CVE-2026-47627 in Triton Inference Server
Summary
by MITRE • 08/18/2026
NVIDIA Triton Inference Server for Linux contains a vulnerability where an attacker could cause path traversal. A successful exploit might lead to denial of service.
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Analysis
by VulDB Data Team • 08/18/2026
The NVIDIA Triton Inference Server, a widely adopted open-source solution for serving deep learning models in production environments, is susceptible to a path traversal vulnerability that poses significant risks to system integrity and availability. This security flaw arises from insufficient validation or sanitization of user-supplied input within the server's request handling mechanisms. Specifically, when processing inference requests or model configuration inputs, the application fails to adequately restrict file paths to their intended directories. An attacker can craft malicious payloads containing directory traversal sequences, such as dot-dot-slash characters, which are not properly neutralized before being processed by the underlying operating system. This oversight allows the server to access files and directories outside of its designated root or model repository path, violating the principle of least privilege and expected isolation boundaries inherent in secure software design.
From a technical perspective, this vulnerability aligns with CWE-22: Improper Limitation of a Pathname to a Restricted Directory, which is categorized under broader classes of input validation failures. The core issue lies in the application's inability to distinguish between legitimate relative paths and maliciously constructed absolute or parent-directory references. In many web-serving architectures, including those based on Triton, path resolution logic must rigorously canonicalize inputs to prevent escape from sandboxed environments. When this normalization process is flawed or bypassed through encoded characters or nested traversal sequences, the server may inadvertently read sensitive configuration files, system binaries, or other critical data stored in accessible locations on the host filesystem. This lack of strict boundary enforcement represents a fundamental weakness in how external resources are referenced and loaded during runtime operations.
The operational impact of exploiting this path traversal vulnerability is primarily centered around denial of service conditions, although potential information disclosure cannot be entirely ruled out depending on file permissions and server configuration. By manipulating paths to point to non-existent locations or causing the system to attempt access to restricted areas, an attacker can trigger exceptions, crashes, or resource exhaustion within the inference engine. Since Triton serves real-time AI workloads for many enterprise applications, any disruption directly affects service availability and latency guarantees. A successful exploit could lead to server instability, requiring restarts that interrupt ongoing inference tasks and degrade user experience. In worst-case scenarios where sensitive files are exposed due to overly permissive access controls following the traversal, confidentiality breaches may also occur, compromising proprietary model weights or internal infrastructure details.
Mitigation strategies should focus on both immediate remediation and long-term architectural hardening. NVIDIA has released patches that address this specific flaw by enhancing input validation routines within the Triton codebase; therefore, updating to the latest secure version is the primary corrective action. Administrators must ensure their deployment pipelines automatically integrate these updates to maintain a strong security posture. Additionally, implementing defense-in-depth measures such as running the inference server with minimal required file system permissions can limit the damage even if traversal occurs. Using containerization or sandboxing technologies like Linux namespaces and cgroups further isolates the application from the host OS, preventing unauthorized access regardless of path manipulation attempts. Regular auditing of input handling logic against standards like CWE-20: Improper Input Validation is also recommended to identify similar weaknesses in custom extensions or integrations built upon the Triton framework.