Beyond the Black Box: Why Your AI Needs a Bill of Materials for Security
Artificial intelligence, particularly large language models (LLMs), is rapidly becoming the new backbone of modern business. From customer service bots to complex data analysis, these models offer unprecedented capabilities. But as we rush to integrate them into our critical systems, a crucial question often goes unanswered: what’s really inside these powerful tools?
Deploying an AI model without understanding its components is like using a piece of software from an unknown source. It’s a “black box” that could contain hidden vulnerabilities, biased data, or even malicious code. To navigate this new landscape safely, businesses must shift from blind trust to active verification.
The Hidden Dangers in the AI Supply Chain
Every AI model you integrate is part of your software supply chain, and each one carries potential risks that are often invisible. Unlike traditional software, where developers can inspect the code, the inner workings of a pre-trained AI model are far more opaque.
The primary risks include:
- Vulnerable Dependencies: Models are built using various libraries and frameworks. A vulnerability in one of these underlying components can be exploited to compromise the entire system.
- Data Poisoning: The model could have been trained on malicious or compromised data. This can be used to create hidden backdoors that allow an attacker to manipulate the model’s output on command.
- Intellectual Property and Licensing Risks: The training data used to build a model may contain copyrighted material or be subject to restrictive licenses, exposing your organization to legal and financial penalties.
- Inherent Bias: A model trained on biased data will produce biased and unfair results, leading to reputational damage and discriminatory outcomes.
Simply put, you cannot secure what you cannot see. Without a clear manifest of a model’s contents, you are inheriting an unknown level of risk.
The Solution: An AI Model Bill of Materials (MBOM)
To bring transparency and security to AI, the industry is moving toward the concept of a Model Bill of Materials (MBOM), sometimes called an AI Manifest. Similar to a Software Bill of Materials (SBOM) used in traditional cybersecurity, an MBOM is a formal record containing the details and components of an AI model.
A comprehensive MBOM provides a transparent look inside the black box, detailing crucial information such as:
- Training Data: Sources, licenses, and a summary of the data used to train the model.
- Model Architecture: The specific algorithms and structure of the model.
- Software Dependencies: A complete list of all libraries, frameworks, and other software used to build and run the model.
- Performance Metrics: Results from testing, including accuracy, and robustness evaluations.
- Known Vulnerabilities: Any identified security flaws or weaknesses in the model or its components.
- Ethical Assessment: Information on tests conducted for bias and fairness.
By demanding and analyzing an MBOM, organizations can finally vet their AI models with the same rigor they apply to any other piece of critical software.
Actionable Security: How to Vet Your AI Models
Implementing a strategy for AI model verification doesn’t have to be overwhelmingly complex. By focusing on a few key actions, you can dramatically improve your security posture and build a more trustworthy AI ecosystem.
Demand Transparency from Vendors. When acquiring a third-party model, make the MBOM a non-negotiable part of the procurement process. Ask for a detailed manifest of the model’s training data, dependencies, and known vulnerabilities. If a vendor can’t provide this, it’s a significant red flag.
Integrate Vetting into Your MLOps Pipeline. For models developed in-house, make the generation of an MBOM a standard step in your development lifecycle. Automated tools can scan your models and their components to create this manifest, ensuring you have a complete inventory of what you’re building.
Conduct Regular Vulnerability Scanning. The threat landscape is constantly changing. Use security tools to continuously scan your deployed models and their MBOMs for new vulnerabilities. This proactive approach allows you to patch weaknesses before they can be exploited.
Establish Clear Governance. Create internal policies that define the acceptable risk level for AI models. Your policy should mandate that no model is deployed into production without a thorough review of its MBOM.
The era of blindly trusting AI is over. As these systems become more integrated into our daily operations, treating them as impenetrable black boxes is a risk no organization can afford. By embracing the discipline of the Model Bill of Materials, we can move toward a future where AI is not only powerful but also transparent, secure, and worthy of our trust.
Source: https://www.helpnetsecurity.com/2025/08/05/manifest-cyber-ai-risk/
