Blog

Supply Chain Vulnerabilities in AI Systems

Author

Robinson Israel Uche

Posted: June 4, 2025 • 5 min Read

Cybersecurity

Supply Chain Vulnerabilities in AI Systems

As artificial intelligence (AI) and large language models (LLMs) become embedded in enterprise infrastructure, securing their supply chain is no longer optional, it’s mission-critical. Just as traditional software supply chains can be exploited via vulnerable packages or third-party dependencies, AI systems inherit a new, broader attack surface: pre-trained models, LoRA adapters, third-party prompts, and even community-trained datasets. The OWASP Top 10 for LLMs identifies LLM03:2025 – Supply Chain as a key vulnerability that enterprises must address to build resilient, trustworthy AI.

Understanding LLM03:2025

LLM03 covers a whole host of hidden dangers that can pop up whenever we build, train, or launch an AI model. For example, someone might grab a model component from an untrusted source without realizing it's out of date or even tampered with. Sometimes the rules around who can use certain pieces of software aren't clear, so people might accidentally break licensing agreements. If a model was trained on old or incomplete information, it can behave in unexpected or unsafe ways and when we don't keep a clear record of where every part of the model came from, it's almost impossible to spot if something bad slipped in. Attackers have started sneaking harmful “adapters” into existing models and even introducing malicious code through shared projects. These tricks aren't just theory, they're the same kind of attacks we see on common software libraries and container images. This is why it's so important to lock down every link in the AI supply chain, from start to finish.

Real-World Consequences

In 2023, researchers demonstrated data exfiltration via malicious LoRA adapters inserted into a public model hub. When AI systems operate on compromised models:

LLM03

Defensive Strategies

To mitigate LLM03-related risks, security leaders must adopt software supply chain principles and extend them into the AI lifecycle:.

  1. Model Vetting and Signature Verification
      2. Only use models from verified, reputable sources
      Verify model integrity via checksums and digital signatures
  2. Licensing and Legal Review
      3. Ensure reuse terms are clear
      Establish policies for open-source adoption in ML workflows
  3. Provenance & Transparency Requirements
      4. Maintain logs of dataset sources, contributors, and fine-tuning stages
      Adopt SBOM-like standards for models (e.g., ML-SBOM)
  4. Supply Chain Tooling
      5. Use platforms like Giskard, Robust Intelligence, or Hugging Face model cards
      Apply security scanning to datasets and model metadata
  5. Secure Collaboration Models
      6. Apply DevSecOps practices to ML pipelines (MLSecOps)
      Require peer review and testing for external contributions

LLM03 reminds us that the intelligence of a model is only as strong as its weakest contributor. In an AI-powered world, model integrity is business integrity.

Reference

OWASP. LLM03: Supply Chain - OWASP Top 10 for LLM Applications.https://genai.owasp.org/llmrisk/llm032025-supply-chain/

Related Content