More Like this

1. Leveraging SecDevOps to Tackle the Technical Debt Associated with Cybersecurity Attack Tactics

   https://doi.org/10.1109/TechDebt.2019.00012  

   Izurieta, Clemente; Prouty, Mary (May 2019, ACM-IEEE Second International Conference on Technical Debt, TechDebt 2019)

   null (Ed.)

   Context: Managing technical debt (TD) associated with external cybersecurity attacks on an organization can significantly improve decisions made when prioritizing which security weaknesses require attention. Whilst source code vulnerabilities can be found using static analysis techniques, malicious external attacks expose the vulnerabilities of a system at runtime and can sometimes remain hidden for long periods of time. By mapping malicious attack tactics to the consequences of weaknesses (i.e. exploitable source code vulnerabilities) we can begin to understand and prioritize the refactoring of the source code vulnerabilities that cause the greatest amount of technical debt on a system. Goal: To establish an approach that maps common external attack tactics to system weaknesses. The consequences of a weakness associated with a specific attack technique can then be used to determine the technical debt principal of said violation; which can be measured in terms of loss of business rather than source code maintenance. Method: We present a position study that uses Jaccard similarity scoring to examine how 11 malicious attack tactics can relate to Common Weakness Enumerations (CWEs). Results: We conduct a study to simulate attacks, and generate dependency graphs between external attacks and the technical consequences associated with CWEs. Conclusion: The mapping of cyber security attacks to weaknesses allows operational staff (SecDevOps) to focus on deploying appropriate countermeasures and allows developers to focus on refactoring the vulnerabilities with the greatest potential for technical debt. 

   more » « less
2. Towards Usable Scoring of Common Weaknesses

   https://doi.org/10.5220/0012090900003555  

   Adebiyi, Olutola; Albanese, Massimiliano Albanese (July 2023, SECRYPT)

   As the number and severity of security incidents continue to increase, remediating vulnerabilities and weaknesses has become a daunting task due to the sheer number of known vulnerabilities. Different scoring systems have been developed to provide qualitative and quantitative assessments of the severity of common vulnerabilities and weaknesses, and guide the prioritization of vulnerability remediation. However, these scoring systems provide only generic rankings of common weaknesses, which do not consider the specific vulnerabilities that exist in each system. To address this limitation, and building on recent principled approaches to vulnerability scoring, we propose new common weakness scoring metrics that consider the findings of vulnerability scanners, including the number of instances of each vulnerability across a system, and enable system-specific rankings that can provide actionable intelligence to security administrators. We built a small testbed to evaluate the proposed metrics against an existing metric, and show that the results are consistent with our intuition. 

   more » « less
3. Come for syntax, stay for speed, write secure code: an empirical study of security weaknesses in Julia programs

   https://doi.org/10.1007/s10664-024-10606-w  

   Zhang, Yue; Murphy, Justin; Rahman, Akond (January 2025, Empirical Software Engineering)

   Abstract ContextPractitioners prefer to achieve performance without sacrificing productivity when developing scientific software. The Julia programming language is designed to develop performant computer programs without sacrificing productivity by providing a syntax that is scripting in nature. According to the Julia programming language website, the common projects are data science, machine learning, scientific domains, and parallel computing. While Julia has yielded benefits with respect to productivity, programs written in Julia can include security weaknesses, which can hamper the security of Julia-based scientific software. A systematic derivation of security weaknesses can facilitate secure development of Julia programs—an area that remains under-explored. ObjectiveThe goal of this paper is to help practitioners securely develop Julia programs by conducting an empirical study of security weaknesses found in Julia programs. MethodWe apply qualitative analysis on 4,592 Julia programs used in 126 open-source Julia projects to identify security weakness categories. Next, we construct a static analysis tool calledJuliaStaticAnalysisTool (JSAT) that automatically identifies security weaknesses in Julia programs. We apply JSAT to automatically identify security weaknesses in 558 open-source Julia projects consisting of 25,008 Julia programs. ResultsWe identify 7 security weakness categories, which include the usage of hard-coded password and unsafe invocation. From our empirical study we identify 23,839 security weaknesses. On average, we observe 24.9% Julia source code files to include at least one of the 7 security weakness categories. ConclusionBased on our research findings, we recommend rigorous inspection efforts during code reviews. We also recommend further development and application of security static analysis tools so that security weaknesses in Julia programs can be detected before execution. 

   more » « less
4. Shifting Left for Machine Learning: An Empirical Study of Security Weaknesses in Supervised Learning-based Projects

   https://doi.org/10.1109/COMPSAC54236.2022.00130  

   Bhuiyan, Farzana Ahamed; Prowell, Stacy; Shahriar, Hossain; Wu, Fan; Rahman, Akond (June 2022, 2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC), 2022)

   Context: Supervised learning-based projects (SLPs), i.e., software projects that use supervised learning algorithms, such as decision trees are useful for performing classification-related tasks. Yet, security weaknesses, such as the use of hard-coded passwords in SLPs, can make SLPs susceptible to security attacks. A characterization of security weaknesses in SLPs can help practitioners understand the security weaknesses that are frequent in SLPs and adopt adequate mitigation strategies. Objective: The goal of this paper is to help practitioners securely develop supervised learning-based projects by conducting an empirical study of security weaknesses in supervised learning-based projects. Methodology: We conduct an empirical study by quantifying the frequency of security weaknesses in 278 open source SLPs. Results: We identify 22 types of security weaknesses that occur in SLPs. We observe ‘use of potentially dangerous function’ to be the most frequently occurring security weakness in SLPs. Of the identified 3,964 security weaknesses, 23.79% and 40.49% respectively, appear for source code files used to train and test models. We also observe evidence of co-location, e.g., instances of command injection co-locates with instances of potentially dangerous function. Conclusion: Based on our findings, we advocate for a shift left approach for SLP development with security-focused code reviews, and application of security static analysis. 

   more » « less
5. Detecting and Characterizing Propagation of Security Weaknesses in Puppet-based infrastructure Management

   https://doi.org/10.1109/TSE.2023.3265962  

   Rahman, Akond; Parnin, Chris (June 2023, IEEE Transactions on Software Engineering)

   Sebastian Uchitel (Ed.)

   Despite being beneficial for managing computing infrastructure automatically, Puppet manifests are susceptible to security weaknesses, e.g., hard-coded secrets and use of weak cryptography algorithms. Adequate mitigation of security weaknesses in Puppet manifests is thus necessary to secure computing infrastructure that are managed with Puppet manifests. A characterization of how security weaknesses propagate and affect Puppet-based infrastructure management, can inform practitioners on the relevance of the detected security weaknesses, as well as help them take necessary actions for mitigation. We conduct an empirical study with 17,629 Puppet manifests with Taint Tracker for Pup pet Manifests ( TaintPup ). We observe 2.4 times more precision, and 1.8 times more F-measure for TaintPup, compared to that of a state-of-the-art security static analysis tool. From our empirical study, we observe security weaknesses to propagate into 4,457 resources, i.e, Puppet-specific code elements used to manage infrastructure. A single instance of a security weakness can propagate into as many as 35 distinct resources. We observe security weaknesses to propagate into 7 categories of resources, which include resources used to manage continuous integration servers and network controllers. According to our survey with 24 practitioners, propagation of security weaknesses into data storage-related resources is rated to have the most severe impact for Puppet-based infrastructure management. 

   more » « less