CVE-2025-23318 in Triton Inference Server
Summary
by MITRE • 08/06/2025
NVIDIA Triton Inference Server for Windows and Linux contains a vulnerability in the Python backend, where an attacker could cause an out-of-bounds write. A successful exploit of this vulnerability might lead to code execution, denial of service, data tampering, and information disclosure.
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Analysis
by VulDB Data Team • 08/06/2025
The vulnerability identified as CVE-2025-23318 affects NVIDIA Triton Inference Server across both Windows and Linux operating systems, specifically within its Python backend component. This critical flaw represents a significant security risk for organizations relying on machine learning inference workloads that utilize NVIDIA's inference server platform. The vulnerability manifests as an out-of-bounds write condition that can be exploited by malicious actors to compromise system integrity and potentially gain unauthorized access to sensitive data or system resources.
The technical nature of this vulnerability stems from improper input validation within the Python backend implementation of the Triton Inference Server. When processing certain inputs or configurations through the Python backend, the software fails to properly bounds-check array accesses or memory operations, leading to memory corruption that can be leveraged for arbitrary code execution. This type of vulnerability falls under CWE-787, which specifically addresses out-of-bounds write conditions that can result in memory corruption and system compromise. The flaw exists in the server's handling of Python-based model execution environments where user-provided inputs are processed without adequate validation mechanisms.
The operational impact of this vulnerability extends beyond simple denial of service conditions to encompass multiple serious security implications. Successful exploitation could enable attackers to execute malicious code with the privileges of the Triton Inference Server process, potentially allowing for complete system compromise. Additionally, the vulnerability may facilitate data tampering operations where adversaries could modify inference results or manipulate model outputs, undermining the integrity of machine learning workflows. Information disclosure capabilities arise from the potential for attackers to extract sensitive data from memory regions that become accessible through the out-of-bounds write condition, particularly affecting model parameters, configuration data, or inference results that contain proprietary information.
Organizations utilizing NVIDIA Triton Inference Server should prioritize immediate mitigation efforts including applying available patches from NVIDIA, implementing network segmentation to limit access to inference server endpoints, and monitoring for suspicious activities that might indicate exploitation attempts. The vulnerability's cross-platform nature means that both Windows and Linux deployments require identical remediation approaches. Security teams should also consider implementing runtime protection measures such as address space layout randomization and stack canaries to make exploitation more difficult. From an ATT&CK framework perspective, this vulnerability could be leveraged as part of a broader attack chain involving initial access through web application exploitation and subsequent privilege escalation to achieve persistent access to inference infrastructure. Organizations should also review their model deployment practices to ensure that only trusted inputs are processed through the Python backend and consider implementing additional input validation layers to reduce the attack surface.