What is the CVSS score of CVE-2021-37679?

CVE-2021-37679 has a CVSS 3.1 base score of 7.8 (High). CVSS vector: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H. EPSS exploitation probability: 0.2%.

Is there a patch available for CVE-2021-37679?

Yes, a patch is available for CVE-2021-37679. Update the affected software to the latest version immediately.

Tensorflow CVE-2021-37679

HIGH

Out-of-bounds Read (CWE-125)

2021-08-12 security-advisories@github.com

Buffer Overflow Information Disclosure Tensorflow

7.8

CVSS 3.1 · NVD

Severity by source

NVD PRIMARY

7.8 HIGH

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

CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H

Attack Vector

Local

Attack Complexity

Low

Privileges Required

Low

User Interaction

None

Scope

Unchanged

Confidentiality

High

Integrity

High

Availability

High

Lifecycle Timeline

CVE Published

Aug 12, 2021 - 23:15 nvd

HIGH 7.8

Blast Radius

ecosystem impact

† from your stack dependencies † transitive graph · vuln.today resolves 4-path depth

50 pypi packages depend on tensorflow (48 direct, 2 indirect)
3 pypi packages depend on tensorflow-gpu (3 direct, 0 indirect)

Ecosystem-wide dependent count for version 2.4.0 and other introduced versions.

DescriptionNVD

TensorFlow is an end-to-end open source platform for machine learning. In affected versions it is possible to nest a tf.map_fn within another tf.map_fn call. However, if the input tensor is a RaggedTensor and there is no function signature provided, code assumes the output is a fully specified tensor and fills output buffer with uninitialized contents from the heap. The t and z outputs should be identical, however this is not the case. The last row of t contains data from the heap which can be used to leak other memory information. The bug lies in the conversion from a Variant tensor to a RaggedTensor. The implementation does not check that all inner shapes match and this results in the additional dimensions. The same implementation can result in data loss, if input tensor is tweaked. We have patched the issue in GitHub commit 4e2565483d0ffcadc719bd44893fb7f609bb5f12. 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.

AnalysisAI

TensorFlow is an end-to-end open source platform for machine learning. Rated high severity (CVSS 7.8), this vulnerability is low attack complexity. This Out-of-bounds Read vulnerability could allow attackers to read data from memory outside the intended buffer boundaries.

Technical ContextAI

This vulnerability is classified as Out-of-bounds Read (CWE-125), which allows attackers to read data from memory outside the intended buffer boundaries. TensorFlow is an end-to-end open source platform for machine learning. In affected versions it is possible to nest a tf.map_fn within another tf.map_fn call. However, if the input tensor is a RaggedTensor and there is no function signature provided, code assumes the output is a fully specified tensor and fills output buffer with uninitialized contents from the heap. The t and z outputs should be identical, however this is not the case. The last row of t contains data from the heap which can be used to leak other memory information. The bug lies in the conversion from a Variant tensor to a RaggedTensor. The implementation does not check that all inner shapes match and this results in the additional dimensions. The same implementation can result in data loss, if input tensor is tweaked. We have patched the issue in GitHub commit 4e2565483d0ffcadc719bd44893fb7f609bb5f12. 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. Affected products include: Google Tensorflow.

RemediationAI

A vendor patch is available. Apply the latest security update as soon as possible. Validate array indices and buffer lengths. Use memory-safe languages. Enable AddressSanitizer during testing.