CVE-2021-29568 in TensorFlow
Summary
by MITRE • 05/15/2021
TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger undefined behavior by binding to null pointer in `tf.raw_ops.ParameterizedTruncatedNormal`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/3f6fe4dfef6f57e768260b48166c27d148f3015f/tensorflow/core/kernels/parameterized_truncated_normal_op.cc#L630) does not validate input arguments before accessing the first element of `shape`. If `shape` argument is empty, then `shape_tensor.flat()` is an empty array. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit 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.
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Analysis
by VulDB Data Team • 05/19/2021
The vulnerability CVE-2021-29568 resides within the TensorFlow machine learning platform, specifically in the `tf.raw_ops.ParameterizedTruncatedNormal` operation that handles parameterized truncated normal distributions. This flaw represents a classic null pointer dereference issue that can lead to undefined behavior and potential system instability. The vulnerability stems from insufficient input validation within the kernel implementation where the code attempts to access elements of a shape tensor without first verifying that the tensor contains valid data. The affected implementation is located in the parameterized_truncated_normal_op.cc file at line 630, where the code directly accesses `shape_tensor.flat()` without checking if the shape argument is empty or null.
The technical exploitation of this vulnerability occurs when an attacker provides an empty shape argument to the `ParameterizedTruncatedNormal` operation. When the shape tensor is empty, the `shape_tensor.flat()` call results in an empty array, yet the subsequent code attempts to access the first element of this empty array leading to undefined behavior. This type of vulnerability falls under CWE-476 which specifically addresses null pointer dereference conditions, and represents a direct violation of proper input validation principles. The flaw is particularly concerning in machine learning environments where TensorFlow operations are often invoked with dynamic inputs from various sources, potentially including untrusted user data.
From an operational impact perspective, this vulnerability can enable attackers to cause application crashes, system instability, or potentially facilitate more sophisticated attacks depending on the execution environment. The vulnerability affects multiple TensorFlow versions including 2.1.4 through 2.4.2, indicating it was present across several supported release lines and could impact production systems running these versions. The undefined behavior resulting from null pointer access could manifest as segmentation faults, memory corruption, or denial of service conditions that would disrupt machine learning workflows. This vulnerability is particularly dangerous in cloud environments or server deployments where TensorFlow serves as a backend for automated machine learning processes, as it could be exploited to interrupt critical AI services.
The mitigation strategy for CVE-2021-29568 involves updating to TensorFlow version 2.5.0 or applying the cherry-picked fix to affected versions 2.4.2, 2.3.3, 2.2.3, and 2.1.4. This fix implements proper validation of input arguments before accessing the shape tensor elements, ensuring that empty or null shapes are handled gracefully rather than causing undefined behavior. Organizations should prioritize updating their TensorFlow installations to prevent exploitation, particularly in environments where the platform processes untrusted inputs. Security teams should also implement monitoring for unusual patterns in machine learning operations that might indicate attempted exploitation of this vulnerability, as the undefined behavior could potentially be leveraged for more advanced attack vectors within the broader ATT&CK framework, specifically under the execution and privilege escalation categories where system stability is compromised.