The digital supply chain for LLMs and AI-powered tools has grown increasingly complex, connecting pre-trained models, adapters, plugins, and infrastructure across multiple vendors. While this interconnectivity drives efficiency, it also introduces new attack surfaces, leaving organizations vulnerable to supply chain attacks.
A recent example illustrates the emerging risks: a newly discovered vulnerability, CVE-2025-53773, in Microsoft Copilot and Visual Studio allows malicious source code to hijack AI coding assistants. Such vulnerabilities turn AI tools themselves into a supply chain attack vector, potentially spreading malicious behavior or code across multiple projects and teams
This blog explores LLM03:2025 Supply Chain risks and strategies to prevent or mitigate attacks, including AI-specific threats.
What is LLM03:2025 Supply Chain?
LLM03:2025 Supply Chain is one of the OWASP Top 10 risks for LLMs and generative AI applications. It refers to vulnerabilities arising from the complex network of third-party components used in building, training, fine-tuning, deploying, or maintaining LLMs. These components include:
- Pre-trained models from external sources
- Adapters like LoRA or PEFT for fine-tuning
- Third-party libraries, frameworks, or plugins
- Datasets and data sources used for training
- Infrastructure (cloud, APIs, storage, etc.)
Any weakness in these components can cascade into the LLM itself or its user applications.
Why Supply Chain Attacks Matter
Supply chain attacks can compromise LLMs even before deployment, causing:
- Backdoors or malicious behavior: Pre-trained models or adapters may be tampered with to execute malicious code under specific inputs.
- Bias or unexpected behavior: Poorly curated datasets can be manipulated, causing harmful outputs.
- Vulnerable or outdated dependencies: Outdated libraries or unpatched components are exploitable, similar to traditional software supply chain attacks.
- Licensing and legal issues: Unverified or improperly licensed components can lead to compliance problems.
- Lack of provenance and traceability: Without records of source and integrity, detecting tampering becomes difficult.
How Attacks Happen in LLM Supply Chains
Unlike traditional software, LLMs rely on pre-trained models, adapters, plugins, datasets, AI coding assistants, and cloud infrastructure, which expand the attack surface. Here is how attackers leverage these weaknesses:
1. Tampering with Models, Adapters, or Datasets Before Integration
Attackers may inject malicious code, hidden triggers, or biased behavior into models, adapters (like LoRA or PEFT), or datasets before they are integrated into production.
Example: A publicly available LoRA adapter may contain a hidden backdoor that activates when specific input prompts are provided, causing data leakage or harmful outputs.
Impact: This enables attackers to compromise AI behavior without interacting with the production environment directly, making detection difficult.
2. Exploiting AI-Powered Development Tools
AI coding assistants like GitHub Copilot,Cursor, Lovable, and Visual Studio introduce a new class of supply chain risk. Vulnerabilities in these tools can be exploited via prompt injection or specially crafted files (e.g., README.md, source code, or configuration files) to escalate local privileges or execute arbitrary commands.
Real-world Example: CVE-2025-53773 allowed attackers to inject instructions that automatically modified .vscode/settings.json, enabling “YOLO mode.” In this mode, Copilot executed system commands without user consent. The attack could propagate across repositories, creating wormable infections and compromising multiple developer environments.
Impact: This demonstrates that AI tools themselves can become vectors for supply chain attacks, automatically spreading malicious behavior across multiple projects.
3. Leveraging Outdated or Vulnerable Dependencies
LLMs often rely on third-party libraries, plugins, and cloud services for training, inference, or deployment. Attackers can exploit:
- Outdated libraries or adapters
- Misconfigured dependencies exposing sensitive endpoints
- Weak API or cloud configurations
Example: An LLM app using a cloud-hosted API with outdated libraries could allow attackers to extract sensitive model information or execute unauthorized commands.
Impact: Even a single weak component can compromise the entire AI supply chain, illustrating the cascading effect of dependencies
4. Injecting Malicious Behavior that Activates Under Specific Inputs
Some attacks are triggered only by certain inputs, making detection challenging. Attackers can craft queries, commands, or prompts that activate hidden backdoors in models, adapters, or AI tools.
Example: CVE-2025-53773 showed that malicious prompts could silently change VSCode settings and trigger system commands, without raising alerts.
Impact: Dormant attacks can remain undetected during testing, and automation features in AI tools can scale the attack across multiple teams and applications.
How to Prevent or Mitigate LLM03:2025 Supply Chain Risks
Preventing supply chain risks in LLMs requires a defense-in-depth approach, combining technical controls, governance, and continuous monitoring. Here is a detailed breakdown:
1. Model & Plugin Vetting
- Source verification: Only use models, adapters, or plugins from trusted and verified providers. Avoid downloading from unknown public repositories.
- Integrity checks: Validate the downloaded files using cryptographic hashes or digital signatures to ensure they have not been tampered with.
- Metadata inspection: Review model cards, documentation, or metadata for training source, performance metrics, biases, and licensing terms.
- Security review: Conduct code or behavior audits of any third-party components before integrating them into production.
2. Dependency Management
- Software Bill of Materials (SBOM): Maintain an up-to-date inventory of all libraries, adapters, and models used in the system.
- Version tracking: Track version numbers, patch history, and release notes for each component. Ensure outdated or vulnerable dependencies are upgraded promptly.
- Automated alerts: Use dependency scanning tools to detect known vulnerabilities in real-time.
3. Data Provenance & Integrity
- Detailed logging: Keep records of all datasets, training sources, and modifications for accountability and traceability.
- Tamper detection: Implement checksums, hashes, or cryptographic proofs to validate data integrity before model training or fine-tuning.
- Data review: Regularly inspect datasets for bias, malicious inputs, or poisoned samples that could compromise model behavior
4. Isolation and Testing
- Sandbox environments: Run all external models, adapters, and plugins in isolated development or staging environments before production deployment.
- Security testing: Conduct functional, adversarial, and penetration testing to detect hidden backdoors, prompt injection vulnerabilities, or unexpected behavior.
- Staged deployment: Only promote components to production after passing all security and behavioral tests.
5. Behavior Monitoring & Anomaly Detection
- Continuous observation: Monitor deployed models for abnormal outputs, unusual API calls, or unexpected behaviors.
- Adversarial triggers: Run controlled tests to detect hidden backdoors or malicious functionality, similar to the CVE-2025-53773 scenario in AI coding tools.
- Alerting: Set up automated alerts for unusual activity, especially actions that could compromise security or data integrity.
6. Access Control & Governance
- Least privilege: Only grant access to third-party models, datasets, or infrastructure to roles that need it.
- Review contracts & licensing: Ensure compliance with all licensing terms, terms of service, and legal obligations for external components.
- Approval workflows: Require multiple approvals for integrating new external components into production.
7. Version Transparency & Update Controls
- Version tracking: Maintain a record of every model, adapter, and plugin version deployed.
- Controlled updates: Avoid automatically pulling new versions without review; integrate updates only after testing in isolated environments.
Securing the AI Supply Chain
LLM03:2025 Supply Chain is a critical risk for AI and LLM applications, particularly as attackers exploit pre-trained models, adapters, and even AI coding tools. A robust strategy combining vetting, isolation, monitoring, and governance ensures secure supply chains, protecting both the model and its users.
