CVE-2021-29552 in TensorFlow
Summary
by MITRE • 05/15/2021
TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a denial of service by controlling the values of `num_segments` tensor argument for `UnsortedSegmentJoin`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/a2a607db15c7cd01d754d37e5448d72a13491bdb/tensorflow/core/kernels/unsorted_segment_join_op.cc#L92-L93) assumes that the `num_segments` tensor is a valid scalar. Since the tensor is empty the `CHECK` involved in `.scalar()()` that checks that the number of elements is exactly 1 will be invalidated and this would result in process termination. 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-2021-29552 affects the TensorFlow machine learning platform, specifically targeting the UnsortedSegmentJoin operation within the tensor processing pipeline. This issue represents a denial of service condition that can be triggered by an attacker manipulating the `num_segments` tensor argument, demonstrating a critical weakness in input validation mechanisms. The flaw resides in how TensorFlow handles scalar tensor validation during segment operations, creating an exploitable path that can terminate processes unexpectedly.
The technical implementation flaw occurs at lines 92-93 of the unsorted_segment_join_op.cc file where the code assumes that the `num_segments` tensor is a valid scalar without proper validation. This assumption fails when the tensor is empty, causing the CHECK mechanism that validates scalar tensor elements to fail. The .scalar()() method's validation specifically requires exactly one element, and when this condition is not met, the process terminates rather than gracefully handling the invalid input. This type of vulnerability falls under CWE-691, which classifies inadequate input validation as a weakness that can lead to process termination or other system instability issues.
From an operational perspective, this vulnerability presents a significant risk to TensorFlow deployments where untrusted inputs might be processed through machine learning pipelines. Attackers can exploit this weakness by crafting malicious tensor inputs that result in empty `num_segments` tensors, thereby causing service disruption and potential system instability. The impact extends beyond simple denial of service to potentially affecting production machine learning systems that rely on TensorFlow's segment operations for data processing. The vulnerability affects multiple TensorFlow versions including 2.1.4, 2.2.3, 2.3.3, 2.4.2, and the affected versions are all within supported release ranges, indicating that organizations using these versions face immediate risk without proper patching.
The mitigation strategy involves applying the fix that will be included in TensorFlow 2.5.0, with cherry-picked updates for older supported versions. This approach aligns with industry best practices for vulnerability remediation where patches are backported to maintain security across supported release lines. Organizations should prioritize updating to patched versions and implement additional input validation measures when processing untrusted data through TensorFlow operations. The vulnerability demonstrates the importance of robust input validation in mathematical computing frameworks where malformed tensor inputs can cause system-wide failures rather than simple computation errors. Security teams should monitor for exploitation attempts targeting this specific denial of service vector and ensure that their TensorFlow deployments are updated according to the vendor's security advisories. The ATT&CK framework would categorize this vulnerability under T1499.004 for network denial of service, as the exploitation results in process termination that effectively renders the service unavailable to legitimate users.