CVE-2021-37635 (GCVE-0-2021-37635)
Vulnerability from cvelistv5 – Published: 2021-08-12 20:30 – Updated: 2024-08-04 01:23
VLAI
EPSS
VEX
Title
Heap out of bounds access in sparse reduction operations in TensorFlow
Summary
TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation of sparse reduction operations in TensorFlow can trigger accesses outside of bounds of heap allocated data. The [implementation](https://github.com/tensorflow/tensorflow/blob/a1bc56203f21a5a4995311825ffaba7a670d7747/tensorflow/core/kernels/sparse_reduce_op.cc#L217-L228) fails to validate that each reduction group does not overflow and that each corresponding index does not point to outside the bounds of the input tensor. We have patched the issue in GitHub commit 87158f43f05f2720a374f3e6d22a7aaa3a33f750. 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.
Severity
7.3 (High)
CWE
- CWE-125 - Out-of-bounds Read
Assigner
References
2 references
| URL | Tags |
|---|---|
| https://github.com/tensorflow/tensorflow/security… | x_refsource_CONFIRM |
| https://github.com/tensorflow/tensorflow/commit/8… | x_refsource_MISC |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| tensorflow | tensorflow |
Affected:
>= 2.5.0, < 2.5.1
Affected: >= 2.4.0, < 2.4.3 Affected: < 2.3.4 |
guessed |
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"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cgfm-62j4-v4rf"
}
],
"sourceIdentifier": "security-advisories@github.com",
"vulnStatus": "Modified",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-125"
}
],
"source": "security-advisories@github.com",
"type": "Secondary"
},
{
"description": [
{
"lang": "en",
"value": "CWE-125"
}
],
"source": "nvd@nist.gov",
"type": "Primary"
}
]
}
},
"redhat_vex": {
"current_release_date": "2026-03-27T13:45:09+00:00",
"cve": "CVE-2021-37635",
"id": "CVE-2021-37635",
"initial_release_date": "2021-01-01T00:00:00+00:00",
"product_status:known_not_affected": "1",
"source": "Red Hat CSAF VEX",
"status": "final",
"title": "Heap out of bounds access in sparse reduction operations in TensorFlow",
"url": "https://security.access.redhat.com/data/csaf/v2/vex/2021/cve-2021-37635.json",
"version": "3"
},
"suse_vex": {
"aggregate_severity": "important",
"current_release_date": "2025-03-15T08:47:44Z",
"cve": "CVE-2021-37635",
"id": "CVE-2021-37635",
"initial_release_date": "2023-02-15T03:39:31Z",
"product_status:recommended": "56",
"source": "SUSE CSAF VEX",
"status": "interim",
"title": "SUSE CVE CVE-2021-37635",
"url": "https://ftp.suse.com/pub/projects/security/csaf-vex/cve-2021-37635.json",
"version": "6"
}
}
}
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.
Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
- Seen: The vulnerability was mentioned, discussed, or observed by the user.
- Confirmed: The vulnerability has been validated from an analyst's perspective.
- Published Proof of Concept: A public proof of concept is available for this vulnerability.
- Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
- Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
- Not confirmed: The user expressed doubt about the validity of the vulnerability.
- Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.
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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
model, served locally by ML-Gateway.
They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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Related by attack behaviour
Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.
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