CVE-2026-47630 in Triton Inference Server
Summary
by MITRE • 08/18/2026
NVIDIA Triton Inference Server for Linux contains a vulnerability where an attacker could cause an absolute path traversal. A successful exploit might lead to code execution.
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Analysis
by VulDB Data Team • 08/18/2026
The NVIDIA Triton Inference Server, a widely adopted platform for serving deep learning models in production environments, is susceptible to an absolute path traversal vulnerability that poses significant risks to system integrity and confidentiality. This flaw arises from insufficient validation of file paths provided during model loading or configuration requests, allowing an attacker to manipulate input parameters to access files outside the intended directory structure. By crafting specific HTTP requests with carefully constructed relative or absolute path sequences, a malicious actor can bypass security restrictions designed to isolate inference workloads within designated sandboxed directories. This type of vulnerability is fundamentally categorized under CWE-22: Improper Limitation of a Pathname to a Restricted Directory, which highlights the failure to properly sanitize user-supplied input before using it in file system operations.
The operational impact of this vulnerability extends beyond simple data exfiltration and can escalate to remote code execution depending on the server's configuration and deployment context. If an attacker successfully traverses directories to access sensitive files such as private keys, environment variables, or other critical system configurations, they may gain insights that facilitate further exploitation. In scenarios where the inference server runs with elevated privileges or is integrated into a larger orchestration framework like Kubernetes without strict security contexts, the ability to read arbitrary files can serve as a stepping stone for more severe attacks. For instance, if sensitive credentials are exposed, an attacker could authenticate to internal services, leading to broader network compromise. Furthermore, in some implementations of path traversal vulnerabilities within web-serving applications, there may be associated risks related to CWE-434: Unrestricted Upload of File with Dangerous Type, although the primary vector here is reading rather than writing files.
From a threat intelligence perspective, this vulnerability aligns with techniques observed in advanced persistent threats and automated exploitation campaigns targeting machine learning infrastructure. The MITRE ATT&CK framework identifies such behaviors under T1083: File and Directory Discovery, where adversaries explore the target system to gather information for subsequent stages of an attack chain. In cloud-native environments, attackers often leverage these initial footholds to pivot laterally or escalate privileges by accessing service account tokens stored in well-known locations like /var/run/secrets/kubernetes.io/serviceaccount/. The lack of robust input validation in Triton’s path handling logic represents a critical gap that undermines the principle of least privilege essential for secure ML deployment.
Mitigation strategies must focus on both immediate remediation and long-term architectural hardening. NVIDIA has released patches addressing this issue, and administrators should prioritize updating to the latest stable version of the Inference Server as soon as possible. Beyond patching, organizations should enforce strict network segmentation to limit access to Triton endpoints only from trusted sources using firewalls or service mesh policies like Istio. Additionally, running the inference server within a restricted container runtime with read-only root filesystems and disabling unnecessary file system mounts can significantly reduce the attack surface. Implementing Web Application Firewalls (WAF) rules that detect common path traversal patterns such as ../ sequences or encoded variants can provide an additional layer of defense in depth. Regular security audits focusing on input validation practices across all API endpoints are also recommended to prevent similar vulnerabilities from being introduced during future development cycles.