In recent years, dynamic languages, such as JavaScript or Python, have been increasingly used in a wide range of fields and applications. Their tricky and misunderstood behaviors pose a great challenge for static analysis of these languages. A key aspect of any dynamic language program is the multiple usage of strings, since they can be implicitly converted to another type value, transformed by string-to-code primitives or used to access an object-property. Unfortunately, string analyses for dynamic languages still lack precision and do not take into account some important string features. In this scenario, more precise string analyses become a necessity. The goal of this paper is to place a first step for precisely handling dynamic language string features. In particular, we propose a new abstract domain approximating strings as finite state automata and an abstract interpretation-based static analysis for the most common string manipulating operations provided by the ECMAScript specification. The proposed analysis comes with a prototype static analyzer implementation for an imperative string manipulating language, allowing us to show and evaluate the improved precision of the proposed analysis.

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Dynamic languages, such as JavaScript, employ string-to-code primitives to turn dynamically generated text into executable code at run-time. These features make standard static analysis extremely hard if not impossible, because its essential data structures, i.e., the control-flow graph and the system of recursive equations associated with the program to analyze, are themselves dynamically mutating objects. Nevertheless, assembling code at run-time by manipulating strings, such as by eval in JavaScript, has been always strongly discouraged, since it is often recognized that “ eval is evil ,” leading static analyzers to not consider such statements or ignoring their effects. Unfortunately, the lack of formal approaches to analyze string-to-code statements pose a perfect habitat for malicious code, that is surely evil and do not respect good practice rules, allowing them to hide malicious intents as strings to be converted to code and making static analyses blind to the real malicious aim of the code. Hence, the need to handle string-to-code statements approximating what they can execute, and therefore allowing the analysis to continue (even in the presence of dynamically generated program statements) with an acceptable degree of precision, should be clear. To reach this goal, we propose a static analysis allowing us to collect string values and to soundly over-approximate and analyze the code potentially executed by a string-to-code statement.

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