CVE-2021-37652 in TensorFlow
Summary
by MITRE • 08/13/2021
TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation for `tf.raw_ops.BoostedTreesCreateEnsemble` can result in a use after free error if an attacker supplies specially crafted arguments. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/boosted_trees/resource_ops.cc#L55) uses a reference counted resource and decrements the refcount if the initialization fails, as it should. However, when the code was written, the resource was represented as a naked pointer but later refactoring has changed it to be a smart pointer. Thus, when the pointer leaves the scope, a subsequent `free`-ing of the resource occurs, but this fails to take into account that the refcount has already reached 0, thus the resource has been already freed. During this double-free process, members of the resource object are accessed for cleanup but they are invalid as the entire resource has been freed. We have patched the issue in GitHub commit 5ecec9c6fbdbc6be03295685190a45e7eee726ab. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.
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Analysis
by VulDB Data Team • 08/17/2021
The vulnerability CVE-2021-37652 affects TensorFlow's implementation of the `tf.raw_ops.BoostedTreesCreateEnsemble` operation, representing a critical use after free error that can be exploited by remote attackers. This flaw exists within TensorFlow's machine learning platform where the implementation handles reference counted resources during initialization failures. The vulnerability stems from a code refactoring that transformed a naked pointer representation into a smart pointer, creating a mismatch between the original error handling logic and the new memory management approach. The affected code path in TensorFlow's core kernels demonstrates how legacy code patterns can create security issues when modernization occurs without complete logic review.
The technical flaw manifests when the `BoostedTreesCreateEnsemble` operation encounters initialization failures and attempts to decrement the reference count of a resource. Under normal circumstances, this decrement should properly manage resource lifecycle, but the refactoring has created a scenario where the resource object undergoes double-free behavior. When the smart pointer goes out of scope, it triggers automatic cleanup that attempts to free memory that has already been freed during the reference count decrement operation. This creates a use after free condition where subsequent cleanup operations attempt to access members of a freed object, leading to potential memory corruption and arbitrary code execution.
The operational impact of this vulnerability extends across multiple TensorFlow versions including 2.3.4, 2.4.3, 2.5.1, and the affected 2.6.0 release, making it a widespread concern for organizations using machine learning platforms. Attackers can exploit this through specially crafted arguments that force the initialization failure path, potentially leading to remote code execution or denial of service conditions. The vulnerability aligns with CWE-415: Double Free, which is categorized under the broader class of memory safety issues in software development. This type of vulnerability is particularly dangerous in machine learning environments where TensorFlow components often process untrusted data inputs from external sources.
The fix implemented in GitHub commit 5ecec9c6fbdbc6be03295685190a45e7eee726ab addresses the core issue by ensuring proper resource lifecycle management during error conditions. The patch corrects the double-free scenario by preventing the automatic smart pointer cleanup from attempting to free memory that has already been explicitly freed during the reference count decrement process. This solution follows established security practices for memory management in C++ applications and aligns with ATT&CK technique T1059.001: Command and Scripting Interpreter for potential exploitation scenarios. Organizations should immediately upgrade to TensorFlow 2.6.0 or apply the cherry-picked patches to versions 2.5.1, 2.4.3, and 2.3.4 to mitigate this vulnerability. The remediation process also emphasizes the importance of thorough code review during refactoring operations, particularly when transitioning from manual memory management to smart pointer-based approaches.