CVE-2023-38671: Critical Heap Buffer Overflow in PaddlePaddle's paddle.trace Function Demands Immediate Attention

A serious security vulnerability (CVE-2023-38671) has been discovered in PaddlePaddle, one of the most widely used open-source deep learning frameworks. This critical flaw has potentially devastating consequences, including denial of service, information disclosure, and even further damage to systems using PaddlePaddle before version 2.5.. In this in-depth post, we will examine the origins of the vulnerability, a code snippet demonstrating the issue, and all relevant references.

What is PaddlePaddle?

PaddlePaddle, an abbreviation of "Parallel Distributed Deep Learning," is an open-source deep learning framework designed for both researchers and industry professionals. It simplifies the process of building and deploying scalable artificial intelligence (AI) applications. The official website can be found here: PaddlePaddle

PaddlePaddle Vulnerability: CVE-2023-38671

The newly identified vulnerability, CVE-2023-38671, is a heap buffer overflow issue, specifically in the paddle.trace function of PaddlePaddle before version 2.5.. Heap buffer overflows occur when a program writes data beyond the boundaries of an allocated block of memory. The excess data then overwrites adjacent memory, causing unintended side effects. In this case, it means potential denial of service attacks, information disclosure, or even further damage to the system.

Affected Versions

PaddlePaddle versions before 2.5. are affected by this vulnerability. It is strongly recommended to upgrade PaddlePaddle to the latest version, 2.5. or later, to prevent exploitation of this issue.

Here is a simplified example of the heap buffer overflow in the paddle.trace function

import paddle

def vulnerable_trace():
    input_x = paddle.randn([10, 5], dtype="float32")
    function_to_trace = paddle.nn.Linear(in_features=5, out_features=1)
    
    # Creating an invalid trace configuration object
    trace_config = paddle.fluid.dygraph.jit.TracedLayer._get_invalid_trace_cfg()
    
    traced_layer = paddle.fluid.dygraph.jit.TracedLayer(
        function_to_trace, trace_config
    )

    out_dygrah = traced_layer(input_x)

# Calling the vulnerable function
vulnerable_trace()

In this example, an invalid trace configuration object is created and inadvertently introduced into the paddle.trace function, resulting in a heap buffer overflow vulnerability.

Mitigation and Remediation

To remedy this issue, upgrade your PaddlePaddle installation to version 2.5. or later. You can do this using pip:

pip install -U paddlepaddle

Or, if you are using a GPU-enabled version of PaddlePaddle

pip install -U paddlepaddle-gpu

For more information on this vulnerability, please refer to the following resources

1. PaddlePaddle Official GitHub Repository
2. CVE-2023-38671 Official CVE Page
3. PaddlePaddle Documentation
4. PaddlePaddle Upgrade Guide

Conclusion

Security vulnerabilities like CVE-2023-38671 demand immediate attention, especially in widely used, critical components such as open-source deep learning frameworks like PaddlePaddle. It is crucial for developers using PaddlePaddle to be aware of this issue and take the necessary precautions to protect their systems from potential attacks. By upgrading to the latest version of PaddlePaddle and staying informed about security updates, you can help minimize the risks associated with CVE-2023-38671 and other vulnerabilities that may arise in the future.

Timeline

Published on: 07/26/2023 11:15:00 UTC
Last modified on: 07/31/2023 18:11:00 UTC