Brandon Wroblewski

Mahzabin Tamanna, Yash Chandrani, Matthew Burrows et al.

Build scripts automate the process of compiling source code, managing dependencies, running tests, and packaging software into deployable artifacts. These scripts are ubiquitous in modern software development pipelines for streamlining testing and delivery. While developing build scripts, practitioners may inadvertently introduce code smells, which are recurring patterns of poor coding practices that may lead to build failures or increase risk and technical debt. The goal of this study is to aid practitioners in avoiding code smells in build scripts through an empirical study of build scripts and issues on GitHub.We employed a mixed-methods approach, combining qualitative and quantitative analysis. First, we conducted a qualitative analysis of 2000 build-script-related GitHub issues to understand recurring smells. Next, we developed a static analysis tool, Sniffer, to automatically detect code smells in 5882 build scripts of Maven, Gradle, CMake, and Make files, collected from 4877 open-source GitHub repositories. To assess Sniffer's performance, we conducted a user study, where Sniffer achieved higher precision, recall, and F-score. We identified 13 code smell categories, with a total of 10,895 smell occurrences, where 3184 were in Maven, 1214 in Gradle, 337 in CMake, and 6160 in Makefiles. Our analysis revealed that Insecure URLs were the most prevalent code smell in Maven build scripts, while HardcodedPaths/URLs were commonly observed in both Gradle and CMake scripts. Wildcard Usage emerged as the most frequent smell in Makefiles. The co-occurrence analysis revealed strong associations between specific smell pairs of Hardcoded Paths/URLs with Duplicates, and Inconsistent Dependency Management with Empty or Incomplete Tags, which indicate potential underlying issues in the build script structure and maintenance practices.

Md Rayhanur Rahman, Brandon Wroblewski, Quinn Matthews et al.

Defending from cyberattacks requires practitioners to operate on high-level adversary behavior. Cyberthreat intelligence (CTI) reports on past cyberattack incidents describe the chain of malicious actions with respect to time. To avoid repeating cyberattack incidents, practitioners must proactively identify and defend against recurring chain of actions - which we refer to as temporal attack patterns. Automatically mining the patterns among actions provides structured and actionable information on the adversary behavior of past cyberattacks. The goal of this paper is to aid security practitioners in prioritizing and proactive defense against cyberattacks by mining temporal attack patterns from cyberthreat intelligence reports. To this end, we propose ChronoCTI, an automated pipeline for mining temporal attack patterns from cyberthreat intelligence (CTI) reports of past cyberattacks. To construct ChronoCTI, we build the ground truth dataset of temporal attack patterns and apply state-of-the-art large language models, natural language processing, and machine learning techniques. We apply ChronoCTI on a set of 713 CTI reports, where we identify 124 temporal attack patterns - which we categorize into nine pattern categories. We identify that the most prevalent pattern category is to trick victim users into executing malicious code to initiate the attack, followed by bypassing the anti-malware system in the victim network. Based on the observed patterns, we advocate organizations to train users about cybersecurity best practices, introduce immutable operating systems with limited functionalities, and enforce multi-user authentications. Moreover, we advocate practitioners to leverage the automated mining capability of ChronoCTI and design countermeasures against the recurring attack patterns.

Md Nazmul Haque, Sivana Hamer, Brandon Wroblewski et al.

Large-scale cyberattacks, referred to as campaigns, are documented across multiple CTI reports from diverse sources, with some providing a high-level overview of attack techniques and others providing technical details. Extracting attack techniques from reports is essential for organizations to identify the controls required to protect against attacks. Manually extracting techniques at scale is impractical. Existing automated methods focus on single reports, leaving many attack techniques and their controls undetected, resulting in a fragmented view of campaign behavior. The goal of this study is to aid security researchers in extracting attack techniques and controls from a campaign by replicating and comparing the performance of the state-of-the-art ATT&CK technique extraction methods in a multi-report campaign setting compared to prior single-report evaluations. We conduct an empirical study of 29 methods to extract attack techniques, spanning named entity recognition (NER), encoder-based classification, and decoder-based LLM approaches. Our study analyzes 90 CTI reports across three major attack campaigns: SolarWinds, XZ Utils, and Log4j, using both quantitative performance metrics and their impact on controls. Our results show that aggregating multiple CTI reports improves the F1 score by about 26% over single-report analysis, with most approaches reaching performance saturation after 5--15 reports. Despite these gains, extraction performance remains limited, with maximum F1 scores of 78.6% for SolarWinds and 54.9% for XZ Utils. Moreover, up to 33.3% of misclassifications involve semantically similar techniques that share tactics and overlap in descriptions. The misclassification has a disproportionate effect on control coverage. Reports that are longer and include technical details consistently perform better, even though their readability scores are low.