CVE-2025-23334
published 2025-08-06CVE-2025-23334: NVIDIA Triton Inference Server for Windows and Linux contains a vulnerability in the Python backend, where an attacker could cause an out-of-bounds read by…
PriorityP343high7.5CVSS 3.1
AVNACLPRNUINSUCHINAN
EPSS
0.75%
50.7th percentile
NVIDIA Triton Inference Server for Windows and Linux contains a vulnerability in the Python backend, where an attacker could cause an out-of-bounds read by sending a request. A successful exploit of this vulnerability might lead to information disclosure.
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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Suricata
ET WEB_SPECIFIC_APPS Vulnerable aiohttp Server Version Response (CVE-2024-23334)
suricata·2024-09-24·CVSS 5.9
CVE-2024-23334 [MEDIUM] ET WEB_SPECIFIC_APPS Vulnerable aiohttp Server Version Response (CVE-2024-23334)
ET WEB_SPECIFIC_APPS Vulnerable aiohttp Server Version Response (CVE-2024-23334)
Rule: alert http $HOME_NET any -> any any (msg:"ET WEB_SPECIFIC_APPS Vulnerable aiohttp Server Version Response (CVE-2024-23334)"; flow:established,to_client; http.server; content:"aiohttp|2f|"; fast_pattern; pcre:"/^(?:1\x2e(?:0\x2e[6-9]|[1-9]\x2e\d)|2\x2e\d\x2e(?:0(?:rc1)?|[1-9]){1,2}|3\x2e[0-6]\x2e[0-1])\x0d\x0a/R"; reference:cve,2024-23334; classtype:web-application-activity; sid:2056093; rev:2; metadata:attack_target Server, tls_state plaintext, created_at 2024_09_24, cve CVE_2024_23334, deployment Perimeter, deployment Internal, performance_impact Low, confidence High, signature_severity Major, tag Exploit, updated_at 2024_09_25, reviewed_at 2025_08_26, mitre_tactic_id TA0001, mitre_tactic_name Initial_
Wiz
Crying Out Cloud Newsletter - September 2025 | Wiz
blogs_wiz·2025-09-07·CVSS 8.1
[HIGH] Crying Out Cloud Newsletter - September 2025 | Wiz
Welcome back! In this edition, we bring you the latest in cloud security - noteworthy incidents, exclusive data, and crucial vulnerabilities. Let's dive in.
## 🔍 Highlights
s1ngularity: Supply Chain Attack Leaks Secrets on GitHub
On August 26, 2025, multiple malicious versions of the widely used Nx build system package were published to the npm registry. These versions contained a post-installation malware script designed to harvest sensitive developer assets, including cryptocurrency wallets, GitHub and npm tokens, SSH keys, and more. The malware leveraged AI command-line tools (including Claude, Gemini, and Q) to aid in their reconnaissance efforts, and then exfiltrated the stolen data to publicly accessible attacker-created repositories within victims’ GitHub accounts.
Learn more in
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
Wiz
Breaking NVIDIA Triton: CVE-2025-23319 - A Vulnerability Chain Leading to AI Server Takeover | Wiz Blog
blogs_wiz·2025-08-04·CVSS 9.0
[CRITICAL] Breaking NVIDIA Triton: CVE-2025-23319 - A Vulnerability Chain Leading to AI Server Takeover | Wiz Blog
The Wiz Research team has discovered a chain of critical vulnerabilities in NVIDIA's Triton Inference Server, a popular open-source platform for running AI models at scale. When chained together, these flaws can potentially allow a remote, unauthenticated attacker to gain complete control of the server, achieving remote code execution (RCE).
This attack path originates in the server's Python backend and starts with a minor information leak that cleverly escalates into a full system compromise. 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, manipulating the AI model's responses and a foothold for attackers to move deeper into a network.
Wiz Research responsibly disclosed
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
Wiz
Breaking NVIDIA Triton: CVE-2025-23319 - A Vulnerability Chain Leading to AI Server Takeover | Wiz Blog
blogs_wiz·2025-08-04·CVSS 9.0
CVE-2025-23319 [CRITICAL] Breaking NVIDIA Triton: CVE-2025-23319 - A Vulnerability Chain Leading to AI Server Takeover | Wiz Blog
The Wiz Research team has discovered a chain of critical vulnerabilities in NVIDIA's Triton Inference Server, a popular open-source platform for running AI models at scale. When chained together, these flaws can potentially allow a remote, unauthenticated attacker to gain complete control of the server, achieving remote code execution (RCE).
This attack path originates in the server's Python backend and starts with a minor information leak that cleverly escalates into a full system compromise. 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, manipulating the AI model's responses and a foothold for attackers to move deeper into a network.
Wiz Research responsibly disclosed
2025-08-06
Published