LLM03:2025 — Navigating Supply Chain Vulnerabilities in Large Language Model (LLM) Applications

LLM03:2025 — Navigating Supply Chain Vulnerabilities in Large Language Model (LLM) Applications

Executive Summary

As the adoption of Large Language Models (LLMs) accelerates across industries—from customer service to legal advisory, healthcare, and finance—supply chain integrity has emerged as a cornerstone for trustworthy, secure, and scalable AI deployment. Unlike traditional software development, the LLM supply chain encompasses training datasets, pre-trained models, fine-tuning techniques, and deployment infrastructures—all of which are susceptible to unique attack vectors.

This post provides an exhaustive and practical analysis of LLM03:2025 – Supply Chain Risks from the OWASP Top 10 for LLM Applications v2.0. It highlights the business impact, ROI implications, and strategic risk mitigation frameworks relevant for decision-makers and technical leads. We explore threats, real-world examples, and best practices, with a specific focus on third-party model dependencies, data poisoning, tampered checkpoints, and insecure deployment platforms.

1. Understanding the LLM Supply Chain

1.1 The Evolving LLM Landscape

The lifecycle of an LLM today involves multiple external touchpoints:

• Third-party training datasets (often scraped from the web)
• Pre-trained base models (e.g., from Hugging Face, GitHub, or internal repositories)
• Fine-tuning adapters using techniques like LoRA and PEFT
• Packaging & deployment via containers, APIs, or edge devices

Each layer introduces an opportunity for malicious interference, resulting in compromised AI behaviours—ranging from biased outputs to catastrophic system failures.

1.2 From Classic Code Flaws to AI-Specific Risks

Traditional software supply chains focus on:

• Dependency management
• Code signing
• Vulnerability patching

In contrast, LLM supply chains add new dimensions:

• Data provenance and verifiability
• Model weight integrity
• Fine-tuning fidelity
• Inferencing security across devices

These factors amplify attack surfaces and require tailored governance models.

2. Key Vulnerabilities in LLM Supply Chains

2.1 Data Poisoning

Definition: The injection of malicious or biased examples into training or fine-tuning datasets.

Business Impact:

• Legal liability from discriminatory or harmful outputs
• Loss of stakeholder trust
• Reputational damage impacting brand equity

Example: A fintech startup unknowingly incorporated Reddit-sourced financial data poisoned to introduce misleading credit advice into its LLM model—leading to a class action suit.

2.2 Model Tampering

Definition: Altering the internal weights or layers of a model during transit or within compromised repositories.

Vectors:

• Compromised .bin or .safetensors files
• Malicious pull requests
• Checkpoint hijacking

Result: Trojanised models that leak data, trigger payloads, or silently bias outputs.

Technical Tip: Hash-based verification and secure storage protocols (e.g., SHA-256 + immutability on IPFS) help mitigate this threat.

2.3 Third-party Model Risks

Most fine-tuned LLMs today use pre-trained base models like:

• LLaMA
• Falcon
• Mistral
• Open-source BERT variants

Problem: These models may come without any security attestations or lineage verification.

Business Risk:

• IP infringement claims
• Regulatory non-compliance (e.g., GDPR, DSA)
• Loss of control over model capabilities

2.4 LoRA and PEFT Adapter Vulnerabilities

Context: LoRA and PEFT allow quick domain adaptation without re-training full models.

Supply Chain Threats:

• Malicious adapters can introduce stealthy backdoors.
• Attackers upload tampered adapters under the guise of community contributions.

Example: In late 2024, a healthcare chatbot using LoRA adapters from Hugging Face began suggesting fatal dosage advice—traced back to a manipulated adapter file.

3. Why C-Level Executives Must Care

3.1 ROI Considerations

Deploying LLMs in enterprise environments without due diligence on the supply chain:

• Increases Total Cost of Ownership due to incident responses and reputational clean-ups.
• Reduces Time-to-Value as internal teams need to triage unanticipated failures.
• Creates regulatory exposure—costly audits, fines, and public scrutiny.

3.2 Business Continuity

Supply chain risks aren’t just technical concerns—they can cripple mission-critical workflows in banking, logistics, legal tech, and customer care.

4. Real-World Case Studies

4.1 The Hugging Face “Fake Adapter” Incident (2024)

A rogue actor uploaded LoRA weights with obfuscated code embedded inside the configuration files. The result? Several downstream applications began leaking API keys and user credentials.

4.2 The “GPT-J Poisoning” Campaign

A data poisoning effort that targeted open datasets like The Pile with highly politicised content. Several LLMs inherited this bias, making them unsuitable for neutral advisory roles in legal and finance sectors.

5. Risk Mitigation Strategies

5.1 Establish a Trusted Model Registry

Maintain an internal, curated repository of approved models and adapters. Use digital signatures, hash verification, and version control to track changes.

Executive Insight: Treat LLMs like software artefacts—subject them to the same rigor as CI/CD pipelines.

