Abstract Code Injection: A Semantic Approach Based on Abstract Non-Interference

Code injection attacks have been the most critical security risks for almost a decade. These attacks are due to an interference between an untrusted input (potentially controlled by an attacker) and the execution of a string-to-code statement, interpreting as code its parameter. In this paper, we provide a semantic-based model for code injection parametric on what the programmer considers safe behaviors. In particular, we provide a general (abstract) non-interference-based framework for abstract code injection policies, i.e., policies characterizing safety against code injection w.r.t. a given specification of safe behaviors. We expect the new semantic perspective on code injection to provide a deeper knowledge on the nature itself of this security threat. Moreover, we devise a mechanism for enforcing (abstract) code injection policies, soundly detecting attacks, i.e., avoiding false negatives

Verifying Bounded Subset-Closed Hyperproperties

Hyperproperties are quickly becoming very popular in the context of systems security, due to their expressive power. They differ from classic trace properties since they are represented by sets of sets of executions instead of sets of executions. This allows us, for instance, to capture information flow security specifications, which cannot be expressed as trace properties, namely as predicates over single executions. In this work, we reason about how it is possible to move standard abstract interpretation-based static analysis methods, designed for trace properties, towards the verification of hyperproperties. In particular, we focus on the verification of bounded subset-closed hyperproperties which are easier to verify than generic hyperproperties. It turns out that a lot of interesting specifications (e.g., Non-Interference) lie in this category

Analyzing Dynamic Code: A Sound Abstract Interpreter for Evil Eval

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

Analyzing Dynamic Code: A Sound Abstract Interpreter for evil eval

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 \u201ceval 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 presence of dynamically generated program statements) with an acceptable degree of precision, should be clear. In order 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