CVE-2026-24210: Nvidia Triton Inference Server DOS Flaw

CVE-2026-24210 Overview

CVE-2026-24210 is an integer overflow vulnerability [CWE-190] affecting NVIDIA Triton Inference Server. A remote attacker can trigger the overflow over the network without authentication or user interaction. Successful exploitation results in denial of service against the inference server process. The vulnerability impacts availability only — confidentiality and integrity remain unaffected per the CVSS vector.

NVIDIA published a security advisory through its customer support portal addressing the flaw. The vulnerability carries a CVSS 3.1 base score of 7.5 and is categorized as HIGH severity.

Critical Impact

Unauthenticated remote attackers can crash NVIDIA Triton Inference Server instances by triggering an integer overflow, disrupting AI/ML inference workloads that depend on the service.

Affected Products

• NVIDIA Triton Inference Server (see vendor advisory for fixed versions)
• Deployments running on Linux kernel-based hosts
• Containerized inference workloads using affected Triton builds

Discovery Timeline

• 2026-05-20 - CVE-2026-24210 published to NVD
• 2026-05-20 - Last updated in NVD database
• Vendor advisory - Published at NVIDIA Support Answer 5828

Technical Details for CVE-2026-24210

Vulnerability Analysis

The flaw resides in input handling logic within NVIDIA Triton Inference Server. An attacker submits crafted network input that causes an arithmetic operation to exceed the maximum value representable by the target integer type. The resulting wraparound corrupts size calculations, loop bounds, or memory allocation parameters used downstream.

NVIDIA's advisory describes the outcome as denial of service. The integer overflow likely manifests during request parsing or tensor size computation, causing the server process to terminate or enter an unrecoverable state. The CVSS vector AV:N/AC:L/PR:N/UI:N indicates network reachability and no preconditions are required beyond protocol access.

Triton typically exposes HTTP, gRPC, and metrics endpoints. Any of these surfaces that accept attacker-influenced numeric fields are candidate attack paths. Production AI inference pipelines using Triton for model serving lose availability when the process crashes, halting downstream applications until the service restarts.

Root Cause

The root cause is missing or insufficient bounds checking on integer values before they participate in arithmetic operations [CWE-190]. When the operation exceeds the integer's representable range, the value wraps and produces an unexpected small or negative number. Subsequent code paths use this corrupted value, triggering the crash or abort condition that yields the denial of service.

Attack Vector

An unauthenticated remote attacker sends a malformed inference request to an exposed Triton endpoint. The request contains numeric fields — such as tensor dimensions, batch sizes, or payload length descriptors — engineered to trigger the overflow. No authentication, privileges, or user interaction are required.

Verified proof-of-concept code is not publicly available at this time. Refer to the NVIDIA Support Answer 5828 for technical specifics on the vulnerable code paths and patched versions.

Detection Methods for CVE-2026-24210

Indicators of Compromise

• Unexpected termination or restart of the tritonserver process without administrator action
• Crash dumps or SIGABRT/SIGSEGV signals logged for Triton processes
• Spikes in failed inference requests followed by service unavailability
• Inbound HTTP or gRPC requests containing abnormally large or negative numeric fields targeting Triton endpoints

Detection Strategies

• Monitor Triton server logs for assertion failures, allocation errors, and abnormal exit codes
• Inspect HTTP and gRPC request payloads for tensor shape or size fields outside expected ranges
• Correlate process termination events with preceding network traffic to inference ports (default 8000/8001/8002)
• Apply web application firewall or API gateway rules that validate numeric bounds on inference request schemas

Monitoring Recommendations

• Track Triton process uptime and restart counts as health metrics
• Alert on repeated 5xx responses from inference endpoints from a single source IP
• Capture network flow telemetry for all hosts running tritonserver to support post-incident analysis
• Enforce schema validation at ingress for inference request bodies

How to Mitigate CVE-2026-24210

Immediate Actions Required

• Apply the NVIDIA security update referenced in NVIDIA Support Answer 5828 to all Triton Inference Server deployments
• Inventory all Triton instances across container registries, Kubernetes clusters, and bare-metal hosts
• Restrict network access to Triton endpoints using firewall rules and service mesh policies
• Require authentication or mutual TLS in front of Triton via an API gateway or reverse proxy

Patch Information

NVIDIA published the official fix and affected version list in the vendor advisory at NVIDIA Support Answer 5828. Administrators should upgrade to the version specified in that advisory. Refer to the NVD CVE-2026-24210 record and the CVE.org entry for authoritative metadata.

Workarounds

• Place Triton behind a reverse proxy that validates and rejects malformed inference requests
• Limit exposure of Triton HTTP and gRPC ports to trusted internal networks only
• Apply rate limiting on inference endpoints to slow automated abuse attempts
• Run Triton under a process supervisor configured to restart the service on crash, reducing availability impact during remediation

# Example: restrict Triton ports to an internal CIDR using iptables
iptables -A INPUT -p tcp --dport 8000 -s 10.0.0.0/8 -j ACCEPT
iptables -A INPUT -p tcp --dport 8001 -s 10.0.0.0/8 -j ACCEPT
iptables -A INPUT -p tcp --dport 8002 -s 10.0.0.0/8 -j ACCEPT
iptables -A INPUT -p tcp --dport 8000 -j DROP
iptables -A INPUT -p tcp --dport 8001 -j DROP
iptables -A INPUT -p tcp --dport 8002 -j DROP