Introduction
Remote attackers can gain shell access to enterprise AI infrastructure by exploiting a single HTTP request flaw in NVIDIA Triton Inference Server. This vulnerability, tracked as CVE-2025-23317, enables unauthenticated remote code execution and has a CVSS score of 9.1, making it a critical risk for organizations deploying AI models at scale.
About NVIDIA Triton Inference Server: NVIDIA Triton Inference Server is a widely adopted open-source platform that streamlines the deployment of AI models in production. Used by major enterprises and cloud providers, Triton supports multi-framework inference and is integral to modern AI infrastructure. Its broad adoption means vulnerabilities can have significant industry-wide impact.
Technical Information
CVE-2025-23317 is rooted in the HTTP server component of NVIDIA Triton Inference Server. The vulnerability arises from insufficient input validation and improper boundary checking during HTTP request parsing. Specifically, the server fails to safely handle certain HTTP requests, including those using chunked transfer encoding or containing oversized or malformed data structures.
Attackers can exploit this by sending a specially crafted HTTP request to an exposed Triton endpoint. The vulnerable code path processes the request, leading to a buffer overflow or memory corruption. This condition enables arbitrary code execution within the context of the Triton server process. Successful exploitation allows the attacker to spawn a reverse shell, granting remote command-line access to the underlying system without requiring authentication.
Key technical characteristics:
- Vulnerability is triggered by malformed HTTP requests targeting inference or administrative endpoints
- Exploitation does not require prior authentication or valid credentials
- The flaw is linked to unsafe memory allocation and lack of bounds checking in the HTTP server’s request handling logic
- Attackers can use standard HTTP client tools to deliver the exploit payload
This vulnerability is particularly severe because Triton servers are often deployed with elevated privileges to access GPU resources, increasing the potential impact of a compromise.
Patch Information
NVIDIA has released a security update for the Triton Inference Server to address several critical vulnerabilities that could allow remote code execution, denial of service, information disclosure, and data tampering. Users are strongly advised to upgrade to version 25.07 to mitigate these risks.
The update specifically addresses vulnerabilities in the Python backend and HTTP server components. By updating to version 25.07, these security flaws are effectively patched, enhancing the overall security of the Triton Inference Server.
To apply the patch, download the latest release from the Triton Inference Server Releases page on GitHub. For detailed guidance on secure deployment practices, refer to the Secure Deployment Considerations Guide.
By promptly updating to version 25.07 and following the recommended deployment guidelines, users can safeguard their systems against potential exploits targeting these vulnerabilities.
Reference: NVIDIA Security Advisory
Affected Systems and Versions
- Product: NVIDIA Triton Inference Server
- Affected versions: All versions prior to 25.07
- Platforms: Both Linux and Windows deployments are affected
- Vulnerable components: HTTP server and Python backend
- Typical vulnerable configurations: Any deployment exposing the HTTP server to network traffic, especially those without strict access controls
Vendor Security History
NVIDIA has a history of coordinated vulnerability disclosure and timely patch releases for its AI and GPU infrastructure products. Multiple critical vulnerabilities have been identified in Triton Inference Server in 2025, including CVE-2025-23319, CVE-2025-23320, and CVE-2025-23334. These findings highlight the need for improved secure development practices, particularly around memory safety and input validation. NVIDIA’s response to recent disclosures has been prompt, with patches released within industry-standard timelines.