CVE-2025-23327: Nvidia Triton Inference Server DOS Flaw

CVE-2025-23327 Overview

NVIDIA Triton Inference Server for Windows and Linux contains a critical integer overflow vulnerability (CWE-190) that can be triggered through specially crafted inputs. This vulnerability allows remote attackers to exploit arithmetic operations that exceed the maximum value of integer data types, potentially leading to denial of service conditions and data tampering. The Triton Inference Server is a widely deployed AI inference platform used in production machine learning environments, making this vulnerability particularly significant for organizations running AI/ML workloads.

Critical Impact

Remote attackers can exploit this integer overflow vulnerability without authentication to cause denial of service and data tampering in NVIDIA Triton Inference Server deployments across Windows and Linux systems.

Affected Products

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

Discovery Timeline

• 2025-08-06 - CVE-2025-23327 published to NVD
• 2025-08-12 - Last updated in NVD database

Technical Details for CVE-2025-23327

Vulnerability Analysis

This vulnerability is classified as an Integer Overflow (CWE-190), a memory corruption vulnerability that occurs when arithmetic operations produce results that exceed the storage capacity of the integer type being used. In the context of NVIDIA Triton Inference Server, an attacker can craft malicious inputs that trigger integer overflow conditions, causing the server to behave unexpectedly.

When an integer overflow occurs, the resulting value wraps around to an unintended number, which can lead to incorrect memory allocations, buffer size miscalculations, or logic errors in the application. In this case, the exploitation can result in both denial of service (crashing or hanging the inference server) and data tampering (corrupting inference results or internal state).

The vulnerability is network-accessible and requires no authentication or user interaction to exploit, making it particularly dangerous in production AI/ML environments where Triton Inference Server handles inference requests from various clients.

Root Cause

The root cause of CVE-2025-23327 lies in improper handling of integer arithmetic operations within the NVIDIA Triton Inference Server. When processing specially crafted inputs, the server performs calculations that can exceed the maximum representable value for the integer data type being used. Without proper bounds checking or overflow detection, these arithmetic operations wrap around, producing unexpected values that compromise the integrity and availability of the service.

Attack Vector

The attack vector is network-based, allowing remote exploitation without requiring any privileges or user interaction. An attacker can send specially crafted requests to the Triton Inference Server that contain input values designed to trigger integer overflow conditions during processing. The server's failure to properly validate and sanitize these inputs before performing arithmetic operations enables the exploitation.

The attack mechanism involves sending malformed inference requests or model inputs with extreme numerical values or dimensions that, when processed by internal calculations, cause integer overflow. This can result in incorrect buffer allocations, memory corruption, or processing errors that lead to denial of service or data integrity violations.

Detection Methods for CVE-2025-23327

Indicators of Compromise

• Unexpected crashes or service restarts of NVIDIA Triton Inference Server processes
• Anomalous inference results or data corruption in model outputs
• Unusual memory allocation patterns or out-of-memory errors in server logs
• Network traffic containing abnormally large numerical values in inference requests

Detection Strategies

• Monitor Triton Inference Server logs for crash reports, segmentation faults, or memory allocation failures
• Implement input validation at the network perimeter to detect requests with extreme numerical values
• Deploy runtime application monitoring to detect anomalous behavior in inference processing
• Use network intrusion detection systems (IDS) to identify potentially malicious request patterns

Monitoring Recommendations

• Enable comprehensive logging for NVIDIA Triton Inference Server with attention to error conditions
• Configure alerting for service availability and unexpected restarts
• Monitor system resource utilization (memory, CPU) for anomalies that may indicate exploitation attempts
• Implement API gateway monitoring to track request patterns and identify suspicious input characteristics

How to Mitigate CVE-2025-23327

Immediate Actions Required

• Review the NVIDIA Security Advisory for affected version details and patching guidance
• Identify all NVIDIA Triton Inference Server deployments in your environment
• Restrict network access to Triton Inference Server instances to trusted clients only
• Implement input validation and rate limiting at the network layer where possible

Patch Information

NVIDIA has published a security advisory addressing this vulnerability. Organizations should consult the NVIDIA Support Answer for specific patch versions and upgrade instructions. Apply the vendor-provided security updates to all affected NVIDIA Triton Inference Server installations as soon as possible.

Additional technical details can be found in the NVD CVE-2025-23327 Details page.

Workarounds

• Implement network segmentation to limit access to Triton Inference Server instances from untrusted networks
• Deploy a Web Application Firewall (WAF) or API gateway with input validation rules to filter potentially malicious requests
• Enable authentication and authorization controls to restrict who can submit inference requests
• Consider temporary service isolation if patching cannot be performed immediately

# Example: Restrict network access to Triton Inference Server using iptables
# Allow only specific trusted IP ranges to access the inference server port
iptables -A INPUT -p tcp --dport 8000 -s 10.0.0.0/8 -j ACCEPT
iptables -A INPUT -p tcp --dport 8000 -j DROP