CVE-2025-23311: Nvidia Triton Inference Server RCE Flaw

CVE-2025-23311 Overview

CVE-2025-23311 is a critical stack overflow vulnerability in NVIDIA Triton Inference Server that allows attackers to cause a stack-based buffer overflow through specially crafted HTTP requests. A successful exploit of this vulnerability could lead to remote code execution, denial of service, information disclosure, or data tampering on affected systems running the inference server.

Critical Impact

This network-exploitable vulnerability requires no authentication or user interaction, allowing remote attackers to potentially achieve arbitrary code execution on systems hosting NVIDIA Triton Inference Server.

Affected Products

• NVIDIA Triton Inference Server (all vulnerable versions)
• Linux Kernel (as underlying operating system)
• Microsoft Windows (as underlying operating system)

Discovery Timeline

• 2025-08-06 - CVE-2025-23311 published to NVD
• 2025-08-12 - Last updated in NVD database

Technical Details for CVE-2025-23311

Vulnerability Analysis

This vulnerability is classified as CWE-121: Stack-based Buffer Overflow. The NVIDIA Triton Inference Server, which is widely used for deploying machine learning models in production environments, contains a flaw in its HTTP request handling logic. When processing specially crafted HTTP requests, the server fails to properly validate input boundaries, allowing an attacker to overwrite adjacent memory on the stack.

The vulnerability affects the inference server's HTTP endpoint processing, which is exposed to handle model inference requests. Since Triton Inference Server is typically deployed in cloud and data center environments to serve AI/ML workloads, successful exploitation could compromise critical production infrastructure.

Root Cause

The root cause of CVE-2025-23311 is improper bounds checking when processing HTTP request data. The server allocates a fixed-size buffer on the stack to hold incoming request components, but fails to validate that the incoming data fits within the allocated space. This allows attackers to supply oversized input that overflows the buffer and corrupts adjacent stack memory, including return addresses and saved registers.

Attack Vector

The attack is network-based, requiring only HTTP access to the Triton Inference Server endpoint. The attacker sends maliciously crafted HTTP requests to the server's inference API. These requests contain oversized or malformed data designed to trigger the stack overflow condition. Since no authentication or privileges are required, any network entity with access to the server's HTTP port can attempt exploitation.

The attack flow involves crafting HTTP requests with excessive data in specific fields that the server processes without adequate size validation, causing the stack-based buffer overflow condition described in CWE-121.

Detection Methods for CVE-2025-23311

Indicators of Compromise

• Unusual process crashes or service restarts of tritonserver processes
• Abnormally large HTTP requests targeting Triton Inference Server endpoints
• Unexpected memory access violations or segmentation faults in server logs
• Anomalous network traffic patterns to inference server ports (typically 8000, 8001, 8002)

Detection Strategies

• Deploy network intrusion detection rules to identify oversized or malformed HTTP requests targeting Triton endpoints
• Monitor for process crashes and core dumps from Triton Inference Server processes
• Implement application-level logging to track request sizes and malformed input attempts
• Use SentinelOne Singularity Platform to detect exploitation attempts and anomalous process behavior

Monitoring Recommendations

• Enable verbose logging on Triton Inference Server to capture request details
• Set up alerts for service crashes or unexpected restarts
• Monitor system resource usage for signs of denial of service conditions
• Track inbound HTTP traffic volume and request characteristics to inference server endpoints

How to Mitigate CVE-2025-23311

Immediate Actions Required

• Apply the security patch from NVIDIA as soon as available
• Restrict network access to Triton Inference Server endpoints using firewall rules
• Implement a reverse proxy or WAF to filter and validate incoming HTTP requests
• Monitor systems for signs of exploitation while preparing to patch

Patch Information

NVIDIA has released information regarding this vulnerability. Organizations should consult the NVIDIA Support Article for official patch availability and installation instructions. Administrators should update to the latest patched version of Triton Inference Server as recommended by NVIDIA.

For additional technical details, refer to the NIST CVE-2025-23311 Details page.

Workarounds

• Place Triton Inference Server behind a reverse proxy that enforces strict HTTP request size limits
• Implement network segmentation to limit exposure of inference server endpoints
• Use firewall rules to restrict access to trusted IP ranges only
• Consider temporarily disabling external HTTP access if immediate patching is not possible

# Example: Configure nginx reverse proxy with request size limits
# Add to nginx.conf server block for Triton Inference Server
client_max_body_size 10m;
client_body_buffer_size 128k;
large_client_header_buffers 4 16k;

# Firewall rule to restrict access to Triton ports (example using iptables)
iptables -A INPUT -p tcp --dport 8000 -s 10.0.0.0/8 -j ACCEPT
iptables -A INPUT -p tcp --dport 8000 -j DROP