From Zero to Root: Deconstructing the NVIDIA Triton RCE Vulnerability

In the world of artificial intelligence, NVIDIA Triton Inference Server is the high-performance engine that powers countless AI applications, serving up models for everything from language translation to medical imaging. When you interact with a sophisticated AI, there’s a good chance Triton is the workhorse running silently in the background. But what happens when that workhorse can be hijacked by an unauthenticated attacker from across the internet?

Security researchers at Wiz have uncovered a critical vulnerability chain in NVIDIA’s Triton Inference Server that allows exactly that. By chaining together several flaws, a remote attacker with no credentials can achieve full Remote Code Execution (RCE) on the server, gaining complete control over some of the most powerful and data-rich systems in an organization’s arsenal.

The primary vulnerability is tracked as CVE-2025-23319, and the combined chain carries a CVSS score of 9.8 (Critical).

What is NVIDIA Triton and Why Does This Matter?

Before diving into the exploit, it’s crucial to understand what Triton is and why its compromise is so significant. The NVIDIA Triton Inference Server is an open-source solution designed to deploy and serve AI models at scale. It can handle models from any framework (like TensorFlow, PyTorch, or TensorRT) and supports both GPU and CPU-based infrastructure.

Essentially, Triton is the “application server” for the AI era. It’s the critical middleware that exposes trained models to the outside world via an API, allowing applications to get predictions and insights. A compromise of Triton doesn’t just mean a server is down; it means the very brain of your AI-powered services could be stolen, manipulated, or used against you.

The Anatomy of the Attack: A Three-Step Kill Chain to RCE

The genius of this attack, as detailed by Wiz, is that it’s not a single flaw but a chain of three distinct steps that, when combined, turn a simple file upload into a full system takeover.

Step 1: The Foothold — Arbitrary File Upload via Path Traversal (CVE-2025-23319)

The attacker’s entry point is a path traversal vulnerability in Triton’s /v2/repository/models/.../upload API endpoint. This flaw allows an unauthenticated attacker to upload a file to an arbitrary location on the server’s filesystem.

Think of it like a mailroom that doesn’t check the address on a package. The attacker sends a file with a destination like ../../../../../../tmp/payload.txt, and the server, failing to properly sanitize the path, dutifully places the file in the /tmp/ directory instead of the intended, secured model repository. This gives the attacker their initial foothold on the machine.

Step 2: The Trojan Horse — Loading a Malicious Model Configuration

With the ability to write files anywhere, the attacker’s next move is to abuse Triton’s own model-loading mechanism. They use the same path traversal vulnerability to upload two malicious files into a new directory they create (e.g., /tmp/malicious-model/):

• config.pbtxt: This is a standard Triton model configuration file. However, the attacker crafts it with specific instructions. They define the model’s “backend” or “platform” as python. This tells Triton that the model itself is not a standard neural network file, but is instead a Python script.
• 1/model.py: This is the attacker’s actual payload, disguised as a model file. It’s a simple Python script containing the commands the attacker wants to execute on the server (e.g., a reverse shell).

Step 3: The Execution — Triton Obeys and Runs the Code

The trap is now set. The final step is to instruct Triton to load this malicious “model.” The attacker sends a legitimate API request to the /v2/repository/models/.../load endpoint, pointing to their malicious model directory (/tmp/malicious-model).

Triton, behaving exactly as it was designed to, does the following:

1. It reads the config.pbtxt file.
2. It sees the instruction to use the python backend.
3. It loads and executes the 1/model.py file to “serve” the model.

In that instant, the attacker’s Python payload runs with the full permissions of the NVIDIA Triton server process. The result is a complete, unauthenticated Remote Code Execution.

The Impact: More Than Just a Compromised Server

The consequences of this attack chain extend far beyond a single server compromise. For a CISO, this represents a multi-faceted business risk that aligns with broader AI security concerns:

• Intellectual Property and Data Theft: Attackers gain access to the most sensitive assets on the server: the proprietary AI models themselves, as well as the sensitive data being fed to them for inference.
• A Gateway for Lateral Movement: AI servers are often powerful machines with privileged access to data stores and other parts of the network. A compromised Triton server is a perfect beachhead for an attacker to pivot deeper into your cloud or on-premises environment.
• Abuse of High-Cost GPU Resources: High-end GPUs are the engine of AI, and they are also a prime target for cryptomining operations. Attackers can use the compromised server to mine cryptocurrency on your dime, leading to massive and unexpected cloud bills.
• Model Manipulation and Sabotage: An attacker could subtly modify models to produce incorrect or biased outputs, silently undermining the integrity of your AI-driven business processes.

