Ethical Hacking News
NVIDIA Triton Inference Server has been found to contain three critical security flaws that could allow unauthenticated attackers to execute code and hijack AI servers, posing a significant risk to organizations relying on this popular platform for their AI-driven operations.
The Triton Inference Server for Windows and Linux has three critical security flaws that could allow unauthenticated attackers to execute code and hijack AI servers. The first vulnerability (CVE-2025-23319) allows an attacker to cause an out-of-bounds write, leading to information disclosure, remote code execution, denial of service, data tampering, or system compromise. The second vulnerability (CVE-2025-23320) causes a shared memory limit to be exceeded, potentially leading to information disclosure and malicious activities. The third vulnerability (CVE-2025-23334) allows an out-of-bounds read, posing a significant risk even if less severe than the first two issues. These vulnerabilities can be combined to achieve full system compromise without requiring authentication credentials.
In a recent discovery that has sent shockwaves through the cybersecurity community, researchers at Wiz have uncovered a series of critical vulnerabilities in NVIDIA's popular Triton Inference Server for Windows and Linux. This open-source platform, designed for running artificial intelligence (AI) models at scale, has been found to contain three significant security flaws that could potentially allow unauthenticated attackers to execute code and hijack AI servers.
The first vulnerability, identified as CVE-2025-23319, affects the Python backend of the Triton Inference Server. According to Wiz researchers Ronen Shustin and Nir Ohfeld, this flaw allows an attacker to cause an out-of-bounds write by sending a malicious request, which could lead to information disclosure, remote code execution, denial of service, data tampering, or even complete system compromise.
The second vulnerability, CVE-2025-23320, also impacts the Python backend. This issue allows an attacker to cause the shared memory limit to be exceeded by sending a very large request, thereby potentially leading to information disclosure and other malicious activities.
The third vulnerability, CVE-2025-23334, affects the Python backend once again, this time allowing an attacker to cause an out-of-bounds read by sending a malicious request. While less severe than the first two issues, this flaw still poses a significant risk to organizations relying on Triton Inference Server for their AI-driven operations.
What makes these vulnerabilities particularly concerning is that they can be combined in various ways to achieve full system compromise without requiring any authentication credentials. According to Wiz researchers, a threat actor could exploit CVE-2025-23320 to leak the full, unique name of the backend's internal IPC shared memory region, thereby gaining access to sensitive information and then leveraging the remaining two flaws to execute malicious code and gain control over the inference server.
This poses a critical risk to organizations using Triton for AI/ML, as a successful attack could lead to the theft of valuable AI models, exposure of sensitive data, manipulation of the AI model's responses, and even a foothold for attackers to move deeper into a network. As such, it is essential that users take immediate action to apply the latest updates and ensure their servers are patched against these vulnerabilities.
It is worth noting that while there is currently no evidence to suggest that any of these vulnerabilities have been exploited in the wild, the potential risks are too great to ignore. NVIDIA's August bulletin for Triton Inference Server highlights fixes for three critical bugs (CVE-2025-23310, CVE-2025-23311, and CVE-2025-23317) that, if successfully exploited, could result in remote code execution, denial of service, information disclosure, and data tampering.
In light of this discovery, cybersecurity experts and organizations are advised to exercise extreme caution when using Triton Inference Server for AI-driven operations. By applying the latest updates and taking proactive measures to secure their servers, they can minimize the risk of a successful attack and protect their sensitive data.
As the threat landscape continues to evolve, it is essential that we prioritize the development of robust security measures and stay informed about emerging vulnerabilities like this one. By doing so, we can ensure the integrity of our digital assets and safeguard against potential threats.
Related Information:
https://www.ethicalhackingnews.com/articles/NVIDIA-Triton-Inference-Server-Vulnerabilities-Exposed-A-Threat-to-AI-Driven-Organizations-ehn.shtml
https://thehackernews.com/2025/08/nvidia-triton-bugs-let-unauthenticated.html
https://nvd.nist.gov/vuln/detail/CVE-2025-23319
https://www.cvedetails.com/cve/CVE-2025-23319/
https://nvd.nist.gov/vuln/detail/CVE-2025-23320
https://www.cvedetails.com/cve/CVE-2025-23320/
https://nvd.nist.gov/vuln/detail/CVE-2025-23334
https://www.cvedetails.com/cve/CVE-2025-23334/
https://nvd.nist.gov/vuln/detail/CVE-2025-23310
https://www.cvedetails.com/cve/CVE-2025-23310/
https://nvd.nist.gov/vuln/detail/CVE-2025-23311
https://www.cvedetails.com/cve/CVE-2025-23311/
https://nvd.nist.gov/vuln/detail/CVE-2025-23317
https://www.cvedetails.com/cve/CVE-2025-23317/
Published: Mon Aug 4 11:29:08 2025 by llama3.2 3B Q4_K_M
