CVE-2025-23310
published 2025-08-06CVE-2025-23310: NVIDIA Triton Inference Server for Windows and Linux contains a vulnerability where an attacker could cause stack buffer overflow by specially crafted inputs…
PriorityP356critical9.8CVSS 3.1
AVNACLPRNUINSUCHIHAH
EPSS
1.81%
76.1th percentile
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.
Affected
2 ranges
| Vendor | Product | Version range | Fixed in |
|---|---|---|---|
| nvidia | triton_inference_server | < 25.07 | 25.07 |
| nvidia | triton_inference_server | — | — |
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Trailofbits
Uncovering memory corruption in NVIDIA Triton (as a new hire)
blogs_trailofbits·2025-08-05·CVSS 9.8
[CRITICAL] Uncovering memory corruption in NVIDIA Triton (as a new hire)
In my first month at Trail of Bits as an AI/ML security engineer, I found two remotely accessible memory corruption bugs in NVIDIA’s Triton Inference Server during a routine onboarding practice. The bugs result from the way HTTP requests are handled by a number of the API routes, including the inference endpoint.
Like all new hires, my first 30 days involved shadowing the team, getting familiar with our processes, and practicing using static analysis tools by running them against an open-source project of my choosing. I chose to focus on AI software that was in scope for Pwn2Own 2025. While the automated tools flagged potential issues, it took manual analysis to demonstrate exploitability, and required an alternate angle (in this case, chunked transfer encoding) to prove why a bug/unsafe
Trailofbits
Uncovering memory corruption in NVIDIA Triton (as a new hire)
blogs_trailofbits·2025-08-04·CVSS 9.8
[CRITICAL] Uncovering memory corruption in NVIDIA Triton (as a new hire)
In my first month at Trail of Bits as an AI/ML security engineer, I found two remotely accessible memory corruption bugs in NVIDIA’s Triton Inference Server during a routine onboarding practice. The bugs result from the way HTTP requests are handled by a number of the API routes, including the inference endpoint.
Like all new hires, my first 30 days involved shadowing the team, getting familiar with our processes, and practicing using static analysis tools by running them against an open-source project of my choosing. I chose to focus on AI software that was in scope for Pwn2Own 2025. While the automated tools flagged potential issues, it took manual analysis to demonstrate exploitability, and required an alternate angle (in this case, chunked transfer encoding) to prove why a bug/unsafe
2025-08-06
Published