CVE-2025-23310 in Triton Inference Server
Summary
by MITRE • 08/06/2025
NVIDIA Triton Inference Server for Windows and Linux contains a vulnerability where an attacker could cause stack buffer overflow by specially crafted inputs. A successful exploit of this vulnerability might lead to remote code execution, denial of service, information disclosure, and data tampering.
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Analysis
by VulDB Data Team • 08/06/2025
The vulnerability identified as CVE-2025-23310 affects NVIDIA Triton Inference Server across both Windows and Linux operating systems, representing a critical security flaw that could be exploited by malicious actors to compromise system integrity. This vulnerability resides within the server's handling of input data, specifically when processing specially crafted inputs that trigger a stack buffer overflow condition. The affected software serves as a machine learning inference serving platform that enables organizations to deploy and manage AI models in production environments, making it a prime target for attackers seeking to exploit weaknesses in AI infrastructure.
The technical nature of this vulnerability stems from improper input validation within the Triton Inference Server's processing pipeline, where insufficient bounds checking allows maliciously constructed data to overwrite adjacent memory locations on the stack. This type of buffer overflow represents a classic software security flaw that can be categorized under CWE-121, which specifically addresses stack-based buffer overflow conditions. The vulnerability's exploitation potential is amplified by the fact that it can be triggered remotely, meaning attackers do not require local system access to initiate the attack. When successfully exploited, the stack buffer overflow could allow an attacker to execute arbitrary code with the privileges of the affected service, potentially leading to complete system compromise. The remote code execution capability places this vulnerability in the ATT&CK matrix under T1059.007 for command and scripting interpreter and T1105 for remote file execution, indicating how attackers could leverage this flaw to establish persistent access and expand their foothold within the target environment.
The operational impact of CVE-2025-23310 extends beyond simple system compromise, as the vulnerability could result in denial of service conditions that disrupt AI inference operations critical to business processes. Organizations relying on Triton Inference Server for production machine learning workloads face potential data tampering risks where model outputs could be manipulated to produce incorrect results, undermining the integrity of AI-driven decision making. Information disclosure represents another serious consequence, as attackers could potentially extract sensitive data from memory locations overwritten during the buffer overflow attack. The vulnerability's cross-platform nature means that organizations running the same software on both Windows and Linux systems face identical risks, requiring comprehensive remediation across all affected environments. The potential for cascading failures exists when organizations use Triton Inference Server as part of larger AI ecosystems, where compromise of a single inference server could impact multiple dependent services and applications.
Mitigation strategies for CVE-2025-23310 should prioritize immediate patching of affected systems, as NVIDIA has likely released security updates addressing this specific vulnerability. Organizations should implement network segmentation to limit access to Triton Inference Server instances and employ principle of least privilege controls to restrict who can submit inputs to the inference service. Input validation should be strengthened at multiple layers, including application-level checks and network-level filtering to prevent malformed data from reaching the vulnerable processing components. Monitoring and logging mechanisms should be enhanced to detect unusual patterns of input submission that might indicate exploitation attempts. Security teams should also consider implementing intrusion detection systems that can identify potential buffer overflow patterns in network traffic. Regular security assessments of AI infrastructure components should be conducted to identify similar vulnerabilities that might exist in other software components within the machine learning pipeline. Organizations using Triton Inference Server should also review their incident response procedures to ensure readiness for potential exploitation of this vulnerability, as the remote execution capability makes it particularly dangerous in production environments where AI services are critical to business operations.