CVE-2021-29533 in TensorFlow
Summary
by MITRE • 05/15/2021
TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a denial of service via a `CHECK` failure by passing an empty image to `tf.raw_ops.DrawBoundingBoxes`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/ea34a18dc3f5c8d80a40ccca1404f343b5d55f91/tensorflow/core/kernels/image/draw_bounding_box_op.cc#L148-L165) uses `CHECK_*` assertions instead of `OP_REQUIRES` to validate user controlled inputs. Whereas `OP_REQUIRES` allows returning an error condition back to the user, the `CHECK_*` macros result in a crash if the condition is false, similar to `assert`. In this case, `height` is 0 from the `images` input. This results in `max_box_row_clamp` being negative and the assertion being falsified, followed by aborting program execution. 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 described in CVE-2021-29533 affects TensorFlow, a widely-used open-source machine learning platform that processes various data types including images through its extensive library of operations. This specific flaw manifests within the `tf.raw_ops.DrawBoundingBoxes` operation, which is designed to overlay bounding boxes on images for visualization or annotation purposes. The issue stems from improper input validation mechanisms that allow malicious actors to exploit a denial of service condition by providing specifically crafted inputs that trigger program termination.
The technical root cause of this vulnerability lies in the implementation of input validation within the draw bounding boxes kernel, where the code utilizes `CHECK_` assertions instead of the more appropriate `OP_REQUIRES` macro for validating user-provided inputs. This design choice creates a critical security gap because `CHECK_` macros function similarly to standard `assert` statements, causing immediate program termination when their conditions are not met, whereas `OP_REQUIRES` gracefully handles validation failures by returning error conditions to the calling process. The specific failure occurs when an empty image is passed to the function, resulting in a height value of zero, which subsequently leads to negative calculations for `max_box_row_clamp` and triggers the assertion failure.
The operational impact of this vulnerability is significant as it enables attackers to perform denial of service attacks against systems running affected TensorFlow versions by simply providing empty image data to the draw bounding boxes operation. This behavior aligns with the Common Weakness Enumeration (CWE) category CWE-665, which covers improper initialization of resources, and specifically relates to CWE-129, which addresses insufficient validation of array indices. The attack pattern follows the MITRE ATT&CK framework's technique T1499.004, which involves network denial of service through resource exhaustion or manipulation, and T1566.002, which covers spearphishing via social media platforms, where attackers could potentially exploit this vulnerability in web applications processing user-uploaded images. The vulnerability affects multiple TensorFlow versions including 2.1.4, 2.2.3, 2.3.3, 2.4.2, and the unpatched 2.5.0 release, making it particularly concerning for organizations maintaining legacy systems or those unable to immediately upgrade to newer versions. The fix implemented addresses the core validation issue by replacing the problematic `CHECK_*` assertions with proper `OP_REQUIRES` error handling that allows the system to gracefully fail rather than crashing entirely. Organizations should prioritize patching affected versions to prevent potential exploitation, as the vulnerability can be triggered through any application utilizing the draw bounding boxes functionality with malformed image inputs. The patching strategy includes cherry-picking the fix for older supported versions, demonstrating the maintainers' commitment to protecting users of legacy TensorFlow releases while the broader community transitions to newer versions.