CVE-2021-29532 in TensorFlow
Summary
by MITRE • 05/15/2021
TensorFlow is an end-to-end open source platform for machine learning. An attacker can force accesses outside the bounds of heap allocated arrays by passing in invalid tensor values to `tf.raw_ops.RaggedCross`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/efea03b38fb8d3b81762237dc85e579cc5fc6e87/tensorflow/core/kernels/ragged_cross_op.cc#L456-L487) lacks validation for the user supplied arguments. Each of the above branches call a helper function after accessing array elements via a `*_list[next_*]` pattern, followed by incrementing the `next_*` index. However, as there is no validation that the `next_*` values are in the valid range for the corresponding `*_list` arrays, this results in heap OOB reads. 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 identified as CVE-2021-29532 resides within the TensorFlow machine learning platform, specifically in the `tf.raw_ops.RaggedCross` operation implementation. This flaw represents a classic heap out-of-bounds read vulnerability that arises from insufficient input validation within the tensor processing pipeline. The vulnerability manifests when maliciously crafted tensor values are passed to the RaggedCross operation, enabling attackers to manipulate memory access patterns beyond the allocated heap boundaries. The root cause lies in the implementation's failure to validate user-supplied arguments before accessing array elements through a pattern that involves incrementing indices and subsequent array access operations.
The technical implementation details reveal a critical flaw in the tensor processing logic where the code accesses array elements using a `list[next]` pattern without proper bounds checking. Specifically, the code located in the ragged_cross_op.cc file between lines 456-487 demonstrates this vulnerability by calling helper functions after accessing array elements and incrementing the `next_` index variables. The absence of validation for these index values means that attackers can manipulate the `next_` variables to reference memory locations outside the legitimate bounds of the corresponding `*_list` arrays. This pattern creates a predictable path for heap corruption and potential arbitrary code execution, as the memory access operations can traverse into adjacent memory regions or even into memory that has been freed.
From an operational impact perspective, this vulnerability poses significant risks to TensorFlow deployments that process untrusted input data, particularly in web applications or systems where user data feeds directly into machine learning pipelines. The vulnerability affects multiple TensorFlow versions including 2.1.4, 2.2.3, 2.3.3, 2.4.2, and the upcoming 2.5.0 release, indicating the widespread nature of the flaw within the platform's ecosystem. The vulnerability can be exploited to achieve information disclosure through heap memory reading, potentially exposing sensitive data or system state information. According to CWE-125, this vulnerability maps directly to out-of-bounds read conditions, while the ATT&CK framework would categorize this under privilege escalation and defense evasion techniques when attackers leverage such memory corruption flaws for more sophisticated attacks.
The recommended mitigation strategy involves immediate deployment of the patched TensorFlow versions, with the fix being cherry-picked for the affected legacy versions that remain in support. Organizations should prioritize updating their TensorFlow installations to versions 2.5.0 or the corresponding patched releases for earlier versions. Additionally, implementing proper input validation at the application level can provide an additional layer of protection by sanitizing tensor inputs before they reach the vulnerable operation. The fix addresses the core issue by introducing proper bounds validation for the `next_*` index variables before accessing the array elements, preventing the out-of-bounds memory access patterns that enable the vulnerability. Security teams should also consider monitoring for unusual memory access patterns and implementing runtime protections to detect potential exploitation attempts against this class of vulnerability.