LLM Supply Chain 101: Stages, Stakeholders, and Threats

Last updated on September 19th, 2026 at 04:21 pm

This is one of the things that goes under-discussed: as soon as an AI model gives the wrong answer, spills data, or is abused to do something harmful, it is not typically an issue with the product or service. It is an issue cooked up much earlier, deep in the chain that most of us don’t touch.

The LLM supply chain is that chain. And it is longer, messier, and more exposed than you think. This failure mode charts all the stages of the process, including raw data scraping, up to the plugins in your coding tool, which are still sitting in your code.

This matters whether you are a developer delivering AI features, a security-conscious engineer, or someone who uses AI tools daily and wants to know what can go awry; the supply chain is.

What Is the LLM Supply Chain, Exactly?

It’s Not Just About the Model

However, the supply chain begins much earlier than any model is defined – and continues much later than one. You do not stop and examine what is on the plate. You check the farm, the processing plant, the delivery truck, the kitchen. Pollution at one link in that chain spoils the final product.

The LLM supply chain is no exception. It includes all individuals and tools, data sets, and processes, as well as third-party services, that interface with a language model from initial data collection to current real-time interaction with live plugs displayed to a user today. The most important steps, in sequence:

• Data Collection
• Training and Fine-Tuning
• Model Packaging and Distribution.
• Infrastructure deployment and infrastructure serving.
• Plugins, Extensions, and Integrations.

All of them have different stakeholders. Each has specific threats.

Stage 1 – Data Collection: Where It All Starts (and Often Goes Wrong)

The Problem With Scraping the Whole Internet

In that regard, training data for large language models is done on a scale that is literally difficult to imagine. Here, thousands and thousands of billions of tokens are pulled from websites, books, code repositories, forums, and PDFs, among others.

Stakeholders include data engineering workflows in AI laboratories, commercial data vendors, open dataset curators (such as the Common Crawl team or the Pile team), and, most recently, synthetic data generators.

The major threat here is data poisoning.

Data poisoning refers to a nefarious attacker intentionally injecting bad content into a dataset that is later used for training. It need not have access to the systems of the AI company, merely the capacity to post content that it gets scraped. A poisoned Wikipedia update, a backdoor-burdened code snippet in an open-source GitHub project, or an insidious manipulation of a widely crawled message board can all poison a model.

I have seen, when reading the research work of the OWASP LLM Top 10, that the issue of data poisoning is explicitly called LLM03 – one of the highest-priority hazards in the entire GenAI threat space.

The attack surface in this scenario is staggering because the data collection is highly automated, and the scale makes reviewing large amounts of data almost impossible. What exacerbates it: Foundation model developers, as a rule, do not publish detailed data cards. You are familiar with what has gotten in.

Stage 2 – Training and Fine-Tuning: The Hidden Backdoor Window

Base Models, You Didn’t Train Yourself

Few organizations train a model in isolation anymore. It is costly, time-consuming, and resource-intensive. Rather, the majority of teams begin with a ready-built base model, which can be either pulled from Hugging Face, a model hosted on the cloud, or available elsewhere in the open-source, and then fine-tune it on their own data.

That is efficient. It is also a security risk that LLM supply chain security often fails to address. Exactly as the name implies, backdoored base models are backdoored. An existing pre-trained model, one that is publicly available, can have concealed behavior that will only be triggered by particular circumstances – an input phrase, a distinct formatting rule. The standard evaluations of the base model appear clean.

The backdoor does not appear until after it was brought to production. I have tried several open-weight models on community hubs and found that the documentation on training data provenance and security audits is as patchy as mud. Other models have specified model cards including notes of responsible disclosure.

Others barely have anything. The MITRE ATLAS framework – a knowledge base of adversarial threats to AI systems in particular- records this type of attack under the umbrella of backdoors in pre-trained models and has been keeping a list of real-world instances of pre-trained model breakages. Even if you’re going to put something on top of open-weight base models, ATLAS is worth bookmarking.

Fine-Tuning Risks: Your Own Adapters

Type of adversaries: Malicious adapters – especially LoRA adapters and PEFT modules are shared across teams or pulled off public repositories and inoculated into otherwise safe base models.

However, this does not exclude the possibility that an adapter will also always bend behavior towards unintended directions to some extent – introducing biases, undermining safety guardrails, or opening up some unseen, goofy responses. The arxiv.org/html/2404.12736v1 research paper goes into great detail about how the AI/MF supply chain propagates risks of the conventional software supply chains – and why the adapter layer deserves particular attention.

Stage 3 – Model Packaging and Distribution

Who Signed This Model?

After a model has been trained or fine-tuned, it must be packaged and deployed – as an artifact to be downloaded, as a containerized service, or as an API endpoint. To a large extent, this step emulates traditional software supply chain security tactics, and much of the risk in software supply chain security is relevant.

Distortion in the process of distribution is a fact. Without the cryptographic signature and verification of a model artifact, there is no assurance that what you are downloading is actually the stuff that the original author authoritatively released. It can silently change a pristine model into an edited one, a man-in-the-middle at the registry level, a compromised CDN, or an unheroic mirror.

It is precisely this issue that tools such as Sigstore and Cosign were designed to solve in the software world – and there is active work to apply these signing and verification concepts to model artifacts. In the latest version (SLSA 1.2), a maturity framework is offered to build provenance and artifact integrity, which AI teams are starting to implement (Supply Chain Levels for Software Artifacts).

Another standard to monitor in this space is the CycloneDX ML-BOM standard, which is the machine learning extension of the software bill of materials format. It enables teams to record precisely what is in a model: coaching information origins, external elements, the subtlety of refining, and data vulnerable to the fragrance of risks. Think of it as an AI nutrition label. The OWASP CycloneDX spearheads this.

