Lifting the Veil on the Large Language Model Supply Chain: Composition, Risks, and Mitigations

• URL: http://arxiv.org/abs/2410.21218v2
• Date: Thu, 31 Oct 2024 03:14:16 GMT
• Title: Lifting the Veil on the Large Language Model Supply Chain: Composition, Risks, and Mitigations
• Authors: Kaifeng Huang, Bihuan Chen, You Lu, Susheng Wu, Dingji Wang, Yiheng Huang, Haowen Jiang, Zhuotong Zhou, Junming Cao, Xin Peng,
• Abstract summary: Large language models (LLM) have sparked significant impact with regard to both intelligence and productivity. This paper provides a comprehensive overview of the LLM supply chain, detailing the stakeholders, composing artifacts, and the supplying types.
• Score: 6.478930807409979
• License:
• Abstract: Large language models (LLM) have sparked significant impact with regard to both intelligence and productivity. In recent years, a great surge has been witnessed in the introduction of both commercial and open-source LLMs. Many businesses have adopted the LLMs into their applications to solve their own domain-specific tasks. However, integrating LLMs into specific business scenarios requires more than just utilizing the models themselves. Instead, it is a systematic process that involves substantial components, which are collectively referred to as the LLM supply chain. The LLM supply chain inherently carries risks. Therefore, it is essential to understand the types of components that may be introduced into the supply chain and the associated risks, enabling different stakeholders to implement effective mitigation measures. While some literature discusses risks associated with LLMs, there is currently no paper that clearly outlines the LLM supply chain from the perspective of both providing and consuming its components. As LLMs have become essential infrastructure in the new era, we believe that a thorough review of the LLM supply chain, along with its inherent risks and mitigation strategies, would be valuable for industry practitioners to avoid potential damages and losses, and enlightening for academic researchers to rethink existing approaches and explore new avenues of research. Our paper provides a comprehensive overview of the LLM supply chain, detailing the stakeholders, composing artifacts, and the supplying types. We developed taxonomies of risk types, risky actions, and mitigations related to various supply chain stakeholders and components. In summary, our work explores the technical and operational aspects of the LLM supply chain, offering valuable insights for researchers and engineers in the evolving LLM landscape.

Related papers

• Large Language Model Supply Chain: Open Problems From the Security Perspective [25.320736806895976]
  Large Language Model (LLM) is changing the software development paradigm and has gained huge attention from both academia and industry. We take the first step to discuss the potential security risks in each component as well as the integration between components of LLM SC.
  arXiv  Detail & Related papers  (2024-11-03T15:20:21Z)
• Supply Chain Network Extraction and Entity Classification Leveraging Large Language Models [5.205252810216621]
  We develop a supply chain graph for the civil engineering sector using large language models (LLMs) We fine-tune an LLM to classify entities within the supply chain graph, providing detailed insights into their roles and relationships. Our contributions include the development of a supply chain graph for the civil engineering sector, as well as a fine-tuned LLM model that enhances entity classification and understanding of supply chain networks.
  arXiv  Detail & Related papers  (2024-10-16T21:24:13Z)
• Prompt Leakage effect and defense strategies for multi-turn LLM interactions [95.33778028192593]
  Leakage of system prompts may compromise intellectual property and act as adversarial reconnaissance for an attacker. We design a unique threat model which leverages the LLM sycophancy effect and elevates the average attack success rate (ASR) from 17.7% to 86.2% in a multi-turn setting. We measure the mitigation effect of 7 black-box defense strategies, along with finetuning an open-source model to defend against leakage attempts.
  arXiv  Detail & Related papers  (2024-04-24T23:39:58Z)
• Large Language Model Supply Chain: A Research Agenda [5.1875389249043415]
  Large language models (LLMs) have revolutionized artificial intelligence, introducing unprecedented capabilities in natural language processing and multimodal content generation. This paper provides the first comprehensive research agenda of the LLM supply chain, offering a structured approach to identify critical challenges and opportunities.
  arXiv  Detail & Related papers  (2024-04-19T09:29:53Z)
• Large Language Models for Blockchain Security: A Systematic Literature Review [32.36531880327789]
  Large Language Models (LLMs) have emerged as powerful tools across various domains within cyber security. This study aims to comprehensively analyze and understand existing research, and elucidate how LLMs contribute to enhancing the security of blockchain systems.
  arXiv  Detail & Related papers  (2024-03-21T10:39:44Z)
• Rethinking Machine Unlearning for Large Language Models [85.92660644100582]
  We explore machine unlearning in the domain of large language models (LLMs) This initiative aims to eliminate undesirable data influence (e.g., sensitive or illegal information) and the associated model capabilities.
  arXiv  Detail & Related papers  (2024-02-13T20:51:58Z)
• Knowledge Fusion of Large Language Models [73.28202188100646]
  This paper introduces the notion of knowledge fusion for large language models (LLMs) We externalize their collective knowledge and unique strengths, thereby elevating the capabilities of the target model beyond those of any individual source LLM. Our findings confirm that the fusion of LLMs can improve the performance of the target model across a range of capabilities such as reasoning, commonsense, and code generation.
  arXiv  Detail & Related papers  (2024-01-19T05:02:46Z)
• Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems [29.828997665535336]
  Large language models (LLMs) have strong capabilities in solving diverse natural language processing tasks. However, the safety and security issues of LLM systems have become the major obstacle to their widespread application. This paper proposes a comprehensive taxonomy, which systematically analyzes potential risks associated with each module of an LLM system.
  arXiv  Detail & Related papers  (2024-01-11T09:29:56Z)
• A Comprehensive Overview of Large Language Models [68.22178313875618]
  Large Language Models (LLMs) have recently demonstrated remarkable capabilities in natural language processing tasks. This article provides an overview of the existing literature on a broad range of LLM-related concepts.
  arXiv  Detail & Related papers  (2023-07-12T20:01:52Z)
• How Can Recommender Systems Benefit from Large Language Models: A Survey [82.06729592294322]
  Large language models (LLM) have shown impressive general intelligence and human-like capabilities. We conduct a comprehensive survey on this research direction from the perspective of the whole pipeline in real-world recommender systems.
  arXiv  Detail & Related papers  (2023-06-09T11:31:50Z)
• On the Risk of Misinformation Pollution with Large Language Models [127.1107824751703]
  We investigate the potential misuse of modern Large Language Models (LLMs) for generating credible-sounding misinformation. Our study reveals that LLMs can act as effective misinformation generators, leading to a significant degradation in the performance of Open-Domain Question Answering (ODQA) systems.
  arXiv  Detail & Related papers  (2023-05-23T04:10:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.