Securing the Foundations of Practical Information Flow Control

Language-based information flow control (IFC) promises to secure computer programs against malicious or incompetent programmers by addressing key shortcomings of modern programming languages. In spite of showing great promise, the field remains under-utilised in practise. This thesis makes contributions to the theoretical foundations of IFC aimed at making the techniques practically applicable. The paper addresses two primary topics, IFC as a library and IFC without false alarms. The contributions range from foundational observations about soundness and completeness, to practical considerations of efficiency and expressiveness

Flexible Information-Flow Control

As more and more sensitive data is handled by software, its trustworthinessbecomes an increasingly important concern. This thesis presents work on ensuringthat information processed by computing systems is not disclosed to thirdparties without the user\u27s permission; i.e. to prevent unwanted flows ofinformation. While this problem is widely studied, proposed rigorousinformation-flow control approaches that enforce strong securityproperties like noninterference have yet to see widespread practical use.Conversely, lightweight techniques such as taint tracking are more prevalent inpractice, but lack formal underpinnings, making it unclear what guarantees theyprovide.This thesis aims to shrink the gap between heavyweight information-flow controlapproaches that have been proven sound and lightweight practical techniqueswithout formal guarantees such as taint tracking. This thesis attempts toreconcile these areas by (a) providing formal foundations to taint trackingapproaches, (b) extending information-flow control techniques to more realisticlanguages and settings, and (c) exploring security policies and mechanisms thatfall in between information-flow control and taint tracking and investigating whattrade-offs they incur

Detecting Violations of Access Control and Information Flow Policies in Data Flow Diagrams

The security of software-intensive systems is frequently attacked. High fines or loss in reputation are potential consequences of not maintaining confidentiality, which is an important security objective. Detecting confidentiality issues in early software designs enables cost-efficient fixes. A Data Flow Diagram (DFD) is a modeling notation, which focuses on essential, functional aspects of such early software designs. Existing confidentiality analyses on DFDs support either information flow control or access control, which are the most common confidentiality mechanisms. Combining both mechanisms can be beneficial but existing DFD analyses do not support this. This lack of expressiveness requires designers to switch modeling languages to consider both mechanisms, which can lead to inconsistencies. In this article, we present an extended DFD syntax that supports modeling both, information flow and access control, in the same language. This improves expressiveness compared to related work and avoids inconsistencies. We define the semantics of extended DFDs by clauses in first-order logic. A logic program made of these clauses enables the automated detection of confidentiality violations by querying it. We evaluate the expressiveness of the syntax in a case study. We attempt to model nine information flow cases and six access control cases. We successfully modeled fourteen out of these fifteen cases, which indicates good expressiveness. We evaluate the reusability of models when switching confidentiality mechanisms by comparing the cases that share the same system design, which are three pairs of cases. We successfully show improved reusability compared to the state of the art. We evaluated the accuracy of confidentiality analyses by executing them for the fourteen cases that we could model. We experienced good accuracy

An empirical study of information flows in real-world Javascript

Information flow analysis prevents secret or untrusted data from flowing into public or trusted sinks. Existing mechanisms cover a wide array of options, ranging from lightweight taint analysis to heavyweight information flow control that also considers implicit flows. Dynamic analysis, which is particularly popular for languages such as JavaScript, faces the question whether to invest in analyzing flows caused by not executing a particular branch, so-called hidden implicit flows. This paper addresses the questions how common different kinds of flows are in real-world programs, how important these flows are to enforce security policies, and how costly it is to consider these flows. We address these questions in an empirical study that analyzes 56 real-world JavaScript programs that suffer from various security problems, such as code injection vulnerabilities, denial of service vulnerabilities, memory leaks, and privacy leaks. The study is based on a state-of-the-art dynamic information flow analysis and a formalization of its core. We find that implicit flows are expensive to track in terms of permissiveness, label creep, and runtime overhead. We find a lightweight taint analysis to be sufficient for most of the studied security problems, while for some privacy-related code, observable tracking is sometimes required. In contrast, we do not find any evidence that tracking hidden implicit flows reveals otherwise missed security problems. Our results help security analysts and analysis designers to understand the cost-benefit tradeoffs of information flow analysis and provide empirical evidence that analyzing information flows in a cost-effective way is a relevant problem