Supply Chain Tampering in artificial intelligence systems represents a critical threat to organizations that develop or use AI models. Unauthorized modifications can be introduced at any stage of the lifecycle: from the data pipeline to the training process, from software dependencies to the containers and cloud environments used for deployment.

This article is part of the AI Infrastructure Testing chapter of the OWASP AI Testing Guide.

The consequences include malicious model behavior, performance degradation, unauthorized data access, or the compromise of the entire AI infrastructure. Protecting the integrity of the supply chain is essential to ensure security, reliability, and regulatory compliance.

Testing Objectives

An effective security test must pursue three main objectives:

• Identify vulnerabilities that allow unauthorized access or modifications to the AI supply chain
• Detect unauthorized alterations in the model lifecycle: training, deployment, and updates
• Ensure integrity and authenticity throughout the AI deployment pipeline

Methodology and Payloads

Dependency poisoning

Software dependencies represent one of the most common attack vectors. Automated analysis of project dependencies (files such as requirements.txt, package.json) using Software Composition Analysis (SCA) tools allows for the identification of compromised or outdated libraries.

Vulnerability indicator: dependencies with HIGH or CRITICAL level vulnerabilities signal the possibility of exploitation via third-party libraries.

Container and image manipulation

Docker images and containers used to distribute AI models may contain vulnerabilities in system packages or included libraries. Image scanning must be integrated into the deployment process.

Vulnerability indicator: critical vulnerabilities in system packages or image libraries, potentially exploitable at runtime to compromise the execution environment.

CI/CD pipeline tampering

The continuous integration and deployment pipeline is a prime target for attackers. Analysis must verify the configuration for misconfigurations: hardcoded secrets, insufficient access controls, or the use of resources from untrusted sources.

Vulnerability indicator: the pipeline may undergo unauthorized modifications, contain plaintext credentials, or use unsigned artifacts during the build process.

Expected Output

A secure AI infrastructure must implement automated controls that ensure:

• Automatic rejection of vulnerable dependencies: the CI/CD pipeline must automatically block builds if HIGH or CRITICAL vulnerabilities are detected in dependencies
• Container image integrity: all production images must be scanned, digitally signed, and verified before deployment. Deployment must be blocked in the presence of critical vulnerabilities
• Pipeline security: implementation of strict Role-Based Access Control (RBAC), prevention of unauthorized modifications, and maintenance of immutable audit logs for all build and deployment activities

Remediation Actions

Dependency management

Implement dependency management tools and integrate automated SCA scans into the CI/CD pipeline. Builds with known vulnerabilities must be blocked automatically. Maintain an updated inventory of all dependencies used.

Expected impact: reduced risk of exploitation via compromised libraries and greater visibility into dependencies used in production.

Container hardening

Use minimalist container images from trusted registries. Implement digital signing of images and verify signatures within the container runtime. Reduce the attack surface by eliminating unnecessary components.

Expected impact: protection of the execution environment from known vulnerabilities and prevention of the use of unauthorized images.

CI/CD pipeline security

Apply the principle of least privilege to all pipeline jobs and users. Store all secrets in dedicated secure vaults. Adopt immutable infrastructure and versioned build configurations to prevent unauthorized manipulation.

Expected impact: prevention of unauthorized pipeline modifications and protection of sensitive credentials from accidental exposure.

Software Bill of Materials (SBOM)

Automatically generate a Software Bill of Materials for every build, documenting all components and dependencies. The SBOM is essential for vulnerability management, regulatory compliance, and effective incident response.

Expected impact: complete traceability of components used and the ability to respond rapidly in the event of vulnerabilities discovered in dependencies.

Suggested Tools

• Trivy: vulnerability scanner for containers and dependencies
• Syft: automatic SBOM generation
• Grype: vulnerability scanner for container images and filesystems
• Snyk: security platform for dependencies and containers
• Cosign: container image signing and verification

Further Insights

ISGroup offers specialized services to protect AI supply chain integrity through our Secure Architecture Review service. Our team of experts deeply evaluates complex AI infrastructures, identifies supply chain vulnerabilities, and provides concrete recommendations to implement effective security controls.

The assessment covers the entire pipeline: from dependency management to container security, from CI/CD configuration to SBOM generation, ensuring that every component meets the highest security standards.

For complete protection of the software development cycle, the Software Assurance Lifecycle service integrates continuous security checks on releases, ensuring that every phase of the software lifecycle adheres to security best practices.

• What is Supply Chain Tampering in AI systems?
• AI Supply Chain Tampering consists of unauthorized modifications introduced at any stage of an AI model’s development or distribution cycle: from the data pipeline to training, from software dependencies to containers and cloud environments. These modifications can compromise the system’s security, reliability, and compliance.
• What are the main attack vectors in the AI supply chain?
• The main vectors include: dependency poisoning (compromised libraries), container and image manipulation (vulnerabilities in Docker images), and CI/CD pipeline tampering (unauthorized changes to the deployment pipeline). Each of these vectors can be exploited to introduce malicious behavior or access sensitive data.
• How do you protect the AI supply chain from vulnerabilities?
• Protection requires a multi-layered approach: automated dependency scanning with SCA tools, digital signing and verification of container images, implementation of strict RBAC on the CI/CD pipeline, automatic generation of SBOMs for every build, and immutable audit logs for all build and deployment activities.
• What is a Software Bill of Materials (SBOM) and why is it important?
• An SBOM is a comprehensive inventory of all components and dependencies used in a software build. It is essential for vulnerability management, regulatory compliance, and effective incident response, allowing for the rapid identification of which systems are affected by a vulnerability discovered in a dependency.
• What automated controls should an organization implement?
• Essential controls include: automatic blocking of builds with vulnerable dependencies (HIGH or CRITICAL), mandatory scanning and digital signing of container images before deployment, strict RBAC on all pipeline jobs, secure vaults for secret management, and versioned, immutable build configurations.
• How are unauthorized modifications in the CI/CD pipeline detected?
• Unauthorized modifications are detected through: immutable audit logs that track all activities, verification of artifact integrity via digital signatures, automated checks on pipeline configuration, monitoring of permission and access changes, and periodic configuration analysis to identify hardcoded secrets or resources from untrusted sources.

References

• OWASP GenAI – OWASP GenAI Project
• NIST – NIST AI 100-2e2025
• MITRE ATT&CK – Supply Chain Compromise Techniques
• SPDX – Software Package Data Exchange

Related Articles

• AI Data Testing: training data security
• Dev-Time Model Theft: protecting AI models
• Testing Poisoning Fine-tuning: validating model integrity

Integrating automated controls on dependencies, containers, and CI/CD pipelines helps prevent unauthorized modifications to the AI supply chain. Regularly testing supply chain integrity is fundamental to ensuring the security and reliability of AI systems in production.