CVE Allocation Crisis: Why AI Models Deserve Exclusion from Security Databases

Artificial intelligence systems are flooding CVE databases with vulnerabilities that don't apply to human-facing software—and security teams are paying the price.

The Growing AI Problem

Machine learning models generate thousands of potential vulnerability reports daily, overwhelming traditional CVE allocation systems. These AI-identified 'flaws' often represent mathematical quirks rather than actual security threats.

Resource Drain on Security Teams

Security analysts waste countless hours triaging AI-generated CVEs that pose zero risk to production systems. The noise-to-signal ratio has reached breaking point—like hiring a financial advisor who panics over every market fluctuation.

Technical vs Practical Vulnerabilities

Unlike software vulnerabilities that attackers can exploit, AI model weaknesses typically require specialized access and conditions that don't exist in real-world deployments. It's the cybersecurity equivalent of worrying about asteroid impacts while your house burns down.

The Path Forward

Security organizations must create separate tracking mechanisms for AI model issues—before the entire CVE system becomes another bloated bureaucracy that measures activity rather than actual security improvement.

The Common Vulnerabilities and Exposures (CVE) system, a globally recognized standard for identifying security flaws in software, is under scrutiny concerning its application to AI models. According to NVIDIA, the CVE system should primarily focus on frameworks and applications rather than individual AI models.

Understanding the CVE System

The CVE system, maintained by MITRE and supported by CISA, assigns unique identifiers and descriptions to vulnerabilities, facilitating clear communication among developers, vendors, and security professionals. However, as AI models become integral to enterprise systems, the question arises: should CVEs also cover AI models?

AI Models and Their Unique Challenges

AI models introduce failure modes such as adversarial prompts, poisoned training data, and data leakage. These resemble vulnerabilities but do not align with the CVE definition, which focuses on weaknesses violating confidentiality, integrity, or availability guarantees. Nvidia argues that the vulnerabilities typically reside in the frameworks and applications that utilize these models, not in the models themselves.

Categories of Proposed AI Model CVEs

Proposed CVEs for AI models generally fall into three categories:

AI Models and CVE Criteria

AI models, due to their probabilistic nature, exhibit behaviors that can be mistaken for vulnerabilities. However, these are often typical inference outcomes exploited in unsafe application contexts. For a CVE to be applicable, a model must fail its intended function in a way that breaches security, which is seldom the case.

The Role of Frameworks and Applications

Vulnerabilities often originate from the surrounding software environment rather than the model itself. For example, adversarial attacks manipulate inputs to produce misclassifications, a failure of the application to detect such queries, not the model. Similarly, issues like data leakage result from overfitting and require system-level mitigations.

When CVEs Might Apply to AI Models

One exception where CVEs could be relevant is when poisoned training data results in a backdoored model. In such cases, the model itself is compromised during training. However, even these scenarios might be better addressed through supply chain integrity measures.

Conclusion

Ultimately, NVIDIA advocates for applying CVEs to frameworks and applications where they can drive meaningful remediation. Enhancing supply chain assurance, access controls, and monitoring is crucial for AI security, rather than labeling every statistical anomaly in models as a vulnerability.

For further insights, you can visit the original source on NVIDIA's blog.

• cve
• ai models
• security
• nvidia