CVE-2026-24173: NVIDIA Triton Server DoS Vulnerability

CVE-2026-24173 Overview

NVIDIA Triton Inference Server contains a vulnerability where an attacker could cause a server crash by sending a malformed request to the server. This vulnerability stems from an integer overflow condition (CWE-190) that can be triggered remotely without authentication. A successful exploit of this vulnerability might lead to denial of service, disrupting AI/ML inference workloads that depend on the Triton Inference Server.

Critical Impact

Unauthenticated remote attackers can crash NVIDIA Triton Inference Server instances by sending specially crafted malformed requests, causing denial of service to AI/ML inference workloads.

Affected Products

• NVIDIA Triton Inference Server (specific versions to be confirmed via vendor advisory)

Discovery Timeline

• 2026-04-07 - CVE-2026-24173 published to NVD
• 2026-04-08 - Last updated in NVD database

Technical Details for CVE-2026-24173

Vulnerability Analysis

This vulnerability affects the NVIDIA Triton Inference Server, a critical component used for deploying AI and machine learning models at scale. The underlying weakness is classified as CWE-190 (Integer Overflow or Wraparound), which occurs when an arithmetic operation attempts to create a numeric value that is outside of the range that can be represented with a given number of digits.

The vulnerability is network-accessible and does not require any privileges or user interaction to exploit. This makes it particularly dangerous in environments where Triton Inference Server endpoints are exposed to untrusted networks. When exploited, the integer overflow condition leads to a server crash, resulting in complete denial of service for all inference requests being processed.

Root Cause

The root cause of this vulnerability is an integer overflow condition (CWE-190) in the request processing logic of NVIDIA Triton Inference Server. When the server receives a malformed request containing values that exceed the expected integer boundaries, the overflow causes unexpected behavior in memory allocation or processing routines, ultimately leading to a crash.

Integer overflow vulnerabilities typically occur when input validation fails to properly check the bounds of numeric values before performing arithmetic operations. In this case, the malformed request likely contains oversized or specially crafted numeric parameters that trigger the overflow during request parsing or processing.

Attack Vector

The attack can be executed remotely over the network without requiring authentication or user interaction. An attacker would craft a malicious request containing values designed to trigger the integer overflow condition. When the Triton Inference Server processes this request, the overflow causes the server to crash.

The attack sequence involves:

1. Identifying an exposed NVIDIA Triton Inference Server endpoint
2. Crafting a malformed request with values designed to trigger integer overflow
3. Sending the request to the target server
4. The server crashes upon processing the malicious input, causing denial of service

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

Detection Methods for CVE-2026-24173

Indicators of Compromise

• Unexpected Triton Inference Server crashes or restarts
• Anomalous network traffic patterns targeting Triton server ports
• Log entries indicating malformed or oversized request parameters
• Repeated connection attempts from suspicious IP addresses preceding server crashes

Detection Strategies

• Monitor Triton Inference Server logs for crash events and error messages related to request processing failures
• Implement network intrusion detection rules to identify malformed inference requests
• Configure alerting on unexpected server restarts or process terminations
• Deploy application-level firewalls to inspect and validate incoming inference requests

Monitoring Recommendations

• Enable verbose logging on Triton Inference Server to capture detailed request information
• Set up real-time monitoring for server availability and response times
• Configure automated alerts for service disruptions or unusual error rates
• Implement network traffic analysis to baseline normal inference request patterns

How to Mitigate CVE-2026-24173

Immediate Actions Required

• Review the NVIDIA Support Advisory for patch availability and apply updates immediately
• Restrict network access to Triton Inference Server endpoints to trusted sources only
• Implement rate limiting on inference endpoints to reduce attack surface
• Monitor server logs for signs of exploitation attempts

Patch Information

NVIDIA has published information regarding this vulnerability. Organizations should consult the NVIDIA Support Advisory for specific patch details and upgrade instructions. It is strongly recommended to apply vendor-provided patches as soon as they become available.

Workarounds

• Place Triton Inference Server behind a reverse proxy or API gateway that can validate and sanitize incoming requests
• Implement network segmentation to limit exposure of inference server endpoints
• Configure firewall rules to restrict access to Triton server ports from untrusted networks
• Enable request size limits and input validation at the network perimeter

# Example: Restrict access to Triton Inference Server using iptables
# Allow only trusted network ranges to access Triton ports (default: 8000, 8001, 8002)
iptables -A INPUT -p tcp --dport 8000:8002 -s 10.0.0.0/8 -j ACCEPT
iptables -A INPUT -p tcp --dport 8000:8002 -j DROP