Security Analysis of Role-based Access Control through Program Verification

We propose a novel scheme for proving administrative role-based access control (ARBAC) policies correct with respect to security properties using the powerful abstraction based tools available for program verification. Our scheme uses a combination of abstraction and reduction to program verification to perform security analysis. We convert ARBAC policies to imperative programs that simulate the policy abstractly, and then utilize further abstract-interpretation techniques from program analysis to analyze the programs in order to prove the policies secure. We argue that the aggressive set-abstractions and numerical-abstractions we use are natural and appropriate in the access control setting. We implement our scheme using a tool called VAC that translates ARBAC policies to imperative programs followed by an interval-based static analysis of the program, and show that we can effectively prove access control policies correct. The salient feature of our approach are the abstraction schemes we develop and the reduction of role-based access control security (which has nothing to do with programs) to program verification problems

Information flow analysis for a dynamically typed language with staged metaprogramming

Web applications written in JavaScript are regularly used for dealing with sensitive or personal data. Consequently, reasoning about their security properties has become an important problem, which is made very difficult by the highly dynamic nature of the language, particularly its support for runtime code generation via eval. In order to deal with this, we propose to investigate security analyses for languages with more principled forms of dynamic code generation. To this end, we present a static information flow analysis for a dynamically typed functional language with prototype-based inheritance and staged metaprogramming. We prove its soundness, implement it and test it on various examples designed to show its relevance to proving security properties, such as noninterference, in JavaScript. To demonstrate the applicability of the analysis, we also present a general method for transforming a program using eval into one using staged metaprogramming. To our knowledge, this is the first fully static information flow analysis for a language with staged metaprogramming, and the first formal soundness proof of a CFA-based information flow analysis for a functional programming language

Preventing SQL Injection through Automatic Query Sanitization with ASSIST

Web applications are becoming an essential part of our everyday lives. Many of our activities are dependent on the functionality and security of these applications. As the scale of these applications grows, injection vulnerabilities such as SQL injection are major security challenges for developers today. This paper presents the technique of automatic query sanitization to automatically remove SQL injection vulnerabilities in code. In our technique, a combination of static analysis and program transformation are used to automatically instrument web applications with sanitization code. We have implemented this technique in a tool named ASSIST (Automatic and Static SQL Injection Sanitization Tool) for protecting Java-based web applications. Our experimental evaluation showed that our technique is effective against SQL injection vulnerabilities and has a low overhead.Comment: In Proceedings TAV-WEB 2010, arXiv:1009.330

LJGS: Gradual Security Types for Object-Oriented Languages

LJGS is a lightweight Java core calculus with a gradual security type system. The calculus guarantees secure information flow for sequential, class-based, typed object-oriented programming with mutable objects and virtual method calls. An LJGS program is composed of fragments that are checked either statically or dynamically. Statically checked fragments adhere to a security type system so that they incur no run-time penalty whereas dynamically checked fragments rely on run-time security labels. The programmer marks the boundaries between static and dynamic checking with casts so that it is always clear whether a program fragment requires run-time checks. LJGS requires security annotations on fields and methods. A field annotation either specifies a fixed static security level or it prescribes dynamic checking. A method annotation specifies a constrained polymorphic security signature. The types of local variables in method bodies are analyzed flow-sensitively and require no annotation. The dynamic checking of fields relies on a static points-to analysis to approximate implicit flows. We prove type soundness and non-interference for LJGS

An investigation into the unsoundness of static program analysis : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Computer Science at Massey University, Palmerston North, New Zealand

