Vulnerabilities > CVE-2021-29608 - Incorrect Calculation of Buffer Size vulnerability in Google Tensorflow

047910
CVSS 7.8 - HIGH
Attack vector
LOCAL
Attack complexity
LOW
Privileges required
LOW
Confidentiality impact
HIGH
Integrity impact
HIGH
Availability impact
HIGH
local
low complexity
google
CWE-131

Summary

TensorFlow is an end-to-end open source platform for machine learning. Due to lack of validation in `tf.raw_ops.RaggedTensorToTensor`, an attacker can exploit an undefined behavior if input arguments are empty. The implementation(https://github.com/tensorflow/tensorflow/blob/656e7673b14acd7835dc778867f84916c6d1cac2/tensorflow/core/kernels/ragged_tensor_to_tensor_op.cc#L356-L360) only checks that one of the tensors is not empty, but does not check for the other ones. There are multiple `DCHECK` validations to prevent heap OOB, but these are no-op in release builds, hence they don't prevent anything. The fix will be included in TensorFlow 2.5.0. We will also cherrypick these commits on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.

Vulnerable Configurations

Part Description Count
Application
Google
385

Common Weakness Enumeration (CWE)

Common Attack Pattern Enumeration and Classification (CAPEC)

  • Overflow Buffers
    Buffer Overflow attacks target improper or missing bounds checking on buffer operations, typically triggered by input injected by an attacker. As a consequence, an attacker is able to write past the boundaries of allocated buffer regions in memory, causing a program crash or potentially redirection of execution as per the attackers' choice.
  • Buffer Overflow via Parameter Expansion
    In this attack, the target software is given input that the attacker knows will be modified and expanded in size during processing. This attack relies on the target software failing to anticipate that the expanded data may exceed some internal limit, thereby creating a buffer overflow.