CVE-2026-65101 in AIStore
Summary
by MITRE • 10/13/2026
NVIDIA AIStore for Linux contains a vulnerability in the distributed sort component where an attacker could cause server-side request forgery by supplying a crafted external key map URL. A successful exploit of this vulnerability might lead to information disclosure.
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Analysis
by VulDB Data Team • 10/13/2026
The NVIDIA AIStore platform, designed to accelerate large-scale data processing and machine learning workflows on Linux systems, contains a critical security flaw within its distributed sort component. This specific weakness arises from the system's handling of external key map URLs provided by users or applications interacting with the storage cluster. The vulnerability allows an attacker who can supply these crafted URLs to manipulate how the server processes requests for data mapping information. By injecting maliciously formatted Uniform Resource Locators, a threat actor can force the AIStore service to initiate connections to unintended destinations under its own identity and network privileges. This mechanism is characteristic of Server-Side Request Forgery, commonly abbreviated as SSRF, which represents a significant deviation from secure input validation practices in distributed storage architectures.
From a technical perspective, the flaw lies in insufficient sanitization or verification of the external key map URL parameter before it is processed by the sort engine. When AIStore receives this configuration data, it likely attempts to resolve and fetch content associated with the provided URL without adequately restricting the protocol schemes, hostnames, or internal network addresses that are permitted access. This lack of strict allow-listing enables an attacker to direct the server's outbound traffic toward sensitive internal resources such as metadata servers, database endpoints, cloud instance metadata services, or other microservices within the cluster topology. The distributed nature of AIStore amplifies this risk because a single compromised node can potentially be leveraged to probe and interact with multiple components across the entire storage fabric, effectively bypassing perimeter security controls that might otherwise protect these internal assets from direct external access.
The operational impact of exploiting this vulnerability centers primarily on information disclosure, though it may serve as an initial vector for more severe attacks such as remote code execution or denial of service depending on the surrounding infrastructure context. An attacker could extract sensitive configuration details, authentication tokens, or proprietary data stored within the AIStore cluster by inducing the server to retrieve and return contents from internal endpoints. In cloud environments, this often includes accessing instance metadata services that provide temporary credentials for other AWS, Azure, or GCP resources, potentially leading to a full compromise of the associated cloud account. Furthermore, because AIStore is frequently used in high-performance computing and deep learning pipelines where data integrity and confidentiality are paramount, such breaches can disrupt critical research initiatives and violate compliance requirements related to data protection regulations like GDPR or HIPAA if sensitive datasets are exposed.
This vulnerability aligns with CWE-918, which classifies Server-Side Request Forgery flaws resulting from improper neutralization of special elements in dynamically generated content or URLs used by a server-side application. Additionally, the exploitation technique maps directly to MITRE ATT&CK tactic T1504, specifically sub-technique T1504.003 for Web Session Hijacking if session tokens are intercepted, or more broadly to reconnaissance activities where internal network mapping is performed via SSRF probes. The ability to pivot from an external input vector into the trusted internal zone underscores a fundamental gap in zero-trust implementation within the application's request handling logic.
To mitigate this risk, immediate remediation should focus on implementing strict validation rules for all URL inputs accepted by the distributed sort component and other AIStore services that process external references. Developers must enforce an allow-list approach that restricts permitted protocols to only those strictly necessary for legitimate functionality, such as HTTP or HTTPS, while explicitly blocking dangerous schemes like file, gopher, or dict. Furthermore, network-level controls should be deployed to prevent the AIStore servers from initiating outbound connections to internal IP ranges unless absolutely required by business logic. Implementing egress filtering rules that deny traffic to private address spaces and cloud metadata endpoints provides a robust defense-in-depth layer. Regular security audits of input handling mechanisms and integration of static application security testing tools into the CI/CD pipeline can help identify similar SSRF patterns before deployment, ensuring the resilience of AI-driven data infrastructure against evolving threat vectors.