Your Action Plan: A 3-Step Guide to Patching, Hunting, and Hardening

This is a critical, exploitable vulnerability chain, and security teams must act decisively.

1. PATCH: The Immediate and Essential Fix

NVIDIA has released patches to address these vulnerabilities. This is the most critical and effective action you can take.

• Affected Versions: NVIDIA Triton Inference Server versions 24.05 and earlier.
• Patched Versions: The flaws are remediated in version 24.06 and later.

Action: All teams running NVIDIA Triton Inference Server must prioritize upgrading to the latest version immediately. Consult the official NVIDIA Security Bulletin (5687) for detailed information and download links.

2. HUNT: Proactively Search for Signs of Compromise

Because this vulnerability can be exploited without authentication, it’s crucial to hunt for any signs of a past or ongoing compromise.

• Look for Suspicious File Paths: The Wiz research team recommends searching your Triton server filesystems for model files (config.pbtxt, model.py, etc.) in unexpected locations, such as /tmp/, /dev/shm/, or other world-writable directories.
• Audit Model Loading Logs: Scrutinize your Triton logs for any load requests for models residing outside of your standard, trusted model repositories.
• Monitor for Anomalous Network Activity: Watch for unexpected outbound connections from your Triton servers, which could indicate a reverse shell or C2 communication channel established by an exploited server.
• Review for Unusual Model Configurations: Look for any model configurations (config.pbtxt) that use the python backend but are not part of a known, legitimate use case.

Conclusion: Securing the New AI Attack Surface

The “Wiz” vulnerability chain in NVIDIA Triton is a stark reminder that the AI revolution brings with it a new and potent attack surface. The tools that power our most advanced innovations are now prime targets. Attackers are demonstrating a deep understanding of this new technology stack and are creatively chaining together seemingly minor flaws to achieve devastating results.

For security leaders, the message is clear: we must extend our security programs to cover the entire AI lifecycle. This means proactive patching, continuous monitoring, and a defense-in-depth strategy that treats AI infrastructure with the same rigor as any other business-critical system. Modern cloud threat detection strategies must evolve to include AI-specific threats. The future will be intelligent, but it must also be secure.

To further enhance your cloud security and implement Zero Trust, contact me on LinkedIn Profile or [email protected].

NVIDIA Triton RCE FAQ (CVE-2025-23319)

• What is the core vulnerability? The core vulnerability is CVE-2025-23319, a path traversal flaw that allows an unauthenticated, remote attacker to upload arbitrary files to an NVIDIA Triton Inference Server.
• How does it lead to Remote Code Execution (RCE)? The path traversal is the first step in a chain. An attacker uses it to upload a malicious model configuration (config.pbtxt) and a Python script payload (model.py). They then send a legitimate command to Triton to “load” this malicious model, which causes Triton to execute the attacker’s Python script.
• Who is affected? Organizations using NVIDIA Triton Inference Server versions 24.05 and earlier are affected.
• What is the immediate fix? You must upgrade your Triton Inference Server to version 24.06 or later, as detailed in NVIDIA’s security bulletin.
• Is this a complex attack to carry out? According to the research, the attack is relatively straightforward for a skilled attacker. It abuses the server’s own intended functionality and does not require exploiting a complex memory corruption bug.

Relevant Resource List

• Wiz Blog: “From Zero to Root: A Vulnerability Chain to Take Over NVIDIA Triton AI Servers” (Primary technical source)
• The Hacker News: “NVIDIA Triton Bugs Let Unauthenticated Attackers Hijack AI Servers” (For news and broader context)
• NVIDIA Security Bulletin: “NVIDIA Triton Inference Server - July 2025” (Official source for affected versions and fixes)
• NVIDIA Triton Inference Server Documentation: (For general information on the product and its architecture)