Static program analysis is widely used in many software applications such as in security analysis, compiler optimisation, program verification and code refactoring. In contrast to dynamic analysis, static analysis can perform a full program analysis without the need of running the program under analysis. While it provides full program coverage, one of the main issues with static analysis is imprecision -- i.e., the potential of reporting false positives due to overestimating actual program behaviours. For many years, research in static program analysis has focused on reducing such imprecision while improving scalability. However, static program analysis may also miss some critical parts of the program, resulting in program behaviours not being reported. A typical example of this is the case of dynamic language features, where certain behaviours are hard to model due to their dynamic nature. The term ``unsoundness'' has been used to describe those missed program behaviours. Compared to static analysis, dynamic analysis has the advantage of obtaining precise results, as it only captures what has been executed during run-time. However, dynamic analysis is also limited to the defined program executions. This thesis investigates the unsoundness issue in static program analysis. We first investigate causes of unsoundness in terms of Java dynamic language features and identify potential usage patterns of such features. We then report the results of a number of empirical experiments we conducted in order to identify and categorise the sources of unsoundness in state-of-the-art static analysis frameworks. Finally, we quantify and measure the level of unsoundness in static analysis in the presence of dynamic language features. The models developed in this thesis can be used by static analysis frameworks and tools to boost the soundness in those frameworks and tools

Towards Vulnerability Discovery Using Staged Program Analysis

Eliminating vulnerabilities from low-level code is vital for securing software. Static analysis is a promising approach for discovering vulnerabilities since it can provide developers early feedback on the code they write. But, it presents multiple challenges not the least of which is understanding what makes a bug exploitable and conveying this information to the developer. In this paper, we present the design and implementation of a practical vulnerability assessment framework, called Melange. Melange performs data and control flow analysis to diagnose potential security bugs, and outputs well-formatted bug reports that help developers understand and fix security bugs. Based on the intuition that real-world vulnerabilities manifest themselves across multiple parts of a program, Melange performs both local and global analyses. To scale up to large programs, global analysis is demand-driven. Our prototype detects multiple vulnerability classes in C and C++ code including type confusion, and garbage memory reads. We have evaluated Melange extensively. Our case studies show that Melange scales up to large codebases such as Chromium, is easy-to-use, and most importantly, capable of discovering vulnerabilities in real-world code. Our findings indicate that static analysis is a viable reinforcement to the software testing tool set.Comment: A revised version to appear in the proceedings of the 13th conference on Detection of Intrusions and Malware & Vulnerability Assessment (DIMVA), July 201

JITANA: A modern hybrid program analysis framework for android platforms

Security vetting of Android apps is often performed under tight time constraints (e.g., a few minutes). As such, vetting activities must be performed “at speed”, when an app is submitted for distribution or a device is analyzed for malware. Existing static and dynamic program analysis approaches are not feasible for use in security analysis tools because they require a much longer time to operate than security analysts can afford. There are two factors that limit the performance and efficiency of current analysis approaches. First, existing approaches analyze only one app at a time. Finding security vulnerabilities in collaborative environments such as Android, however, requires collaborating apps to be analyzed simultaneously. Thus, existing approaches are not adequate when applied in this context. Second, existing static program analysis approaches tend to operate in a “closed world” fashion; therefore, they are not easily integrated with dynamic analysis processes to efficiently produce hybrid analysis results within a given time constraint. In this work, we introduce JITANA, an efficient and scalable hybrid program analysis framework for Android. JITANA has been designed from the ground up to be used as a building block to construct efficient and scalable program analysis techniques. JITANA also operates in an open world fashion, so malicious code detected as part of dynamic analysis can be quickly analyzed and the analysis results can be seamlessly integrated with the original static analysis results. To illustrate JITANA’s capability, we used it to analyze a large collection of apps simultaneously to identify potential collaborations among apps. We have also constructed several analysis techniques on top of JITANA and we use these to perform security vetting under four realistic scenarios. The results indicate that JITANA is scalable and robust; it can effectively and efficiently analyze complex apps including Facebook, Pokémon Go, and Pandora that the state-of-the-art approach cannot handle. In addition, we constructed a visualization engine as a plugin for JITANA to provide real-time feedback on code coverage to help analysts assess their vetting efforts. Such feedback can lead analysts to hard to reach code segments that may need further analysis. Finally we illustrate the effectiveness of JITANA in detecting and analyzing dynamically loaded code. Supplementary material attached below