over 762 million monthly active users, and report the empirical ndings on Monkey's limitations in an industrial setting. Second, we develop a new approach to address major limitations of Monkey and accomplish substantial code-coverage improvements over Monkey. We conclude the paper with empirical insights for future enhancements to both Monkey and our approach.