My experience showed that teams that used ML-BOMs during packaging detected third-party dependency problems that typical security audits would not have caught.

Stage 4 – Deployment and Serving Infrastructure

The Model Is Live. Now What Can Go Wrong?

The deployment is where the model is exposed to actual traffic – and where infra-level vulnerabilities are involved. The players have changed: DevOps teams, cloud providers, API gateway engineers, and platform security teams.

The types of risks at this stage are:

• Weak API: Poor model structuring or insecure API settings that allow immediate injection at scale.
• Depending on advisable values in the serving stack (think: PyTorch, TensorFlow, ONNX Runtime, all of which have CVEs)
• Weak access controls on model endpoints can allow unauthorized calls or model-extraction attacks.

The NIST AI Risk Management Framework (AI RMF 1.0) focuses heavily on risk governance during deployment. The NIST AI RMF Playbook offers hands-on controls on AI implementation by organizations on a large scale – all the way up to incident response.

This phase of AI Vulnerability Scanning generally includes automated scanning of the serving infrastructure and the model’s behavior, applying it to secure-code the serving infrastructure, and finding prompt injection vulnerabilities, information leakage, and output manipulation. Others, such as OpenSSF Scorecard, designed for open-source software repositories, are being extended to compute hygiene signals for AI model repositories.

It is particularly interesting that the coordinated argument of cybersecurity authorities in the US, UK, Canada, and Australia, which is issued by agencies such as CISA and the Canadian Center for Cyber Security, identifies deployment infrastructure as one of the most vulnerable links in the AI/ML supply chain.

Stage 5 – Plugins, Extensions, and Integrations: The Newest Threat Surface

I Tested This Layer. It’s Underprotected.

This is what worries me right now.

The implication of current LLM applications is not that models are deployed directly. They are attached to tools – web search extensions, code execution platforms, file browsers, database connectors, calendar extensions. Patterns: GPT-4 and Claude have slowed down because models and their associated usage are rapidly expanding the model ecosystem, and security reviews can’t keep pace.

Plugin breaches can lead to what OWASP LLM Top 10 labels as insecure plugin design and insecure supply chain. What is safe today can be modified tomorrow to include malicious code. If a plugin’s creator abandons it, someone with different intentions may purchase it.

The risk extends even to so-called coding copilots, such as GitHub Copilot, Cursor, and similar AI-assisted development environments. These tools consume code context, provide completions, and increasingly call external APIs. I observed during testing that many developers don’t look at what telemetry their coding assistant sends upstream or which external models drive those suggestions into production code.

Plug-in-based indirect prompts are a very subtle attack. A bad actor can insert code within a webpage, document, or data source that the AI tool reads, and the model then implements it as though the user entered the instructions. The model by itself may be spotless. The vulnerability occurs purely because of a poorly sandboxed plugin or integration.

A study by asiapacific.org outlines a chain of hidden risks in the LLM supply chain, with a particular focus on this plugin and integration layer.

Who Are the Stakeholders Across All These Stages?

A Quick Map

In this chain, all the stakeholders are possible weak links, but not necessarily with ill intent, but by neglect, under-investment, or absence of security best practices that are not standardized.

What’s Already Here vs. What’s Just Beginning

Mature Today

• SLSA framework for artifact provenance and integrity.
• OWASP Top 10 as a threat taxonomy base.
• NIST AI RMF: government and risk management.
• Sigstore/Cosign for signing (borrowed model) of the software supply chain.
• CycloneDX SBOM documentation of the model component.

Still Developing

• AI/ML system Predictive Vulnerability Analysis – With the use of historical CVE databases and architecture model indicators, predict the points at which vulnerabilities are apt to be created before an attacker starts exploiting them.
• LoRA security review practices and Adaptors are standardized.
• Vetting an automated Marketplace of plugins.
• Sharing standards of provenance of cross-organization models.

Specific regulatory frameworks to regulate the LLM supply chain (the current regulation of AI focuses on the output, not the machine that produces outputs). The difference between “not yet mature” and “still developing” lies in the gap between the most real risk occurring at present.

Two External Trust Boosters Worth Citing.

These are the official external sources that you may cite using the following recommended anchor texts:

1. LLM Applications OWASP Top 10. Recommended anchor text: OWASP supply chain vulnerability guidance. Apply it to discuss plug-in risks, training data poisoning, or third-party model risks.
2. Risk Management Framework of NIST AI. Proposed anchor text: NIST AI Risk Management Framework. Use it when discussing deployment governance, risk controls, or organizational responsibility.

Both are government/standards-body sources with high domain authority, are non-commercial, and focus directly on all the stages discussed in this article.

Final Take

The LLM supply chain is not a fictitious theory security scholars should worry about. It is the real way any AI tool executes on raw data to the interface that you are looking at – and this has real, publicly described vulnerabilities at every single step.

Data poisoning isn’t a theoretical concern. Backdoored base models pass standard evals. Bad adapters are similar to optimization. Hacked versions get updated automatically. Coding copilots work on context that no one audits.

The teams who construct and utilize AI tools by 2025 and beyond must take LLM Supply Chain Security with the strictness it merits in relation to traditional software security – and hopefully such a case will be made before an attack incident proves them right.

The frameworks exist. The threat taxonomy is overlaid. The last piece is unified adoption, which begins with a discussion of the pipeline.

Pranay Sai Aduvala

I’m a technology writer passionate about AI and digital marketing. I create engaging and useful content that bridges the gap between complex technology concepts and digital technologies. My writing makes the process easy and engaging. I encourage participation I continue to research innovation and technology. Let’s connect and talk technology!