CVE-2024-53880: NVIDIA Triton Inference Server Integer Overflow Vulnerability in Model Loading API Leads to Potential Denial of Service

A vulnerability recently discovered within NVIDIA's Triton Inference Server may lead to undesired denial of service scenarios. The vulnerability, CVE-2024-53880, exists within the software's model loading API, which can be exploited by users loading a model with an extra-large file size. When executed, an integer overflow or wraparound error may occur, which could cause multiple service disruptions within the affected systems.

Details

NVIDIA Triton Inference Server (formerly known as TensorRT Inference Server) is a software solution that enables you to deploy, manage, and scale AI models to deliver optimized predictive analytics. This high-performance deep learning specific software is widely used in numerous fields such as computer vision, speech recognition, and natural language processing.

The recently discovered vulnerability (CVE-2024-53880) stems from NVIDIA Triton Inference Server's model loading API. When a too-large file size is loaded into the system, an integer overflow or wraparound error can affect the software's internal variable, potentially causing service disruptions or denial of service. Although there are currently no known cases of this vulnerability being exploited in the wild, it is imperative for developers and users to understand the exploit and take appropriate action to secure their systems.

Below is a code snippet illustrating the potential vulnerability

// C++ code
int loadModel(size_t model_size) {
    // Vulnerable integer overflow
    size_t buffer_size = model_size + HEADER_SIZE;
    
    // Check for integer overflow
    if (buffer_size < model_size) {
        return ERROR; // Integer overflow detected
    }
    
    void* buffer = malloc(buffer_size);
    if (!buffer) {
        return ERROR; // Memory allocation error
    }
    
    // ... Load the model into the buffer ...
    
    return SUCCESS;
}

In this code snippet, the model_size is added to the HEADER_SIZE, causing an overflow of the variable buffer_size, which can potentially lead to denial of service.

Original References

The vulnerability was discovered by [insert researcher's name or organization] and reported to NVIDIA. You can find a detailed report of the vulnerability at the following links:

1. NVIDIA Security Bulletin
2. CVE-2024-53880 details on MITRE

To ensure that your systems remain safe and uncompromised, it is recommended to follow these steps

1. Update NVIDIA Triton Inference Server to the latest version, which includes a patch for this vulnerability.
2. Implement input validation checks to verify the size of the loaded model file, and ensure that it does not exceed the specified maximum size.

Conclusion

While there are currently no known instances where CVE-2024-53880 has been exploited, being aware of and safeguarding against this vulnerability can help prevent unforeseen service disruptions or denial of service scenarios. By staying up-to-date with security patches and using proper input validation methods, developers and users of NVIDIA Triton Inference Server can better secure their systems.

Timeline

Published on: 02/12/2025 01:15:08 UTC