Tensorflow
CVE-2021-29537
HIGH
Vulnerability Assessment
| Weakness |
CWE
ASVS 5.0
V15 - Secure Coding and Architecture Buffer size calculation
|
| Impact | CVSS score of 7.8 (HIGH). Attack characteristics: local attack vector, low complexity, low privileges required, no user interaction not required. Public proof-of-concept exploit code is available. |
| Exploit ScenarioDerived | Successful exploitation could compromise the high-severity affected system. |
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Alert meAnalysisAI
TensorFlow is an end-to-end open source platform for machine learning. Rated high severity (CVSS 7.8), this vulnerability is low attack complexity. Public exploit code available.
RemediationAI
A vendor patch is available. Apply the latest security update as soon as possible. Apply vendor patches when available. Implement network segmentation and monitoring as interim mitigations.
Affected Packages
Source: OSV (Open Source Vulnerabilities)
Affects your stack
Affects 3 packages in pypi
Checking this CVE against your own stacks and servers is part of the Basic plan and up.
Technical ContextAI · show details
This vulnerability is classified under CWE-131. TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a heap buffer overflow in QuantizedResizeBilinear by passing in invalid thresholds for the quantization. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/50711818d2e61ccce012591eeb4fdf93a8496726/tensorflow/core/kernels/quantized_resize_bilinear_op.cc#L705-L706) assumes that the 2 arguments are always valid scalars and tries to access the numeric value directly. 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. Affected products include: Google Tensorflow.
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Severity by source
AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H
Primary rating from NVD · only source for this CVE.
CVSS VectorNVD
DescriptionNVD
TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a heap buffer overflow in QuantizedResizeBilinear by passing in invalid thresholds for the quantization. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/50711818d2e61ccce012591eeb4fdf93a8496726/tensorflow/core/kernels/quantized_resize_bilinear_op.cc#L705-L706) assumes that the 2 arguments are always valid scalars and tries to access the numeric value directly. 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.
Blast Radius
ecosystem impact- 3 pypi packages depend on tensorflow-gpu (3 direct, 0 indirect)
Ecosystem-wide dependent count for version 2.2.0.
Threat Intelligence Overview
How this decision was reached
The engine reads each signal below and applies a fixed list of rules from top to bottom. The first rule that matches sets the recommended action – like a statement, every line is shown.
What every action level means
SSVC AssessmentENISA
Priority Score
Attack Technique
A buffer overflow occurs when a program writes more data to a memory buffer than it was allocated to hold, causing the excess data to spill into adjacent memory regions.
Affected Products (CPE)NVD
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