TensorFlow is a big name in the world of machine learning. It helps researchers and engineers train and run deep learning models for everything from computer vision to natural language processing. But even top libraries can have their weak spots. In late 2022, the TensorFlow team discovered a serious bug in the way one of its core operations handles large input values. This bug, tracked as CVE-2022-41907, could expose applications to security risks like crashes, denial of service, or even code execution.

In this post, I’ll break down what CVE-2022-41907 is, show with code how the issue happens, and give you everything you need to fix and stay safe.

What is CVE-2022-41907?

CVE-2022-41907 points to a vulnerability in the ResizeNearestNeighborGrad operation in TensorFlow.

Vulnerability type: Integer overflow

- Potential impact: Crash, denial of service, or other memory corruption risks if large size input is provided
- Patch: GitHub commit 00c821af032ba9e5f5fa3fe14690c8d28a657624

Why Does This Happen?

The operation ResizeNearestNeighborGrad does gradient computation for resizing images using the "nearest neighbor" method. It takes an input tensor, a "size" argument (the new shape), and does some backend arithmetic to figure out how to propagate gradients back.

Problem: When you give a very large value in the size argument, the internal math overflows the integer type used in TensorFlow's code (usually a 32-bit or 64-bit signed integer). This overflow can lead to negative numbers, out-of-bounds errors, or even writing to the wrong memory.

Proof-of-Concept (PoC) Code

Suppose you’re running the following TensorFlow code. You don’t expect any drama if you set the resize size very big, but in vulnerable versions this can make your program crash:

import tensorflow as tf

# Create a fake image tensor
fake_grad = tf.constant([[[[1.]]]], dtype=tf.float32)  # shape (1, 1, 1, 1)
size = [231 - 1, 231 - 1]  # INT_MAX for 32-bit

try:
    # Vulnerable call: overflows in ResizeNearestNeighborGrad
    result = tf.raw_ops.ResizeNearestNeighborGrad(
        grads = fake_grad,
        size = size,
        align_corners = False,
        half_pixel_centers = False,
    )
    print(result)
except Exception as e:
    print("Error or crash occurred:", e)

What Happens Here?

On safe inputs, ResizeNearestNeighborGrad resizes the gradient as expected. But with extremely large size, the internal computation can overflow and misbehave—potentially crashing Python, corrupting memory, or even triggering weird, undefined behavior.

How Bad Is the Risk?

- If you allow arbitrary input shapes (for instance, in a web service), attackers could easily exploit this by sending huge numbers.
- If you only feed known-good data, you’re safer but still not immune—bugs or bad data could accidentally hit the overflow.

The Official Fix

The TensorFlow team patched this with a small but crucial change: commit 00c821af032ba9e5f5fa3fe14690c8d28a657624.

What does the fix do?  
It adds checks to make sure the arithmetic on size doesn’t overflow. If an input is too big or negative, it now raises an error instead of letting the bug happen.

Mitigation Steps

1. Upgrade TensorFlow!

If you must stay on 2.10.x, 2.9.x, or 2.8.x, upgrade to at least 2.10.1, 2.9.3, or 2.8.4.

2. Input Validation  
Add checks to make sure user-provided shapes or sizes for resizing are reasonable. Never trust raw input from users.

Example

def safe_resize_size(size):
    MAX_SIZE = 10000  # pick a reasonable max size for your app
    return [min(s, MAX_SIZE) for s in size]

user_size = get_user_input_somehow()
protected_size = safe_resize_size(user_size)

3. Monitor for Repeated Crashes
If your TensorFlow service starts crashing, especially on image- or shape-heavy input, investigate immediately.

References

- CVE-2022-41907 at NVD
- TensorFlow Security Advisories
- Commit fixing the issue

Summary

CVE-2022-41907 is a classic example of how machine learning code—especially code processing shapes, sizes, and big numbers—needs to be extra careful with checks and input validation. If you use TensorFlow for any critical service, update ASAP and be on the lookout for similar bugs.

Stay safe, keep dependencies up-to-date, and always be wary of unchecked user inputs—even in ML code!

Timeline

Published on: 11/18/2022 22:15:00 UTC
Last modified on: 11/23/2022 13:52:00 UTC