Table of Contents:

[References and Further Reading](#references)

What is CVE-2023-38671 and Why Does it Matter?

CVE-2023-38671 is a high-impact security vulnerability found in PaddlePaddle, an open-source deep learning framework. This flaw, present before version 2.5., is caused by a heap buffer overflow in the paddle.trace function.

Possibly execute code and cause even greater damage

CVE-2023-38671 received media coverage due to PaddlePaddle’s growing use in AI, deep learning, and cloud platforms.


Quick Introduction to PaddlePaddle

PaddlePaddle (Parallel Distributed Deep Learning) is a powerful and flexible deep learning framework, mostly developed by Baidu. Its use is growing all over the world because of good performance and easy syntax, especially popular in China and research environments.


Explaining Heap Buffer Overflow in Simple Words

A heap buffer overflow happens when a program tries to write more data to a spot in memory (the "heap") than the program has allocated. Think of it like pouring 2 gallons of water into a 1-gallon bucket — the extra water spills over, making a mess.

This extra data might overwrite important information, crash the program, or even let an attacker run malicious code.


How Does the Vulnerability Happen in paddle.trace?

The trace function in PaddlePaddle is used for tracing execution graphs (like PyTorch’s jit.trace). In PaddlePaddle versions before 2.5., the code didn’t properly check the size of certain buffers before copying or writing data, resulting in a heap buffer overflow.

Here is a simplified example in C++-like pseudocode (not the real source, but illustrative)

void trace_execute(const char* input) {
    char buffer[100];
    // BAD: does not check if input is too big for buffer - classic overflow!
    strcpy(buffer, input);  
    process(buffer);
}

If input is longer than 100 characters, it’ll overwrite memory after buffer. Similar unsafe copying happened deeper in the real PaddlePaddle codebase when handling certain inputs with paddle.trace.


Proof-of-Concept Code Snippet

Let’s look at a safer Python-style PoC that shows how this might be triggered using PaddlePaddle (pre-2.5.):

import paddle

# Suppose 'trace' calls into unsafe native code underneath
def make_oversized_model_input():
    # Generate a massively oversized input that can trigger the underlying overflow
    return paddle.randn([1024, 1024, 1024], dtype='float32')  # Huge tensor!

# Vulnerable call
if __name__ == "__main__":
    model = paddle.nn.Linear(1024, 8)
    big_input = make_oversized_model_input()
    try:
        paddle.jit.trace(model, big_input)
    except Exception as e:
        print("Error happened!", e)

On vulnerable versions, this can make PaddlePaddle crash, leak memory, or behave unpredictably. In a real attacker’s hands, it could be further tailored to grab sensitive info or even inject code.


Impact: What Could Attackers Really Do?

- Denial of Service: By feeding oversized data to paddle.trace, an attacker could crash apps using PaddlePaddle, causing outages in AI pipelines and services.
- Information Disclosure: Since buffer overflows often let you peek into adjacent memory, secrets like models or API tokens could potentially leak.
- Code Execution: In special cases, with skillful exploitation, an attacker could manipulate overflowed memory to run their own code—a worst-case scenario.


Update to version 2.5. or later — the bug is patched.

Download: https://github.com/PaddlePaddle/Paddle/releases

Input Validation:

Always validate and restrict the size of the data you pass to model-tracing functions, especially if it could come from outside sources.

Run as Low Privilege:

Don’t run AI services as root/admin to limit the blast radius if exploited.

Keep up with new PaddlePaddle releases and set up alerts for CVEs in frameworks you use.

References and Further Reading

- Official PaddlePaddle Security Advisory (GHSA-6gxw-hh36-jxvw)
- CVE-2023-38671 at MITRE
- PaddlePaddle Release Notes
- Understanding Buffer Overflows (OWASP)

In Short

CVE-2023-38671 in PaddlePaddle pre-2.5. is a serious bug that lets attackers mess with memory when tracing models, risking crashes, leaks, or worse. If you use PaddlePaddle, upgrade now, review your input sizes, and keep an eye out for similar flaws in your AI stack. Stay safe and patched!

If you want more technical details or demonstration code, check the links above — and don’t forget to update!

Timeline

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