CVE-2025-23324: Nvidia Triton Inference Server DoS Flaw

CVE-2025-23324 Overview

NVIDIA Triton Inference Server for Windows and Linux contains a critical integer overflow vulnerability (CWE-190) that can be exploited by providing malformed requests. When a user submits an invalid request that triggers an integer overflow or wraparound condition, it causes a segmentation fault, resulting in service disruption. This vulnerability poses a significant risk to organizations relying on Triton Inference Server for AI/ML inference workloads in production environments.

Critical Impact

A successful exploit of this vulnerability can lead to denial of service, potentially disrupting critical AI inference operations and machine learning pipelines.

Affected Products

• NVIDIA Triton Inference Server (Windows)
• NVIDIA Triton Inference Server (Linux)
• Systems running Linux kernel with Triton Inference Server deployed

Discovery Timeline

• 2025-08-06 - CVE-2025-23324 published to NVD
• 2025-08-12 - Last updated in NVD database

Technical Details for CVE-2025-23324

Vulnerability Analysis

This vulnerability stems from improper handling of integer arithmetic operations within NVIDIA Triton Inference Server's request processing logic. When the server receives specially crafted invalid requests, the integer overflow condition causes numeric values to wrap around, leading to memory access violations and subsequent segmentation faults.

The attack is network-accessible without requiring authentication or user interaction, making it particularly concerning for internet-facing deployments. The primary impact is to availability, as successful exploitation crashes the inference server, disrupting AI/ML services that depend on it.

Root Cause

The root cause is an Integer Overflow (CWE-190) vulnerability in the request handling code. Integer overflows occur when arithmetic operations produce values that exceed the maximum representable value for the data type, causing the value to wrap around. In this case, the overflow results in invalid memory operations that trigger a segmentation fault, crashing the Triton Inference Server process.

Attack Vector

The attack can be executed remotely over the network by sending malformed requests to the Triton Inference Server. The attacker does not require any privileges or user interaction to exploit this vulnerability. The attack flow involves:

1. Attacker identifies a Triton Inference Server instance exposed on the network
2. Attacker crafts a malicious request containing values designed to trigger integer overflow
3. The server processes the request, and the integer overflow causes memory corruption
4. A segmentation fault occurs, crashing the server and denying service to legitimate users

For technical details on the vulnerability mechanism, refer to the NVIDIA Security Advisory.

Detection Methods for CVE-2025-23324

Indicators of Compromise

• Unexpected Triton Inference Server crashes or restarts
• Segmentation fault errors in server logs (SIGSEGV signals)
• Unusual patterns of malformed requests in network traffic targeting inference endpoints
• Service availability interruptions without apparent resource exhaustion

Detection Strategies

• Monitor Triton Inference Server process health and restart frequency
• Implement log analysis to detect segmentation fault (SIGSEGV) events in application logs
• Deploy network intrusion detection rules to identify anomalous inference request patterns
• Configure alerting for unexpected service terminations or container restarts

Monitoring Recommendations

• Enable detailed logging on Triton Inference Server to capture request metadata
• Implement process monitoring to track server stability and uptime metrics
• Deploy network traffic analysis to baseline normal inference request patterns and detect anomalies
• Use container orchestration health checks to detect and report service disruptions

How to Mitigate CVE-2025-23324

Immediate Actions Required

• Review the NVIDIA Security Advisory for affected versions and patch availability
• Restrict network access to Triton Inference Server to trusted clients and networks
• Implement rate limiting on inference endpoints to reduce potential attack surface
• Monitor server health and configure automatic restart policies as a temporary measure

Patch Information

NVIDIA has published security guidance for this vulnerability. Organizations should consult the NVIDIA Support Answer for specific patch details, affected version information, and upgrade instructions. Apply the vendor-provided security update as soon as it becomes available for your deployment.

Workarounds

• Deploy network-level access controls (firewalls, security groups) to limit who can reach the Triton Inference Server
• Place Triton Inference Server behind a reverse proxy or API gateway that can filter malformed requests
• Implement request validation at the application layer before forwarding to the inference server
• Consider running Triton Inference Server in an isolated environment with automatic restart capabilities to minimize downtime from potential exploitation

# Example: Restrict access to Triton Inference Server using iptables
# Allow only trusted network (e.g., 10.0.0.0/24) to access port 8000
iptables -A INPUT -p tcp --dport 8000 -s 10.0.0.0/24 -j ACCEPT
iptables -A INPUT -p tcp --dport 8000 -j DROP