An Empirical Study on Using Large Language Models to Analyze Software Supply Chain Security Failures
Open accessAbstract
As we increasingly depend on software systems, the consequences of breaches in the software supply chain become more severe. High-profile cyber attacks like SolarWinds and ShadowHammer have resulted in significant financial and data losses, underlining the need for stronger cybersecurity. One way to prevent future breaches is by studying past failures. However, traditional methods of analyzing past failures require manually reading and summarizing reports about them. Automated support could reduce costs and allow analysis of more failures. Natural Language Processing (NLP) techniques such as Large Language Models (LLMs) could be leveraged to assist the analysis of failures. In this study, we assessed the ability of Large Language Models (LLMs) to analyze historical software supply chain breaches. We used LLMs to replicate the manual analysis of 69 software supply chain security failures performed by members of the Cloud Native Computing Foundation (CNCF). We developed prompts for LLMs to categorize these by four dimensions: type of compromise, intent, nature, and impact. GPT 3.5's categorizations had an average accuracy of 68% and Bard's had an accuracy of 58% over these dimensions. We report that LLMs effectively characterize software supply chain failures when the source articles are detailed enough for consensus among manual analysts, but cannot yet replace human analysts. Future work can improve LLM performance in this context, and study a broader range of articles and failures.
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LLMs show good potential for automatically analyzing and summarizing software supply chain security vulnerabilities, especially when the data is comprehensive, but still cannot replace human analysts in this specific field.
“Their results show that LLMs show good potential, especially when the data is comprehensive, but still cannot replace human analysts in this specific field.”
From When LLMs meet cybersecurity: a systematic literature review (2025) · cites this paper as Singla et al. 2023
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- Authors
- Singla, T., Anandayuvaraj, D., Kalu, K. G., Schorlemmer, T. R., & Davis, J. C.
- Affiliations
- Purdue University West Lafayette (US)
- Venue
- Proceedings of the 2023 Workshop on Software Supply Chain Offensive Research and Ecosystem Defenses
- Publisher
- ACM
- Published
- 2023-11-26
- Pages
- 5-15
- OpenAlex
- W4388954904
- Cited by
- 26
- References
- 45
- Citations by year
- 2026: 5 · 2025: 16 · 2024: 4 · 2023: 1
- FWCI
- 6.68
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- ACM Workshop on Software Supply Chain Offensive Research and Ecosystem Defenses (SCORED) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- NSF (2229703)
- Cisco
- Topics
- Physical Sciences › Computer Science › Information Systems › Information and Cyber Security
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- software supply chain security, large language models, failure analysis, prompt engineering, automated security analysis, GPT-3.5, Google Bard, SolarWinds
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