CVE-2021-29521 (GCVE-0-2021-29521)
Vulnerability from cvelistv5 – Published: 2021-05-14 19:35 – Updated: 2024-08-03 22:11
VLAI
EPSS
VEX
Title
Segfault in SparseCountSparseOutput
Summary
TensorFlow is an end-to-end open source platform for machine learning. Specifying a negative dense shape in `tf.raw_ops.SparseCountSparseOutput` results in a segmentation fault being thrown out from the standard library as `std::vector` invariants are broken. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/8f7b60ee8c0206a2c99802e3a4d1bb55d2bc0624/tensorflow/core/kernels/count_ops.cc#L199-L213) assumes the first element of the dense shape is always positive and uses it to initialize a `BatchedMap<T>` (i.e., `std::vector<absl::flat_hash_map<int64,T>>`(https://github.com/tensorflow/tensorflow/blob/8f7b60ee8c0206a2c99802e3a4d1bb55d2bc0624/tensorflow/core/kernels/count_ops.cc#L27)) data structure. If the `shape` tensor has more than one element, `num_batches` is the first value in `shape`. Ensuring that the `dense_shape` argument is a valid tensor shape (that is, all elements are non-negative) solves this issue. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2 and TensorFlow 2.3.3.
Severity
CWE
- CWE-131 - Incorrect Calculation of Buffer Size
Assigner
References
2 references
| URL | Tags |
|---|---|
| https://github.com/tensorflow/tensorflow/security… | x_refsource_CONFIRM |
| https://github.com/tensorflow/tensorflow/commit/c… | x_refsource_MISC |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| tensorflow | tensorflow |
Affected:
< 2.3.3
Affected: >= 2.4.0, < 2.4.2 |
guessed |
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},
{
"source": "af854a3a-2127-422b-91ae-364da2661108",
"tags": [
"Exploit",
"Patch",
"Third Party Advisory"
],
"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hr84-fqvp-48mm"
}
],
"sourceIdentifier": "security-advisories@github.com",
"vulnStatus": "Modified",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-131"
}
],
"source": "security-advisories@github.com",
"type": "Primary"
}
]
}
},
"redhat_vex": {
"current_release_date": "2026-03-27T14:17:37+00:00",
"cve": "CVE-2021-29521",
"id": "CVE-2021-29521",
"initial_release_date": "2021-01-01T00:00:00+00:00",
"product_status:known_not_affected": "1",
"source": "Red Hat CSAF VEX",
"status": "final",
"title": "Segfault in SparseCountSparseOutput",
"url": "https://security.access.redhat.com/data/csaf/v2/vex/2021/cve-2021-29521.json",
"version": "3"
},
"suse_vex": {
"aggregate_severity": "important",
"current_release_date": "2025-02-17T05:33:14Z",
"cve": "CVE-2021-29521",
"id": "CVE-2021-29521",
"initial_release_date": "2023-02-15T03:43:25Z",
"source": "SUSE CSAF VEX",
"status": "interim",
"title": "SUSE CVE CVE-2021-29521",
"url": "https://ftp.suse.com/pub/projects/security/csaf-vex/cve-2021-29521.json",
"version": "5"
}
}
}
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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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