Abstract Model Counting: A Novel Approach for Quantification of Information Leaks

acmid: 2590328 keywords: model checking, quantitative information flow, satisfiability modulo theories, symbolic execution location: Kyoto, Japan numpages: 10acmid: 2590328 keywords: model checking, quantitative information flow, satisfiability modulo theories, symbolic execution location: Kyoto, Japan numpages: 10acmid: 2590328 keywords: model checking, quantitative information flow, satisfiability modulo theories, symbolic execution location: Kyoto, Japan numpages: 10We present a novel method for Quantitative Information Flow analysis. We show how the problem of computing information leakage can be viewed as an extension of the Satisfiability Modulo Theories (SMT) problem. This view enables us to develop a framework for QIF analysis based on the framework DPLL(T) used in SMT solvers. We then show that the methodology of Symbolic Execution (SE) also fits our framework. Based on these ideas, we build two QIF analysis tools: the first one employs CBMC, a bounded model checker for ANSI C, and the second one is built on top of Symbolic PathFinder, a Symbolic Executor for Java. We use these tools to quantify leaks in industrial code such as C programs from the Linux kernel, a Java tax program from the European project HATS, and anonymity protocol

Attacker Control and Impact for Confidentiality and Integrity

Language-based information flow methods offer a principled way to enforce strong security properties, but enforcing noninterference is too inflexible for realistic applications. Security-typed languages have therefore introduced declassification mechanisms for relaxing confidentiality policies, and endorsement mechanisms for relaxing integrity policies. However, a continuing challenge has been to define what security is guaranteed when such mechanisms are used. This paper presents a new semantic framework for expressing security policies for declassification and endorsement in a language-based setting. The key insight is that security can be characterized in terms of the influence that declassification and endorsement allow to the attacker. The new framework introduces two notions of security to describe the influence of the attacker. Attacker control defines what the attacker is able to learn from observable effects of this code; attacker impact captures the attacker's influence on trusted locations. This approach yields novel security conditions for checked endorsements and robust integrity. The framework is flexible enough to recover and to improve on the previously introduced notions of robustness and qualified robustness. Further, the new security conditions can be soundly enforced by a security type system. The applicability and enforcement of the new policies is illustrated through various examples, including data sanitization and authentication

The Anatomy and Facets of Dynamic Policies

Information flow policies are often dynamic; the security concerns of a program will typically change during execution to reflect security-relevant events. A key challenge is how to best specify, and give proper meaning to, such dynamic policies. A large number of approaches exist that tackle that challenge, each yielding some important, but unconnected, insight. In this work we synthesise existing knowledge on dynamic policies, with an aim to establish a common terminology, best practices, and frameworks for reasoning about them. We introduce the concept of facets to illuminate subtleties in the semantics of policies, and closely examine the anatomy of policies and the expressiveness of policy specification mechanisms. We further explore the relation between dynamic policies and the concept of declassification.Comment: Technical Report of publication under the same name in Computer Security Foundations (CSF) 201

Dynamic Information Flow Analysis in Ruby

With the rapid increase in usage of the internet and online applications, there is a huge demand for applications to handle data privacy and integrity. Applications are already complex with business logic; adding the data safety logic would make them more complicated. The more complex the code becomes, the more possibilities it opens for security-critical bugs. To solve this conundrum, we can push this data safety handling feature to the language level rather than the application level. With a secure language, developers can write their application without having to worry about data security. This project introduces dynamic information flow analysis in Ruby. I extend the JRuby implementation, which is a widely used implementation of Ruby written in Java. Information flow analysis classifies variables used in the program into different security levels and monitors the data flow across levels. Ruby currently supports data integrity by a tainting mechanism. This project extends this tainting mechanism to handle implicit data flows, enabling it to protect confidentiality as well as integrity. Experimental results based on Ruby benchmarks are presented in this paper, which show that: This project protects confidentiality but at the cost of 1.2 - 10 times slowdown in execution time

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

Policy-agnostic programming on the client-side

Browser security has become a major concern especially due to web pages becoming more complex. These web applications handle a lot of information, including sensitive data that may be vulnerable to attacks like data exfiltration, cross-site scripting (XSS), etc. Most modern browsers have security mechanisms in place to prevent such attacks but they still fall short in preventing more advanced attacks like evolved variants of data exfiltration. Moreover, there is no standard that is followed to implement security into the browser. A lot of research has been done in the field of information flow security that could prove to be helpful in solving the problem of securing the client-side. Policy- agnostic programming is a programming paradigm that aims to make implementation of information flow security in real world systems more flexible. In this paper, we explore the use of policy-agnostic programming on the client-side and how it will help prevent common client-side attacks. We verify our results through a client-side salary management application. We show a possible attack and how our solution would prevent such an attack

Sound and Precise Malware Analysis for Android via Pushdown Reachability and Entry-Point Saturation

We present Anadroid, a static malware analysis framework for Android apps. Anadroid exploits two techniques to soundly raise precision: (1) it uses a pushdown system to precisely model dynamically dispatched interprocedural and exception-driven control-flow; (2) it uses Entry-Point Saturation (EPS) to soundly approximate all possible interleavings of asynchronous entry points in Android applications. (It also integrates static taint-flow analysis and least permissions analysis to expand the class of malicious behaviors which it can catch.) Anadroid provides rich user interface support for human analysts which must ultimately rule on the "maliciousness" of a behavior. To demonstrate the effectiveness of Anadroid's malware analysis, we had teams of analysts analyze a challenge suite of 52 Android applications released as part of the Auto- mated Program Analysis for Cybersecurity (APAC) DARPA program. The first team analyzed the apps using a ver- sion of Anadroid that uses traditional (finite-state-machine-based) control-flow-analysis found in existing malware analysis tools; the second team analyzed the apps using a version of Anadroid that uses our enhanced pushdown-based control-flow-analysis. We measured machine analysis time, human analyst time, and their accuracy in flagging malicious applications. With pushdown analysis, we found statistically significant (p < 0.05) decreases in time: from 85 minutes per app to 35 minutes per app in human plus machine analysis time; and statistically significant (p < 0.05) increases in accuracy with the pushdown-driven analyzer: from 71% correct identification to 95% correct identification.Comment: Appears in 3rd Annual ACM CCS workshop on Security and Privacy in SmartPhones and Mobile Devices (SPSM'13), Berlin, Germany, 201