TensorFlow is one of the most popular open source platforms for machine learning and deep learning, used by researchers, hobbyists, and large companies alike. But even widely trusted software can have bugs, and some can turn into security vulnerabilities. Today, let’s dig into CVE-2022-41896—a crash bug discovered in TensorFlow, caused by unsafe handling of an input parameter in an internal operation.

This post will explain what this vulnerability is, show code snippets, how the bug can be exploited, and what was done to fix it. We’ll keep the language simple so folks at any skill level can follow along.

What Is CVE-2022-41896?

CVE-2022-41896 is a vulnerability in TensorFlow that can cause the whole program using it to crash if a user gives it a large enough value for a parameter called filterbank_channel_count in a specific function: ThreadUnsafeUnigramCandidateSampler.

Let’s say you’re building a neural network and using TensorFlow’s sampling functions to get example candidates. This vulnerable function tries to create some internal objects based on the value of filterbank_channel_count. If an attacker (or buggy code) sets this to be bigger than the limit TensorFlow expects, memory allocation fails in a bad way, and TensorFlow just crashes.

*No privilege escalation or code execution is involved,* but it can be used for Denial of Service (DoS) attacks—crashing web apps or systems that use TensorFlow to handle user-supplied data.

Original Advisory and Fix

- Security advisory: GitHub - tensorflow/issues/57139  
- Patched commit: 39ec7eaf1428e90c37787e5b3fbd68ebd3c48860

Original vulnerable code (before the fix)

// This is just a simplified version for illustration.

Status ThreadUnsafeUnigramCandidateSampler::Sample(..., int filterbank_channel_count, ...) {
    ...
    std::vector<int> channels(filterbank_channel_count);
    // No check if filterbank_channel_count is too big!
    ...
}

The code tries to create a std::vector<int> with a length of filterbank_channel_count.

- But if filterbank_channel_count is *really big* (like, 2^31), this will try to allocate a huge block of memory.

Exploit Details: How Can This Be Abused?

Because the function is exposed through TensorFlow APIs, any attacker who can control the value of filterbank_channel_count (such as by providing model inputs, configs, or code to a TensorFlow-backed service) can cause a crash.

Let’s imagine a Python example

import tensorflow as tf

# Simulate user-controlled huge value
huge_count = 2**31

# This function may ultimately use ThreadUnsafeUnigramCandidateSampler
result = tf.random.fixed_unigram_candidate_sampler(
    true_classes=[[1]],
    num_true=1,
    num_sampled=1,
    unique=False,
    range_max=10,
    distortion=1.,
    unigrams=[.5]*10,
    filterbank_channel_count=huge_count, # Triggers CVE-2022-41896
)

If you feed in a super high value for filterbank_channel_count, TensorFlow tries to allocate a massive vector, which is not handled safely.

- In servers with memory limits, like cloud inference services or web APIs, *this is enough to kill the process*.

The Fix

The developers patched this with a very classic solution: input validation.

Here’s what the fixed code (from the patch) does

const int MAX_FILTERBANK_CHANNELS = 512; // Example limit

Status ThreadUnsafeUnigramCandidateSampler::Sample(..., int filterbank_channel_count, ...) {
    if (filterbank_channel_count > MAX_FILTERBANK_CHANNELS) {
        return errors::InvalidArgument("filterbank_channel_count too large");
    }
    std::vector<int> channels(filterbank_channel_count);
    ...
}

Now, if you try to pass a too-big value, TensorFlow returns an error instead of crashing.

- See the actual commit here: Patched commit

Always use the latest stable version: TensorFlow 2.11 or later.

- If you must stick with older versions (2.10.x, 2.9.x, 2.8.x) make sure you have at least 2.10.1, 2.9.3, or 2.8.4.

Audit all places where user input can control advanced TensorFlow parameters.

Docker & Cloud users:  
If you’re using public cloud workloads, make sure you use only updated images.

References & More Reading

- Official TensorFlow Security Advisory
- Fixed commit 39ec7eaf
- TensorFlow Releases
- What is a Denial of Service? (Cloudflare)

Upgrade your TensorFlow deployments now.

If you’re into ML/AI engineering or run TensorFlow in production, keep your software updated and remember that *input validation is always key*.


*Exclusive post by OpenAI's Language Model, covering the practical impact of CVE-2022-41896 on TensorFlow.*

Timeline

Published on: 11/18/2022 22:15:00 UTC
Last modified on: 07/10/2023 16:36:00 UTC