CVE-2026-24214: Nvidia Triton Inference Server RCE Flaw

CVE-2026-24214 Overview

CVE-2026-24214 is an integer overflow vulnerability in the NVIDIA Triton Inference Server DALI (Data Loading Library) backend. An unauthenticated remote attacker can trigger the overflow condition by sending crafted input to the affected backend. Successful exploitation may lead to arbitrary code execution, data tampering, or denial of service against the inference server.

The flaw is tracked under CWE-190: Integer Overflow or Wraparound. Because Triton is widely deployed for production AI/ML inference workloads, exploitation could compromise model integrity and host systems running GPU inference services.

Critical Impact

Network-reachable Triton Inference Server instances using the DALI backend can be exploited without authentication or user interaction, enabling code execution and full compromise of inference workloads.

Affected Products

• NVIDIA Triton Inference Server (DALI backend)
• Linux distributions running affected Triton container images
• Windows builds of NVIDIA Triton Inference Server

Discovery Timeline

• 2026-05-20 - CVE-2026-24214 published to NVD
• 2026-05-20 - NVIDIA security advisory answer ID 5828 published
• 2026-05-20 - Last updated in NVD database

Technical Details for CVE-2026-24214

Vulnerability Analysis

The vulnerability resides in the DALI backend component of NVIDIA Triton Inference Server. DALI handles input data preprocessing pipelines for inference requests, including image decoding, resizing, and tensor construction. The backend performs arithmetic on size or count values derived from attacker-controlled request data without adequate bounds checking.

When the computed value exceeds the maximum representable integer, it wraps around to a small or negative value. Downstream allocation or copy routines then operate on an undersized buffer while processing the original, larger payload. This mismatch produces memory corruption that can be steered toward code execution.

Because Triton exposes HTTP and gRPC endpoints for inference requests, the attack surface is reachable across the network. No prior authentication, privileges, or user interaction are required to deliver a malicious request.

Root Cause

The root cause is missing or incorrect validation of size-related integer values used during tensor or buffer construction in the DALI backend. Operations such as width * height * channels * element_size can overflow the underlying integer type, producing an undersized allocation that is later written with the full-sized payload.

Attack Vector

An attacker submits a crafted inference request to a Triton endpoint configured with the DALI backend. The request supplies tensor dimensions, element counts, or batch sizes engineered to trigger arithmetic overflow during preprocessing. The resulting heap corruption can be leveraged for remote code execution under the Triton service account, data tampering of model inputs or outputs, or process crash denial of service.

The vulnerability is described in prose because no public proof-of-concept code is available. See the NVIDIA Support Article for vendor-specific technical details.

Detection Methods for CVE-2026-24214

Indicators of Compromise

• Unexpected crashes, segmentation faults, or restarts of the tritonserver process, especially correlated with DALI backend activity
• Inference requests containing anomalously large or negative tensor shape parameters in HTTP/gRPC payloads
• Outbound connections, shell processes, or file writes originating from the Triton service account or container
• New or modified files in model repository directories not initiated by authorized MLOps pipelines

Detection Strategies

• Monitor Triton server logs for backend errors, allocation failures, and abnormal termination events tied to DALI preprocessing
• Inspect HTTP/gRPC inference payloads at the API gateway for malformed shape, dtype, or batch-size fields
• Apply behavioral identification on inference hosts to flag process spawning or network egress from tritonserver
• Correlate GPU node telemetry with request logs to identify request patterns that consistently precede crashes

Monitoring Recommendations

• Enable verbose logging on the DALI backend and forward logs to a centralized SIEM for retention and correlation
• Track baseline request shapes and alert on statistical outliers in tensor dimensions or payload size
• Monitor host-level indicators on inference nodes including unexpected child processes, file system writes, and outbound connections

How to Mitigate CVE-2026-24214

Immediate Actions Required

• Apply the patched Triton Inference Server release referenced in the NVIDIA security bulletin as soon as it is available in your environment
• Restrict network exposure of Triton HTTP (8000) and gRPC (8001) endpoints to trusted internal networks or service meshes
• Place an authenticating reverse proxy or API gateway in front of Triton to enforce request authentication and input validation
• Audit deployed model configurations to confirm whether the DALI backend is in use and prioritize those workloads

Patch Information

NVIDIA has published guidance for this vulnerability in the official advisory at answer ID 5828. Administrators should consult the advisory for the fixed Triton Inference Server version, update affected container images, and redeploy inference workloads. Verify the patched version is in use by checking the tritonserver --version output after upgrade.

Workarounds

• Disable the DALI backend in tritonserver startup configuration if it is not required by production models
• Enforce strict input validation at the application layer to reject inference requests with oversized or out-of-range tensor dimensions
• Run Triton inside a hardened container with seccomp, AppArmor, or SELinux profiles to limit post-exploitation impact
• Apply network segmentation and zero-trust policies so inference endpoints are only reachable by authorized client services

# Configuration example: launch Triton with only required backends and bind to loopback
tritonserver \
  --model-repository=/models \
  --backend-config=dali,disable=true \
  --http-address=127.0.0.1 \
  --grpc-address=127.0.0.1 \
  --allow-http=true \
  --allow-grpc=true \
  --log-verbose=1