Sajal Halder

Michael Robinson, Sajal Halder, Muhammad Ejaz Ahmed et al.

Understanding vulnerability propagation is essential for assessing how vulnerabilities spread across components of a software package. This supports more accurate impact analysis and enhances threat detection and mitigation. In this paper, we investigate how a small number of vulnerable JavaScript packages contribute to the creation of a disproportionately large number of vulnerable packages. This paper presents insights from 1,515 reported vulnerabilities gathered from a custom-built vulnerability database containing 1,077,946 JavaScript packages sourced from `npm-follower' and their associated dependency networks. Dependency networks were constructed using the deps.dev API, with vulnerabilities identified by parsing package names and version numbers through the Google Open Source Vulnerability API. Our findings reveal that 61.30% (660,748) of packages are reliant on one or more dependency packages, and 21.60% (232,836) of total packages have at least one known vulnerability throughout their dependency networks -- of which most (42%) are of High severity. We also found that it takes, on average, approximately 4 years and 11 months to fix a vulnerable package from when the first vulnerable version is published on npm -- although publication times of vulnerabilities occur approximately 19 days after a fix is available. Finally, we observe a high concentration of frequently present vulnerabilities throughout dependency networks, with the top-7 most frequent vulnerabilities accounting for 25% of vulnerability cases and the top-23 most frequent accounting for 50%. Based on these findings, we propose recommendations for developers and package managers to mitigate the threat and occurrence of vulnerabilities within the npm dependency network and the broader software repository community.

Sishun Liu, Sajal Halder, Ke Deng et al.

Information Operations on social media networks have been identified as a significant threat to democracy and modern society, but they are challenging and expensive to detect by humans. Existing supervised IO detection methods fail to capture the dynamic nature of evolving IO user behavior, while existing unsupervised approaches rely on oversimplified assumptions of coordination among IO users that may not exist in practice. To overcome the limitations of existing methods, we formulate IO user detection as an anomaly detection problem and propose a novel unsupervised IO user detection approach called Temporal-bEhavior-laNguage Signals for information Operation Recognition (TENSOR), which leverages multimodal data, including temporal online user behavior, such as message posting activities, and the textual content of the messages. The motivation is that IO users are typically a very small fraction of all online users and have unique temporal behavioral and language patterns. Specifically, we train a Temporal Point Process (TPP) to capture abnormal temporal behavioral patterns of IO users because they are known to behave in a coordinated manner for IO campaigns. We further introduce a novel evidence function that converts LLM responses, which are generated from user post timelines, into quantitative scores to adjust the TPP outputs for better IO user detection. Experimental results show that TENSOR outperforms the baselines on five real-world IO datasets. Code is available at https://github.com/xiuzhenzhang/TENSOR.