Security Analysis of the OWASP Benchmark with Julia

Among the various facets of cybersecurity, software security plays a crucial role. This requires the assessment of the security of programs and web applications exposed to the external world and consequently potential targets of attacks like SQL-injections, crosssite scripting, boundary violations, and command injections. The OWASP Benchmark Project developed a Java benchmark that contains thousands of test programs, featuring such security breaches. Its goal is to measure the ability of an analysis tool to identify vulnerabilities and its precision. We present how the Julia static analyzer, a sound tool based on abstract interpretation, performs on this benchmark in terms of soundness and precision. We discuss the details of its security analysis over a taint analysis of data, implemented through binary decision diagrams

Automatic Software Repair: a Bibliography

This article presents a survey on automatic software repair. Automatic software repair consists of automatically finding a solution to software bugs without human intervention. This article considers all kinds of repairs. First, it discusses behavioral repair where test suites, contracts, models, and crashing inputs are taken as oracle. Second, it discusses state repair, also known as runtime repair or runtime recovery, with techniques such as checkpoint and restart, reconfiguration, and invariant restoration. The uniqueness of this article is that it spans the research communities that contribute to this body of knowledge: software engineering, dependability, operating systems, programming languages, and security. It provides a novel and structured overview of the diversity of bug oracles and repair operators used in the literature

Formal Analysis of Vulnerabilities of Web Applications Based on SQL Injection (Extended Version)

We present a formal approach that exploits attacks related to SQL Injection (SQLi) searching for security flaws in a web application. We give a formal representation of web applications and databases, and show that our formalization effectively exploits SQLi attacks. We implemented our approach in a prototype tool called SQLfast and we show its efficiency on real-world case studies, including the discovery of an attack on Joomla! that no other tool can find

Static analysis of android apps interaction with automotive CAN

Modern car infotainment systems allow users to connect an Android device to the vehicle. The device then interacts with the hardware of the car, hence providing new interaction mechanisms to the driver. However, this can be misused and become a major security breach into the car, with subsequent security concerns: the Android device can both read sensitive data (speed, model, airbag status) and send dangerous commands (brake, lock, airbag explosion). Moreover, this scenario is unsettling since Android devices connect to the cloud, opening the door to remote attacks by malicious users or the cyberspace. The OpenXC platform is an open-source API that allows Android apps to interact with the car’s hardware. This article studies this library and shows how it can be used to create injection attacks. Moreover, it introduces a novel static analysis that identifies such attacks before they actually occur. It has been implemented in the Julia static analyzer and finds injection vulnerabilities in actual apps from the Google Play marketplace

Next Generation Black-Box Web Application Vulnerability Analysis Framework

abstract: Web applications are an incredibly important aspect of our modern lives. Organizations and developers use automated vulnerability analysis tools, also known as scanners, to automatically find vulnerabilities in their web applications during development. Scanners have traditionally fallen into two types of approaches: black-box and white-box. In the black-box approaches, the scanner does not have access to the source code of the web application whereas a white-box approach has access to the source code. Today’s state-of-the-art black-box vulnerability scanners employ various methods to fuzz and detect vulnerabilities in a web application. However, these scanners attempt to fuzz the web application with a number of known payloads and to try to trigger a vulnerability. This technique is simple but does not understand the web application that it is testing. This thesis, presents a new approach to vulnerability analysis. The vulnerability analysis module presented uses a novel approach of Inductive Reverse Engineering (IRE) to understand and model the web application. IRE first attempts to understand the behavior of the web application by giving certain number of input/output pairs to the web application. Then, the IRE module hypothesizes a set of programs (in a limited language specific to web applications, called AWL) that satisfy the input/output pairs. These hypotheses takes the form of a directed acyclic graph (DAG). AWL vulnerability analysis module can then attempt to detect vulnerabilities in this DAG. Further, it generates the payload based on the DAG, and therefore this payload will be a precise payload to trigger the potential vulnerability (based on our understanding of the program). It then tests this potential vulnerability using the generated payload on the actual web application, and creates a verification procedure to see if the potential vulnerability is actually vulnerable, based on the web application’s response.Dissertation/ThesisMasters Thesis Computer Science 201

Intriguing Properties of Adversarial ML Attacks in the Problem Space

Recent research efforts on adversarial ML have investigated problem-space attacks, focusing on the generation of real evasive objects in domains where, unlike images, there is no clear inverse mapping to the feature space (e.g., software). However, the design, comparison, and real-world implications of problem-space attacks remain underexplored. This paper makes two major contributions. First, we propose a novel formalization for adversarial ML evasion attacks in the problem-space, which includes the definition of a comprehensive set of constraints on available transformations, preserved semantics, robustness to preprocessing, and plausibility. We shed light on the relationship between feature space and problem space, and we introduce the concept of side-effect features as the byproduct of the inverse feature-mapping problem. This enables us to define and prove necessary and sufficient conditions for the existence of problem-space attacks. We further demonstrate the expressive power of our formalization by using it to describe several attacks from related literature across different domains. Second, building on our formalization, we propose a novel problem-space attack on Android malware that overcomes past limitations. Experiments on a dataset with 170K Android apps from 2017 and 2018 show the practical feasibility of evading a state-of-the-art malware classifier along with its hardened version. Our results demonstrate that "adversarial-malware as a service" is a realistic threat, as we automatically generate thousands of realistic and inconspicuous adversarial applications at scale, where on average it takes only a few minutes to generate an adversarial app. Our formalization of problem-space attacks paves the way to more principled research in this domain.Comment: This arXiv version (v2) corresponds to the one published at IEEE Symposium on Security & Privacy (Oakland), 202

Static analysis for discovering IoT vulnerabilities

The Open Web Application Security Project (OWASP), released the \u201cOWASP Top 10 Internet of Things 2018\u201d list of the high-priority security vulnerabilities for IoT systems. The diversity of these vulnerabilities poses a great challenge toward development of a robust solution for their detection and mitigation. In this paper, we discuss the relationship between these vulnerabilities and the ones listed by OWASP Top 10 (focused on Web applications rather than IoT systems), how these vulnerabilities can actually be exploited, and in which cases static analysis can help in preventing them. Then, we present an extension of an industrial analyzer (Julia) that already covers five out of the top seven vulnerabilities of OWASP Top 10, and we discuss which IoT Top 10 vulnerabilities might be detected by the existing analyses or their extension. The experimental results present the application of some existing Julia\u2019s analyses and their extension to IoT systems, showing its effectiveness of the analysis of some representative case studies

CHARACTERIZATION, DETECTION AND EXPLOITATION OF DATA INJECTION VULNERABILITIES IN ANDROID

Ph.DDOCTOR OF PHILOSOPH