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Critical Flaws in NVIDIA’s Triton Inference Server Pose Major Security Risks

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By Tom Grant
3 min read
Critical Flaws in NVIDIA’s Triton Inference Server Pose Major Security Risks

TL;DR

Newly discovered vulnerabilities in NVIDIA’s Triton Inference Server allow remote attackers to execute arbitrary code, posing significant risks to AI systems. Users are urged to update to the latest version immediately to mitigate these threats.

Critical Flaws in NVIDIA’s Triton Inference Server Enable Remote Takeover

Recent findings by the Wiz Research team have uncovered critical security flaws in NVIDIA’s Triton Inference Server for both Windows and Linux platforms. These vulnerabilities can be exploited by remote, unauthenticated attackers to fully compromise vulnerable servers, achieving remote code execution (RCE) and posing a severe threat to AI infrastructure.

Understanding Triton Inference Server

Triton Inference Server is an open-source inference serving software designed to streamline AI inferencing. It supports the deployment of AI models from various deep learning and machine learning frameworks, including TensorRT, TensorFlow, PyTorch, ONNX, OpenVINO, Python, RAPIDS FIL, and more.

Vulnerability Details

The Wiz Research team identified a chain of critical vulnerabilities in NVIDIA’s Triton Inference Server. When exploited together, these flaws could allow a remote, unauthenticated attacker to gain complete control of the server, enabling remote code execution (RCE) 1.

The attack sequence begins with a minor information leak in Triton’s Python backend, which can escalate to full system compromise. This chain of vulnerabilities, if exploited, could compromise AI models, data, and overall network security2.

NVIDIA has promptly addressed these vulnerabilities, which are tracked as CVE-2025-23319, CVE-2025-23320, and CVE-2025-23334. Users are strongly advised to update to version 25.07 to mitigate these risks.

Vulnerability Breakdown

  1. CVE-2025-23319 (CVSS score: 8.1)
    • Description: A vulnerability in the Python backend allows an attacker to cause an out-of-bounds write by sending a specially crafted request.
    • Impact: Successful exploitation could lead to remote code execution, denial of service, data tampering, or information disclosure.
  2. CVE-2025-23320 (CVSS score: 7.5)
    • Description: A vulnerability in the Python backend enables an attacker to exceed the shared memory limit by sending a very large request.
    • Impact: Successful exploitation could lead to information disclosure.
  3. CVE-2025-23334 (CVSS score: 5.9)
    • Description: A vulnerability in the Python backend allows an attacker to cause an out-of-bounds read by sending a specially crafted request.
    • Impact: Successful exploitation could lead to information disclosure.

Potential Consequences

Compromising an NVIDIA Triton Inference Server can have severe repercussions, including:

  • Theft of proprietary AI models
  • Exposure of sensitive data
  • Manipulation of AI outputs
  • Using the compromised server to penetrate deeper into the organization’s network

Expert Insights

Researchers emphasize the importance of defense-in-depth strategies, where security is considered at every layer of an application. As AI and machine learning deployments become more widespread, securing the underlying infrastructure is crucial.

“A verbose error message in a single component, a feature that can be misused in the main server were all it took to create a path to potential system compromise. As companies deploy AI and ML more widely, securing the underlying infrastructure is paramount. This discovery highlights the importance of defense-in-depth, where security is considered at every layer of an application.”

No Known Exploits

Currently, there are no reports of these vulnerabilities being exploited in the wild. However, users are strongly encouraged to update their systems to protect against potential future attacks.

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Conclusion

The discovery of these vulnerabilities underscores the importance of timely updates and robust security measures in protecting AI infrastructure. Organizations must remain vigilant and proactive in safeguarding their systems against emerging threats.

References

  1. Wiz Research Team (2025). “NVIDIA Triton Vulnerabilities”. Wiz. Retrieved 2025-08-05. ↩︎

  2. NVIDIA Docs (2025). “Triton Inference Server User Guide”. NVIDIA. Retrieved 2025-08-05. ↩︎

Cybersecurity & Data Protection, Vulnerabilities
cybersecurity threat-intelligence ai
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