5.2 Vet All Third-Party Contributions

Implement:

• Static analysis on weights and configurations
• Code audits for all adapters and custom layers
• Automated scanning tools (e.g., OSS Index, Trivy)

5.3 Data Governance for Fine-Tuning

Use documented and versioned datasets. Consider:

• Differential privacy for PII-containing corpora
• Manual review layers for high-stakes domains like medicine or law

5.4 Monitor On-device Deployments

On-device LLMs, such as those on smartphones, wearables, and edge routers, are harder to secure due to physical exposure and fragmentation.

Best practices:

• Disable external model loads by default
• Employ secure boot and attestation mechanisms
• Enable remote audit logs

6. Proactive Defence with AI Red-Teaming

Set up internal red teams for LLM security—specialised in:

• Prompt injection detection
• Model inversion tests
• Poisoning attack simulations

This is particularly crucial for C-suites deploying LLMs in regulated verticals.

7. Compliance and Regulatory Readiness

7.1 GDPR, DSA, and LLMs

If a model’s data lineage isn’t trackable, you can’t honour deletion requests, nor ensure fairness—two fundamental GDPR principles.

Also, under the EU Digital Services Act (DSA) and AI Act, organisations are required to:

• Conduct risk assessments
• Document dataset sourcing
• Explain model decisions

Supply chain negligence = regulatory exposure.

8. Strategic Recommendations for C-Level Leaders

9. The Future: Secure, Transparent, Responsible AI

Supply chain risks in LLMs are a contemporary Achilles’ heel. Left unchecked, they can collapse entire AI initiatives. However, with the right blend of governance, tooling, and executive alignment, organisations can transform these vulnerabilities into a strategic advantage.

10. Common Examples of Supply Chain Risks in LLM Applications

Understanding specific, real-world supply chain risks is essential for both technical and executive stakeholders. Below we explore some of the most pervasive examples, their technical mechanisms, and how they pose threats to your organisation’s security, reputation, and bottom line.

10.1 Traditional Third-party Package Vulnerabilities

Overview

This class of vulnerabilities arises from outdated or deprecated components, such as Python packages, NodeJS libraries, or compiled binaries used during model training, serving, or fine-tuning. These vulnerabilities mirror the OWASP Top 10 Web App Risk “A06:2021 – Vulnerable and Outdated Components”, but they manifest with heightened consequences in the LLM lifecycle due to the complexity and breadth of dependencies in machine learning pipelines.

Why It Matters in LLMs

Unlike typical software stacks, LLM applications often involve:

• Heavily layered dependency trees (e.g., Transformers → Tokenizers → NumPy)
• Use of abandoned open-source repositories during model prototyping
• Reliance on automated pipelines (e.g., Hugging Face Spaces or GitHub Actions) that may not vet dependencies

Example:

In 2023, a CVE emerged in an outdated version of the sentencepiece library, used by many tokenisers. Threat actors were able to leverage this to execute arbitrary code during tokenisation. Several downstream LLM applications were unknowingly compromised.

Business Impact:

• Attackers gaining remote access to inference servers or development machines
• Supply chain risk snowballing into data exfiltration or model theft
• Loss of customer trust and compliance certification failures

Mitigation Strategies:

• Use Software Composition Analysis (SCA) tools (e.g., Snyk, OWASP Dependency-Check)
• Enforce strict version pinning in all environments
• Maintain internal vulnerability feeds specific to ML ecosystem libraries
• Prefer well-maintained, community-verified libraries over obscure dependencies

🎯 C-Suite Tip: Allocate budget for a dedicated ML security role or consultant to audit training and deployment stacks. Think of it as supply chain insurance for AI.

10.2 Licensing Risks

Overview

AI development is a complex ecosystem where code, models, datasets, and even fine-tuning adapters are stitched together, often from various open-source and proprietary sources. While this accelerates innovation, it introduces licensing mismatches and compliance liabilities. A single misstep in adhering to the terms of a dataset or model licence could expose your organisation to legal claims, reputational damage, or forced product recalls.

Why It Matters in LLMs

Unlike traditional software development, LLM pipelines consume vast amounts of externally sourced data and model weights—each with their own licensing regime. These may include:

• Permissive licences (e.g., Apache 2.0, MIT)
• Copyleft licences (e.g., GPLv3)
• Restrictive licences (e.g., non-commercial, academic-use-only)
• Creative Commons licences (e.g., CC-BY-NC)
• Custom terms from providers like Hugging Face, EleutherAI, or Stability AI

The problem arises when organisations, especially under agile or startup pressure, overlook, misinterpret, or ignore these licensing conditions.

Case Example: The Dataset Dilemma

In 2022, a fintech startup incorporated a Hugging Face dataset labelled with a CC-BY-NC 4.0 (non-commercial) licence to fine-tune their customer support LLM. The model performed excellently, and they scaled its use across their commercial SaaS offerings. Months later, the original dataset creator issued a takedown notice, arguing unauthorised commercial use.

