CVE-2025-23333
📋 TL;DR
CVE-2025-23333 is an out-of-bounds read vulnerability in NVIDIA Triton Inference Server's Python backend that allows attackers to read memory beyond allocated bounds by manipulating shared memory data. This could lead to information disclosure of sensitive data from the server's memory. Organizations using NVIDIA Triton Inference Server with Python backend on Windows or Linux are affected.
💻 Affected Systems
- NVIDIA Triton Inference Server
📦 What is this software?
⚠️ Risk & Real-World Impact
Worst Case
An attacker could read sensitive information from server memory, potentially exposing model weights, inference data, credentials, or other confidential information stored in adjacent memory regions.
Likely Case
Information disclosure of random memory contents, potentially including fragments of inference data or server state information, but not reliable extraction of specific sensitive data.
If Mitigated
Limited impact with proper network segmentation and access controls, potentially only exposing non-sensitive memory fragments.
🎯 Exploit Status
Exploitation requires the attacker to have ability to manipulate shared memory data used by the Python backend, which typically requires some level of access or ability to submit specially crafted inference requests.
🛠️ Fix & Mitigation
✅ Official Fix
Patch Version: 24.09 or later
Vendor Advisory: https://nvidia.custhelp.com/app/answers/detail/a_id/5687
Restart Required: No
Instructions:
1. Download NVIDIA Triton Inference Server version 24.09 or later from NVIDIA NGC. 2. Replace existing Triton installation with the updated version. 3. No server restart is required for hot-swapping in most configurations.
🔧 Temporary Workarounds
Disable Python Backend Shared Memory
allDisable shared memory usage in Python backend configuration to prevent the vulnerability from being exploited.
Set 'shared-memory' parameter to 'off' in Python backend configuration
Use Alternative Backends
allSwitch to non-Python backends (TensorRT, ONNX Runtime, etc.) for inference workloads where possible.
Configure model repositories to use non-Python backends
🧯 If You Can't Patch
- Implement strict network access controls to limit who can submit inference requests to the Triton server
- Monitor for unusual memory access patterns or abnormal inference request patterns
🔍 How to Verify
Check if Vulnerable:
Check Triton server version with: 'tritonserver --version' and verify if it's below 24.09Check Version:
tritonserver --versionVerify Fix Applied:
Confirm version is 24.09 or higher with: 'tritonserver --version'📡 Detection & Monitoring
Log Indicators:
- Unusual Python backend errors related to memory access
- Abnormal shared memory operations in system logs
Network Indicators:
- Unusual patterns of inference requests targeting Python backend
- Multiple rapid requests with varying shared memory parameters
SIEM Query:
source="triton" AND (error="memory" OR error="out of bounds" OR error="shared memory")