CVE-2025-23317: Nvidia Triton Inference Server RCE Flaw

CVE-2025-23317 Overview

CVE-2025-23317 is a critical vulnerability in the NVIDIA Triton Inference Server HTTP server component. An unauthenticated remote attacker can send a specially crafted HTTP request to trigger a heap-based buffer overflow [CWE-122]. Successful exploitation can spawn a reverse shell on the host running the inference server.

NVIDIA Triton Inference Server is widely deployed to serve machine learning models in production AI pipelines across cloud and on-premises environments. The vulnerability affects deployments on both Linux and Windows. Consequences include remote code execution, denial of service, data tampering, and information disclosure.

Critical Impact
Unauthenticated network attackers can achieve remote code execution on Triton Inference Server hosts, exposing AI model infrastructure and training data.

Affected Products

NVIDIA Triton Inference Server (all versions prior to the fixed release)
Triton Inference Server deployments on Linux
Triton Inference Server deployments on Microsoft Windows

Discovery Timeline

2025-08-06 - CVE-2025-23317 published to NVD
2025-08-12 - Last updated in NVD database

Technical Details for CVE-2025-23317

Vulnerability Analysis

The vulnerability resides in the HTTP server component of NVIDIA Triton Inference Server. The flaw is classified as a heap-based buffer overflow [CWE-122]. An attacker reaches the vulnerable code path by sending a specially crafted HTTP request to an exposed Triton endpoint.

Triton Inference Server exposes HTTP and gRPC endpoints to handle inference requests, model management, and health checks. Because the vulnerable HTTP server runs with the privileges of the inference service, code execution gained through this flaw typically inherits access to model artifacts, GPU resources, and adjacent services. The attack requires no authentication and no user interaction.

The EPSS score places this issue in the 87th percentile, reflecting elevated exploitation likelihood relative to other published CVEs. NVIDIA has released a security advisory and patched build addressing the issue.

Root Cause

The root cause is a heap-based buffer overflow in HTTP request handling logic. The server fails to enforce proper bounds when processing attacker-supplied input in an HTTP request, allowing memory beyond the allocated heap buffer to be overwritten. Corruption of adjacent heap metadata or function pointers enables control-flow hijacking and arbitrary code execution.

Attack Vector

The attack vector is network-based and requires no privileges or user interaction. An attacker who can reach the Triton HTTP listener — typically TCP port 8000 by default — sends a malformed HTTP request crafted to overflow the vulnerable heap buffer. The crafted payload is structured to redirect execution and stage a reverse shell back to attacker-controlled infrastructure. See the NVIDIA Support Article for vendor-specific details.

No verified public proof-of-concept code is available at this time. Technical details should be referenced from the NVD CVE-2025-23317 entry.

Detection Methods for CVE-2025-23317

Indicators of Compromise

Unexpected outbound TCP connections originating from Triton Inference Server processes, particularly to non-corporate IP ranges on uncommon ports.
Anomalous child processes spawned by the tritonserver binary, such as /bin/sh, bash, cmd.exe, or powershell.exe.
Triton HTTP server crashes, segmentation faults, or restart loops correlating with malformed inbound HTTP requests.
HTTP requests to Triton endpoints containing oversized headers, unusual content lengths, or malformed JSON inference payloads.

Detection Strategies

Monitor process lineage for shells and interpreters parented by tritonserver or its container runtime.
Inspect HTTP traffic to Triton endpoints (default port 8000) for abnormally large or malformed inference requests targeting /v2/ API routes.
Alert on egress connections from inference server hosts that do not match expected model-fetch or telemetry destinations.
Correlate Triton service crash events with preceding inbound HTTP requests to identify exploitation attempts.

Monitoring Recommendations

Enable verbose access logging on Triton HTTP endpoints and forward logs to a centralized SIEM for retention and analysis.
Deploy network-based intrusion detection signatures for anomalous Triton API traffic patterns.
Track GPU and CPU utilization baselines on inference hosts to surface anomalous workloads consistent with post-exploitation activity.

How to Mitigate CVE-2025-23317

Immediate Actions Required

Upgrade NVIDIA Triton Inference Server to the fixed version listed in the NVIDIA security bulletin.
Restrict network exposure of Triton HTTP and gRPC endpoints to trusted internal networks only — do not expose to the public internet.
Audit running Triton instances for indicators of compromise, including unexpected child processes and outbound connections.
Rotate credentials, API keys, and model signing keys accessible to Triton service accounts if compromise is suspected.

Patch Information

NVIDIA has released patched builds of Triton Inference Server addressing CVE-2025-23317. Refer to the NVIDIA Support Article (Answer ID 5687) for the specific fixed version and download links. Apply the vendor patch as the primary remediation; configuration changes alone do not fully address the underlying heap overflow.

Workarounds

Place Triton Inference Server behind an authenticating reverse proxy or API gateway that validates and size-limits inbound HTTP requests.
Use network segmentation and firewall rules to restrict access to Triton endpoints to authorized inference clients only.
Run Triton inside a hardened container with a minimal, non-root user and read-only filesystem to limit post-exploitation impact.
Disable the HTTP frontend and rely solely on gRPC where the deployment architecture allows, reducing the attack surface until patching is complete.

bash

# Example: restrict Triton HTTP endpoint to localhost and front with a reverse proxy
# Bind Triton HTTP server to loopback only
tritonserver --model-repository=/models \
  --http-address=127.0.0.1 \
  --http-port=8000 \
  --allow-grpc=false

# iptables rule to block external access to Triton ports
iptables -A INPUT -p tcp --dport 8000 -s 10.0.0.0/8 -j ACCEPT
iptables -A INPUT -p tcp --dport 8000 -j DROP