TensorFlow is one of the most popular open source platforms for machine learning, used by everyone from hobbyists to big tech companies. But being so popular also makes it an attractive target for attackers—especially when a security flaw is discovered in its codebase. Let’s take a deep dive into a serious vulnerability: CVE-2022-41900, involving FractionalMaxPool and FractionalAvgPool operations and how a subtle bug could lead to memory corruption, crashes, or even remote code execution.

TL;DR

- Vulnerability: Improper input handling in TensorFlow’s FractionalMaxPool and FractionalAvgPool ops.

Type: Heap out-of-bounds access (memory corruption).

- Impact: Potential crashes or arbitrary code execution via malicious models/scripts.
- Fixed in: GitHub commit 2165251, available in 2.11. and will be back-ported to 2.10.1.

The Vulnerability: What’s Going On?

TensorFlow implements special pooling operations—FractionalMaxPool and FractionalAvgPool—allowing you to apply non-integer pool sizes (the size of the area sampled from input data). These pooling_ratio values are provided as floating point numbers.

The problem occurs when someone provides an illegal or invalid pooling_ratio, which is not validated properly by TensorFlow versions before 2.11.. This means TensorFlow might blindly accept values that result in buffer overflows or underflows during memory allocation and access.

Impact: By crafting a specific input (i.e., a malicious value for pooling_ratio), an attacker can read or write outside of the allocated memory—causing your app to crash, leak information, or even run malicious code in some cases.

Here’s a minimal code snippet on how a malicious user could trigger the bug

import tensorflow as tf

# Example of a malicious, non-sensical pooling_ratio
bad_ratio = [1., .1, 1., 1.]  # .1 is way too small

# Dummy input
x = tf.constant([[1., 2.], [3., 4.]])
x = tf.reshape(x, [1, 2, 2, 1])

# This call can cause heap memory access violations in affected TensorFlow versions
y = tf.raw_ops.FractionalMaxPool(
    value=x,
    pooling_ratio=bad_ratio,
    pseudo_random=False,
    overlapping=False,
    seed=42,
    seed2=42
)

print(y)

What happens?

- With this pooling_ratio, the internal C++ implementation allocates and accesses memory beyond allowed bounds.
- In real-world use, such an input could lead to a crash (segmentation fault), memory leaks, or, with careful crafting, trigger remote code execution (RCE).

*Technical Explainer*

The bug lies in TensorFlow’s C++ kernel code for FractionalMaxPool (and FractionalAvgPool) operations. If the pooling_ratio array contains illegal values (too small, zero, negative, or NaN), it can cause calculations of memory size and offsets to go awry. Here’s a reference to the offending code before the fix.

*CVE & Announcements*

- CVE-2022-41900 - NVD Entry
- TensorFlow Security Advisory

*Patch and Fix*

The TensorFlow team has patched the issue so the operation will check the validity of pooling_ratio inputs and reject illegal ones, preventing the dangerous memory access.

- Patch commit: 2165251

Code excerpt from the patch

if (pooling_ratio[dim] < 1.) {
  context->CtxFailure(errors::InvalidArgument(
    "pooling_ratio[", dim, "] = ", pooling_ratio[dim], " must be >= 1."));
  return;
}

Should You Worry? And What Should You Do

If you are using TensorFlow as an end-user only (never loading user-supplied models or running Python code from untrusted sources), your risk is quite low.

However, if your TensorFlow models can be influenced by untrusted code or if you wrap it in a service (for e.g., ML inference as a service), update immediately to a patched version (2.11.+ or 2.10.1+ after cherry-pick)!

Conclusion

CVE-2022-41900 is a stark reminder that even the best software can have security bugs in unexpected places, like advanced math operations. The good news: The TensorFlow team has responded quickly, and upgrading is simple.

Stay patched—and always audit your code when your data or your users might come from the wild.


Useful Links:  
- TensorFlow Security Updates  
- Commit 2165251 (the patch)  
- CVE-2022-41900 - National Vulnerability Database  

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Timeline

Published on: 11/18/2022 22:15:00 UTC
Last modified on: 11/23/2022 13:35:00 UTC