A newly uncovered set of security vulnerabilities in NVIDIA’s Triton Inference Server, which is available for both Windows and Linux, poses significant risks to AI model deployments. These flaws could allow unauthenticated remote attackers to seize control of affected servers and execute arbitrary code.
Researchers from Wiz identified that when these vulnerabilities are exploited in tandem, they can grant complete control over the server to an attacker. The vulnerabilities include:
- CVE-2025-23319: (CVSS score: 8.1) This issue in the Python backend allows out-of-bounds writes through specially crafted requests.
- CVE-2025-23320: (CVSS score: 7.5) This flaw can lead to excessive shared memory usage, triggered by oversized requests.
- CVE-2025-23334: (CVSS score: 5.9) This vulnerability facilitates out-of-bounds reads via a similarly crafted request.
Exploiting these weaknesses could lead to data exposure, remote code execution, denial of service, or data corruption, particularly associated with CVE-2025-23319. All issues have been rectified in version 25.07 of the software.
The researchers outlined a specific attack scenario where an actor could misuse CVE-2025-23320 to reveal the unique name of the internal IPC shared memory region. Armed with this information, they could then utilize the other two vulnerabilities to achieve full control over the Triton server.
This presents a critical challenge for organizations relying on Triton for AI and machine learning tasks, as successful exploitation could steal proprietary AI models and sensitive data, manipulate AI output, or enable lateral movement within networks.
Additionally, NVIDIA recently issued a bulletin addressing three other critical vulnerabilities (CVE-2025-23310, CVE-2025-23311, and CVE-2025-23317) that could permit similar unauthorized actions if exploited. While no evidence supports that these vulnerabilities have been exploited in real-world scenarios, organizations are strongly encouraged to update to the latest version to better safeguard their systems.
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