mlsanityData to 14 Oct 2026

A Security-Oriented Lifecycle Model for Large Language Model Systems

arXiv 2608.03626 ↗First posted 4 Aug 2026cs.CR cs.AI cs.CYcited by 0

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Abstract (arXiv)

Large language models are being integrated into critical infrastructure and enterprise workflows at unprecedented scale,yet the lifecycle frameworks governing their development and operations were designed for operational efficiency rather than security analysis. As a result, security-relevant activities such as data provenance verification, artifact signing, agentic permission control, and decommissioning are often left implicit or assumed to receive due care. Governance frameworks, in turn, organise requirements around risk levels or management processes without clearly linking them to the lifecycle stages where they apply. This paper addresses both deficiencies. We propose a lifecycle model for LLM systems that supports security analysis by structuring it around security-relevant boundaries rather than workflow optimisation. The model comprises 32 stages across four core pipeline layers (Data, Model, Distribution, Application), supported by a 12-stage LLMOps pillar and a 9-category governance pillar. Thirteen stages are introduced here as separate units because they expose distinct security concerns that existing frameworks do not clearly distinguish. A governance mapping synthesising the NIST AI RMF, the EU AI Act, and ISO/IEC 42001 reveals a structural property of the current regulatory landscape: governance evidence concentrates at deployment-facing stages, where systems are visible to regulators, while the most consequential decisions, data selection, alignment strategy, and capability boundaries, are made at development-facing stages, where regulatory visibility is lowest.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 25 of its 49 references that are in the map. First posted on arXiv 4 Aug 2026.

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25 of 49 references.

References · 25

  1. 2023A Survey of Large Language Modelscited by 2,576
  2. 2023Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injectioncited by 1,443
  3. 2021Membership Inference Attacks on Machine Learning: A Surveycited by 321
  4. 2025Model Context Protocol (MCP): Landscape, Security Threats, and Future Research Directionscited by 265
  5. 2024The Emerged Security and Privacy of LLM Agent: A Survey with Case Studiescited by 131
  6. 2022Machine Learning Operations (MLOps): Overview, Definition, and Architecturecited by 128
  7. 2022Wild Patterns Reloaded: A Survey of Machine Learning Security against Training Data Poisoningcited by 94
  8. 2024RLHF Deciphered: A Critical Analysis of Reinforcement Learning from Human Feedback for LLMscited by 90
  9. 2024Exploiting LLM Quantizationcited by 65
  10. 2024BadMerging: Backdoor Attacks Against Model Mergingcited by 34
  11. 2023RLHFPoison: Reward Poisoning Attack for Reinforcement Learning with Human Feedback in Large Language Modelscited by 32
  12. 2024Large Language Model Supply Chain: A Research Agendacited by 23
13 more
  1. 2023The Pipeline for the Continuous Development of Artificial Intelligence Models -- Current State of Research and Practicecited by 14
  2. 2024Preference Poisoning Attacks on Reward Model Learningcited by 14
  3. 2024A Large-Scale Exploit Instrumentation Study of AI/ML Supply Chain Attacks in Hugging Face Modelscited by 14
  4. 2025MoEcho: Exploiting Side-Channel Attacks to Compromise User Privacy in Mixture-of-Experts LLMscited by 13
  5. 2023Analysis of Label-Flip Poisoning Attack on Machine Learning Based Malware Detectorcited by 11
  6. 2025An Empirical Analysis of Machine Learning Model and Dataset Documentation, Supply Chain, and Licensing Challenges on Hugging Facecited by 6
  7. 2026AIBoMGen: Generating an AI Bill of Materials for Secure, Transparent, and Compliant Model Trainingcited by 6
  8. 2024Lifting the Veil on Composition, Risks, and Mitigations of the Large Language Model Supply Chaincited by 4
  9. 2025Cost-Minimized Label-Flipping Poisoning Attack to LLM Alignmentcited by 4
  10. 2025Towards Secure MLOps: Surveying Attacks, Mitigation Strategies, and Research Challengescited by 3
  11. 2025MetaBreak: Jailbreaking Online LLM Services via Special Token Manipulationcited by 2
  12. 2025RE for AI in Practice: Managing Data Annotation Requirements for AI Autonomous Driving Systemscited by 1
  13. 2025Securing the AI Supply Chain: What Can We Learn From Developer-Reported Security Issues and Solutions of AI Projects?cited by 1

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