SoK: Understanding Vulnerabilities in the Large Language Model Supply Chain
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909499604860928 |
|---|---|
| author | Wang, Shenao Zhao, Yanjie Liu, Zhao Zou, Quanchen Wang, Haoyu |
| author_facet | Wang, Shenao Zhao, Yanjie Liu, Zhao Zou, Quanchen Wang, Haoyu |
| contents | Large Language Models (LLMs) transform artificial intelligence, driving advancements in natural language understanding, text generation, and autonomous systems. The increasing complexity of their development and deployment introduces significant security challenges, particularly within the LLM supply chain. However, existing research primarily focuses on content safety, such as adversarial attacks, jailbreaking, and backdoor attacks, while overlooking security vulnerabilities in the underlying software systems. To address this gap, this study systematically analyzes 529 vulnerabilities reported across 75 prominent projects spanning 13 lifecycle stages. The findings show that vulnerabilities are concentrated in the application (50.3%) and model (42.7%) layers, with improper resource control (45.7%) and improper neutralization (25.1%) identified as the leading root causes. Additionally, while 56.7% of the vulnerabilities have available fixes, 8% of these patches are ineffective, resulting in recurring vulnerabilities. This study underscores the challenges of securing the LLM ecosystem and provides actionable insights to guide future research and mitigation strategies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_12497 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SoK: Understanding Vulnerabilities in the Large Language Model Supply Chain Wang, Shenao Zhao, Yanjie Liu, Zhao Zou, Quanchen Wang, Haoyu Cryptography and Security Large Language Models (LLMs) transform artificial intelligence, driving advancements in natural language understanding, text generation, and autonomous systems. The increasing complexity of their development and deployment introduces significant security challenges, particularly within the LLM supply chain. However, existing research primarily focuses on content safety, such as adversarial attacks, jailbreaking, and backdoor attacks, while overlooking security vulnerabilities in the underlying software systems. To address this gap, this study systematically analyzes 529 vulnerabilities reported across 75 prominent projects spanning 13 lifecycle stages. The findings show that vulnerabilities are concentrated in the application (50.3%) and model (42.7%) layers, with improper resource control (45.7%) and improper neutralization (25.1%) identified as the leading root causes. Additionally, while 56.7% of the vulnerabilities have available fixes, 8% of these patches are ineffective, resulting in recurring vulnerabilities. This study underscores the challenges of securing the LLM ecosystem and provides actionable insights to guide future research and mitigation strategies. |
| title | SoK: Understanding Vulnerabilities in the Large Language Model Supply Chain |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2502.12497 |