Executive Summary

The Zero Day Initiative (ZDI) has disclosed a critical vulnerability in NVIDIA AIStore, a scalable object storage system tailored for AI workloads. The vulnerability, CVE-2025-33186, is an authentication bypass with a CVSS score of 9.8, stemming from the use of hard-coded credentials. This flaw allows a remote, unauthenticated attacker to gain full access to the AIStore platform, posing a severe risk to the sensitive data and machine learning models it manages. The disclosure highlights the critical need for robust security in the underlying infrastructure that powers modern AI development and deployment.

Vulnerability Details

ZDI published advisories for two vulnerabilities in NVIDIA AIStore on November 14, 2025.

• CVE-2025-33186 - NVIDIA AIStore Authentication Bypass Vulnerability

  • CVSS Score: 9.8 (Critical)
  • Description: This vulnerability exists within the AuthN (authentication) component of AIStore. The root cause is the presence of hard-coded credentials, which an attacker can use to bypass authentication checks.
  • Impact: A remote, unauthenticated attacker can gain unauthorized access, potentially leading to the theft or manipulation of sensitive AI training data, exfiltration of proprietary models, or complete system compromise.

• CVE-2025-33185 - NVIDIA AIStore Information Disclosure Vulnerability

  • CVSS Score: 7.5 (High)
  • Description: This flaw, also in the AuthN component, results from a missing authentication check for a critical function.
  • Impact: An attacker could leverage this vulnerability to access sensitive system information that could be used to facilitate further attacks.

Affected Systems

• Product: NVIDIA AIStore
• Component: AuthN (Authentication Service)

Administrators of NVIDIA AIStore deployments are urged to review the vendor's advisories and apply the necessary patches.

Exploitation Status

There is no indication of active exploitation in the wild at the time of disclosure. However, given the critical severity and the simplicity of exploiting hard-coded credentials, it is highly likely that threat actors will develop exploits for this vulnerability quickly.

Impact Assessment

The compromise of an AI storage platform like AIStore can have devastating consequences. Attackers could steal valuable intellectual property in the form of proprietary datasets and trained AI models. They could also conduct data poisoning attacks by manipulating the training data, subtly corrupting the behavior of AI models in a way that is difficult to detect. For organizations heavily invested in AI, such a breach could undermine the integrity of their entire AI/ML pipeline and erode customer trust.

Cyber Observables for Detection

• log_source: AIStore AuthN service logs.
• network_traffic_pattern: Monitor for access to AIStore management interfaces from untrusted or external IP addresses.
• api_endpoint: Look for anomalous API calls to administrative functions within AIStore that originate from unexpected sources.

Detection Methods

• Log Analysis: Review AIStore authentication logs for any successful logins that do not correspond to legitimate user activity. Any access using the hard-coded credentials should be treated as a critical incident. This is an application of D3FEND's Authentication Event Thresholding (D3-ANET).
• Network Monitoring: Implement network monitoring to baseline normal traffic to the AIStore cluster. Alert on any connections from outside the expected IP ranges, especially to administrative ports.
• Configuration Review: Proactively search for any instances of hard-coded credentials in configuration files or source code within your environment.

Remediation Steps

• Apply Patches: The most critical step is to apply the security patches provided by NVIDIA to address CVE-2025-33186 and CVE-2025-33185. This is a direct implementation of D3FEND's Software Update (D3-SU).
• Network Isolation: Restrict access to the AIStore management interface. It should not be exposed to the public internet and should only be accessible from a trusted management network. This aligns with D3FEND's Network Isolation (D3-NI).
• Credential Management: As a general best practice, eliminate all hard-coded credentials from applications and configurations. Use a secure vault or secrets management solution to handle service credentials, and enforce regular credential rotation. This is a form of Application Configuration Hardening (D3-ACH).