Outcome:

• The firm was forced to withdraw the LLM service temporarily
• Faced reputational fallout and renegotiation of commercial contracts
• Legal costs exceeded £80,000

Implications for C-Suite Executives

Prompt Engineer Pitfalls

• Copying datasets from academic forums with unclear licences
• Using LoRA adapters trained on restricted corpora
• Fine-tuning open LLMs (e.g., LLaMA, Falcon) on data with non-commercial clauses, then deploying them in monetised products

Best Practices for Risk Mitigation

1. Conduct Licence Audits
   
   Perform regular audits of all datasets, models, scripts, and pre-trained components. Maintain a Machine Learning Bill of Materials (ML-BoM) similar to SBOMs in software security.
   
2. Classify Assets Based on Licence Types
   
   Group your models and data into safe for commercial use, requires attribution, restricted, and prohibited. This makes pipeline governance easier.
   
3. Legal and ML Collaboration
   
   Foster cross-functional workflows between the ML team, Legal counsel, and Compliance units.
   
4. Automated Licence Detection Tools
   
   Integrate tools such as:
   
   • scancode-toolkit
   • FOSSA
   • Hugging Face Licence Classifier (where available)
5. Consider “Clean Room” Approaches
   
   Especially for fine-tuning large LLMs intended for commercial deployment. This involves using internally curated, licence-cleared datasets.
   

💼 C-Suite Strategy Insight:

Treat licences not as afterthoughts, but as strategic enablers or blockers of product viability. Establish an internal AI Licence Council to review and approve high-risk integrations across product lines.

10.3 Outdated or Deprecated Models

Overview

Just like legacy software, machine learning models—especially LLMs—can become outdated. When models are no longer actively maintained, patched, or improved by their creators, they begin to accrue technical debt and security risk. Organisations that continue to rely on these deprecated models put themselves at significant risk of exploitation, poor performance, and regulatory non-compliance.

Why It Matters in LLMs

The rate at which the AI space is evolving is unprecedented. Open-access LLMs (e.g., GPT-2, BERT, LLaMA-1) that were once state-of-the-art are now superseded by newer, more secure, and more capable versions. Yet many organisations still rely on these legacy models due to:

• Prior investments in fine-tuning
• In-house deployment infrastructure tailored to old architectures
• Lack of awareness of known issues or patches
• Fear of migration cost or performance unpredictability

Unfortunately, this reluctance often invites attack vectors.

Example: BERT and the Risk of Abandonment

BERT (Bidirectional Encoder Representations from Transformers) revolutionised NLP in 2018. However, by 2024, several community-maintained variants of BERT were flagged for known vulnerabilities such as:

• Token collision bugs
• Hidden prompt injections through special tokens
• Sub-optimal performance on adversarial queries

Some versions were removed from Hugging Face altogether due to misuse in misinformation bots. Yet, many enterprise products still embed these outdated BERT versions, especially in customer support and search systems.

Security Concerns

Business Impact

1. Reputational Risk: Outdated models may respond inaccurately, offensively, or misleadingly.
2. Compliance Failure: Models trained on untraceable or outdated datasets may violate GDPR, AI Act, or industry-specific standards.
3. Increased Attack Surface: Legacy model components are often easier to compromise.
4. Hidden Opportunity Costs: Continuing to support old models diverts resources from innovation and scalability.

🔍 C-Suite Watchpoint: Just because a model “still works” does not mean it is secure, ethical, or performant. In the AI space, stability ≠ safety.

Mitigation Strategies

1. Regular Model Lifecycle Audits

Establish an internal protocol to review model health every 6–12 months. Tag each model as:

• Active
• Deprecated (phasing out)
• Retired

Track this in an internal AI Asset Registry.

2. Automated Model Scanning

Use tools like:

• Hugging Face’s Safety Scanner
• MLSecCheck or Microsoft’s Counterfit
• Open-source model vetting scripts for detecting trigger phrases or suspicious weights

3. Transition to Maintained Alternatives

Replace older models with actively maintained successors. For example:

• From BERT → RoBERTa, DeBERTa, or DistilBERT
• From GPT-2 → GPT-J or Falcon

Ensure these successors are fully vetted for licensing, security, and ethical constraints.

4. LoRA and PEFT-aware Migration

For organisations using LoRA or PEFT (Parameter-Efficient Fine-Tuning) on old models: re-train adapters on newer base models to reduce retraining cost while benefiting from updated architectures.

Strategic ROI Angle for Executives

💡 Executive Insight:

By phasing out deprecated models and aligning AI usage with current best practices, your organisation demonstrates a proactive stance on security, innovation, and compliance—a critical message for investors, partners, and regulators alike.

11. Final Thoughts

Prompt engineers must take the lead in technical hardening of the supply chain, while C-Suite leaders must create a framework of accountability, investment, and policy. LLMs are here to stay—let’s ensure they’re built on secure, trusted foundations.

Secure your Risk Now!

Have you conducted a supply chain audit of your LLM deployments? Let’s talk about frameworks, tooling, and advisory strategies for enterprise-ready AI.