Understanding and Identifying Vulnerabilities Related to Architectural Security Tactics

To engineer secure software systems, software architects elicit the system\u27s security requirements to adopt suitable architectural solutions. They often make use of architectural security tactics when designing the system\u27s security architecture. Security tactics are reusable solutions to detect, resist, recover from, and react to attacks. Since security tactics are the building blocks of a security architecture, flaws in the adoption of these tactics, their incorrect implementation, or their deterioration during software maintenance activities can lead to vulnerabilities, which we refer to as tactical vulnerabilities . Although security tactics and their correct adoption/implementation are crucial elements to achieve security, prior works have not investigated the architectural context of vulnerabilities. Therefore, this dissertation presents a research work whose major goals are: (i) to identify common types of tactical vulnerabilities, (ii) to investigate tactical vulnerabilities through in-depth empirical studies, and (iii) to develop a technique that detects tactical vulnerabilities caused by object deserialization. First, we introduce the Common Architectural Weakness Enumeration (CAWE), which is a catalog that enumerates 223 tactical vulnerability types. Second, we use this catalog to conduct an empirical study using vulnerability reports from large-scale open-source systems. Among our findings, we observe that Improper Input Validation was the most reoccurring vulnerability type. This tactical vulnerability type is caused by not properly implementing the Validate Inputs tactic. Although prior research focused on devising automated (or semi-automated) techniques for detecting multiple instances of improper input validation (e.g., SQL Injection and Cross-Site Scripting) one of them got neglected, which is the untrusted deserialization of objects. Unlike other input validation problems, object deserialization vulnerabilities exhibit a set of characteristics that are hard to handle for effective vulnerability detection. We currently lack a robust approach that can detect untrusted deserialization problems. Hence, this dissertation introduces DODO untrusteD ObjectDeserialization detectOr), a novel program analysis technique to detect deserialization vulnerabilities. DODO encompasses a sound static analysis of the program to extract potentially vulnerable paths, an exploit generation engine, and a dynamic analysis engine to verify the existence of untrusted object deserialization. Our experiments showed that DODO can successfully infer possible vulnerabilities that could arise at runtime during object deserialization