CVE-2021-29605: TensorFlow TFLite Integer Overflow Vulnerability

Background

TensorFlow is an end-to-end open source platform for machine learning. It provides a comprehensive ecosystem of tools, libraries, and community resources that enable researchers and developers to build and deploy machine learning models. TensorFlow Lite (TFLite) is a lightweight version of TensorFlow designed for mobile and embedded devices. TFLite allows developers to run machine learning models on resource-constrained platforms. However, a vulnerability has been discovered in the TFLite code that handles the allocation of TFLiteIntArray objects.

Vulnerability Detail

The TFLite code for allocating TFLiteIntArrays is vulnerable to an integer overflow issue. This vulnerability arises due to an unchecked multiplication operation that can result in an overflow of the int datatype. An attacker can exploit this vulnerability by crafting a malicious model that triggers the overflow, leading to an invalid value being passed to the malloc function. As a result, the ret->size pointer dereferences an invalid memory location, potentially leading to a crash or arbitrary code execution. This vulnerability affects TensorFlow versions 2.1.4 and above, including TensorFlow 2.5.0.

pip install --upgrade "tensorflow==2.21.0"

Workaround

As a temporary workaround, developers can manually apply the patch provided by the TensorFlow team to fix the integer overflow issue. The patch involves modifying the common.c file in the TFLite codebase. Here is the modified code snippet:

// Original code
int size = count * sizeof(int);

// Patched code
int64_t size64 = (int64_t)count * sizeof(int);
if (size64 > INT_MAX) {
  // Handle the overflow case
  // ... (allocate memory using a different approach)
} else {
  int size = (int)size64;
  // ... (allocate memory using the original approach)
}

Conclusion

In conclusion, a vulnerability has been identified in TensorFlow Lite (TFLite) that allows an attacker to trigger an integer overflow, potentially leading to crashes or arbitrary code execution. The TensorFlow team has released patches to address this vulnerability, and it is crucial for users to update their TensorFlow installations to the latest versions. For further assistance or support in vulnerability remediation, please contact DevNack.com.

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