# Securing the AI Supply Chain: What Can We Learn From Developer-Reported Security Issues and Solutions of AI Projects?

arXiv: https://arxiv.org/abs/2512.23385 · HTML: https://mlsanity.com/paper/2512.23385/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 89 references that are in the map.
- First posted on arXiv 29 Dec 2025.

## Builds on (most cited first, 30 of 34)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [How is ChatGPT's behavior changing over time?](https://mlsanity.com/paper/2307.09009/) (2023)
- [Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspective](https://mlsanity.com/paper/2112.06409/) (2021)
- [Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems](https://mlsanity.com/paper/2401.05778/) (2024)
- [SecureBERT: A Domain-Specific Language Model for Cybersecurity](https://mlsanity.com/paper/2204.02685/) (2022)
- [LLMs in Software Security: A Survey of Vulnerability Detection Techniques and Insights](https://mlsanity.com/paper/2502.07049/) (2025)
- [A Survey on Data-driven Software Vulnerability Assessment and Prioritization](https://mlsanity.com/paper/2107.08364/) (2021)
- [Models Are Codes: Towards Measuring Malicious Code Poisoning Attacks on Pre-trained Model Hubs](https://mlsanity.com/paper/2409.09368/) (2024)
- [A Large-Scale Exploit Instrumentation Study of AI/ML Supply Chain Attacks in Hugging Face Models](https://mlsanity.com/paper/2410.04490/) (2024)
- [PatchRNN: A Deep Learning-Based System for Security Patch Identification](https://mlsanity.com/paper/2108.03358/) (2021)
- [Commit2Vec: Learning Distributed Representations of Code Changes](https://mlsanity.com/paper/1911.07605/) (2019)
- [Deep Learning Model Reuse in the HuggingFace Community: Challenges, Benefit and Trends](https://mlsanity.com/paper/2401.13177/) (2024)
- [VulCurator: A Vulnerability-Fixing Commit Detector](https://mlsanity.com/paper/2209.03260/) (2022)
- [PUMiner: Mining Security Posts from Developer Question and Answer Websites with PU Learning](https://mlsanity.com/paper/2003.03741/) (2020)
- [Security for Machine Learning-based Software Systems: a survey of threats, practices and challenges](https://mlsanity.com/paper/2201.04736/) (2022)
- [Threat Assessment in Machine Learning based Systems](https://mlsanity.com/paper/2207.00091/) (2022)
- [Software Vulnerability Prediction in Low-Resource Languages: An Empirical Study of CodeBERT and ChatGPT](https://mlsanity.com/paper/2404.17110/) (2024)
- [What do we know about Hugging Face? A systematic literature review and quantitative validation of qualitative claims](https://mlsanity.com/paper/2406.08205/) (2024)
- [LLMSecConfig: An LLM-Based Approach for Fixing Software Container Misconfigurations](https://mlsanity.com/paper/2502.02009/) (2025)
- [What Do Users Ask in Open-Source AI Repositories? An Empirical Study of GitHub Issues](https://mlsanity.com/paper/2303.09795/) (2023)
- [Trust in Software Supply Chains: Blockchain-Enabled SBOM and the AIBOM Future](https://mlsanity.com/paper/2307.02088/) (2023)
- [On the Suitability of Hugging Face Hub for Empirical Studies](https://mlsanity.com/paper/2307.14841/) (2023)
- [Mitigating Data Imbalance for Software Vulnerability Assessment: Does Data Augmentation Help?](https://mlsanity.com/paper/2407.10722/) (2024)
- [Automated Code-centric Software Vulnerability Assessment: How Far Are We? An Empirical Study in C/C++](https://mlsanity.com/paper/2407.17053/) (2024)
- [Automatic Data Labeling for Software Vulnerability Prediction Models: How Far Are We?](https://mlsanity.com/paper/2407.17803/) (2024)
- [How Do Developers Deal with Security Issue Reports on GitHub?](https://mlsanity.com/paper/2112.10359/) (2021)

## Built on by (most cited first, 1 of 1)

- [A Security-Oriented Lifecycle Model for Large Language Model Systems](https://mlsanity.com/paper/2608.03626/) (2026)

## Abstract (arXiv)

The rapid growth of Artificial Intelligence (AI) models and applications has led to an increasingly complex security landscape. Developers of AI projects must contend not only with traditional software supply chain issues but also with novel, AI-specific security threats. However, little is known about what security issues are commonly encountered and how they are resolved in practice. This gap hinders the development of effective security measures for each component of the AI supply chain. We bridge this gap by conducting an empirical investigation of developer-reported issues and solutions, based on discussions from Hugging Face and GitHub. To identify security-related discussions, we develop a pipeline that combines keyword matching with an optimal fine-tuned distilBERT classifier, which achieved the best performance in our extensive comparison of various deep learning and large language models. This pipeline produces a dataset of 312,868 security discussions, providing insights into the security reporting practices of AI applications and projects. We conduct a thematic analysis of 753 posts sampled from our dataset and uncover a fine-grained taxonomy of 32 security issues and 24 solutions across four themes: (1) System and Software, (2) External Tools and Ecosystem, (3) Model, and (4) Data. We reveal that many security issues arise from the complex dependencies and black-box nature of AI components. Notably, challenges related to Models and Data often lack concrete solutions. Our insights can offer evidence-based guidance for developers and researchers to address real-world security threats across the AI supply chain.