Structural Analysis: Shape Information via Points-To Computation

This paper introduces a new hybrid memory analysis, Structural Analysis, which combines an expressive shape analysis style abstract domain with efficient and simple points-to style transfer functions. Using data from empirical studies on the runtime heap structures and the programmatic idioms used in modern object-oriented languages we construct a heap analysis with the following characteristics: (1) it can express a rich set of structural, shape, and sharing properties which are not provided by a classic points-to analysis and that are useful for optimization and error detection applications (2) it uses efficient, weakly-updating, set-based transfer functions which enable the analysis to be more robust and scalable than a shape analysis and (3) it can be used as the basis for a scalable interprocedural analysis that produces precise results in practice. The analysis has been implemented for .Net bytecode and using this implementation we evaluate both the runtime cost and the precision of the results on a number of well known benchmarks and real world programs. Our experimental evaluations show that the domain defined in this paper is capable of precisely expressing the majority of the connectivity, shape, and sharing properties that occur in practice and, despite the use of weak updates, the static analysis is able to precisely approximate the ideal results. The analysis is capable of analyzing large real-world programs (over 30K bytecodes) in less than 65 seconds and using less than 130MB of memory. In summary this work presents a new type of memory analysis that advances the state of the art with respect to expressive power, precision, and scalability and represents a new area of study on the relationships between and combination of concepts from shape and points-to analyses

In-Vivo Bytecode Instrumentation for Improving Privacy on Android Smartphones in Uncertain Environments

In this paper we claim that an efficient and readily applicable means to improve privacy of Android applications is: 1) to perform runtime monitoring by instrumenting the application bytecode and 2) in-vivo, i.e. directly on the smartphone. We present a tool chain to do this and present experimental results showing that this tool chain can run on smartphones in a reasonable amount of time and with a realistic effort. Our findings also identify challenges to be addressed before running powerful runtime monitoring and instrumentations directly on smartphones. We implemented two use-cases leveraging the tool chain: BetterPermissions, a fine-grained user centric permission policy system and AdRemover an advertisement remover. Both prototypes improve the privacy of Android systems thanks to in-vivo bytecode instrumentation.Comment: ISBN: 978-2-87971-111-

Evaluating Design Tradeoffs in Numeric Static Analysis for Java

Numeric static analysis for Java has a broad range of potentially useful applications, including array bounds checking and resource usage estimation. However, designing a scalable numeric static analysis for real-world Java programs presents a multitude of design choices, each of which may interact with others. For example, an analysis could handle method calls via either a top-down or bottom-up interprocedural analysis. Moreover, this choice could interact with how we choose to represent aliasing in the heap and/or whether we use a relational numeric domain, e.g., convex polyhedra. In this paper, we present a family of abstract interpretation-based numeric static analyses for Java and systematically evaluate the impact of 162 analysis configurations on the DaCapo benchmark suite. Our experiment considered the precision and performance of the analyses for discharging array bounds checks. We found that top-down analysis is generally a better choice than bottom-up analysis, and that using access paths to describe heap objects is better than using summary objects corresponding to points-to analysis locations. Moreover, these two choices are the most significant, while choices about the numeric domain, representation of abstract objects, and context-sensitivity make much less difference to the precision/performance tradeoff

The Meaning of Memory Safety

We give a rigorous characterization of what it means for a programming language to be memory safe, capturing the intuition that memory safety supports local reasoning about state. We formalize this principle in two ways. First, we show how a small memory-safe language validates a noninterference property: a program can neither affect nor be affected by unreachable parts of the state. Second, we extend separation logic, a proof system for heap-manipulating programs, with a memory-safe variant of its frame rule. The new rule is stronger because it applies even when parts of the program are buggy or malicious, but also weaker because it demands a stricter form of separation between parts of the program state. We also consider a number of pragmatically motivated variations on memory safety and the reasoning principles they support. As an application of our characterization, we evaluate the security of a previously proposed dynamic monitor for memory safety of heap-allocated data.Comment: POST'18 final versio

Using HTML5 to Prevent Detection of Drive-by-Download Web Malware

The web is experiencing an explosive growth in the last years. New technologies are introduced at a very fast-pace with the aim of narrowing the gap between web-based applications and traditional desktop applications. The results are web applications that look and feel almost like desktop applications while retaining the advantages of being originated from the web. However, these advancements come at a price. The same technologies used to build responsive, pleasant and fully-featured web applications, can also be used to write web malware able to escape detection systems. In this article we present new obfuscation techniques, based on some of the features of the upcoming HTML5 standard, which can be used to deceive malware detection systems. The proposed techniques have been experimented on a reference set of obfuscated malware. Our results show that the malware rewritten using our obfuscation techniques go undetected while being analyzed by a large number of detection systems. The same detection systems were able to correctly identify the same malware in its original unobfuscated form. We also provide some hints about how the existing malware detection systems can be modified in order to cope with these new techniques.Comment: This is the pre-peer reviewed version of the article: \emph{Using HTML5 to Prevent Detection of Drive-by-Download Web Malware}, which has been published in final form at \url{http://dx.doi.org/10.1002/sec.1077}. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archivin

Modular Construction of Shape-Numeric Analyzers

The aim of static analysis is to infer invariants about programs that are precise enough to establish semantic properties, such as the absence of run-time errors. Broadly speaking, there are two major branches of static analysis for imperative programs. Pointer and shape analyses focus on inferring properties of pointers, dynamically-allocated memory, and recursive data structures, while numeric analyses seek to derive invariants on numeric values. Although simultaneous inference of shape-numeric invariants is often needed, this case is especially challenging and is not particularly well explored. Notably, simultaneous shape-numeric inference raises complex issues in the design of the static analyzer itself. In this paper, we study the construction of such shape-numeric, static analyzers. We set up an abstract interpretation framework that allows us to reason about simultaneous shape-numeric properties by combining shape and numeric abstractions into a modular, expressive abstract domain. Such a modular structure is highly desirable to make its formalization and implementation easier to do and get correct. To achieve this, we choose a concrete semantics that can be abstracted step-by-step, while preserving a high level of expressiveness. The structure of abstract operations (i.e., transfer, join, and comparison) follows the structure of this semantics. The advantage of this construction is to divide the analyzer in modules and functors that implement abstractions of distinct features.Comment: In Proceedings Festschrift for Dave Schmidt, arXiv:1309.455