CVE-2022-29204 (GCVE-0-2022-29204)
Vulnerability from cvelistv5 – Published: 2022-05-20 22:40 – Updated: 2025-04-22 17:58
VLAI
EPSS
VEX
Title
Missing validation causes denial of service in TensorFlow via `Conv3DBackpropFilterV2`
Summary
TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of `tf.raw_ops.UnsortedSegmentJoin` does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack. The code assumes `num_segments` is a positive scalar but there is no validation. Since this value is used to allocate the output tensor, a negative value would result in a `CHECK`-failure (assertion failure), as per TFSA-2021-198. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.
Severity
5.5 (Medium)
SSVC
Exploitation: poc
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-04-22 15:46 UTC
Assigner
GitHub_M
CNA
References
9 references
| URL | Tags |
|---|---|
| https://github.com/tensorflow/tensorflow/blob/mas… | x_refsource_MISC |
| https://github.com/tensorflow/tensorflow/releases… | x_refsource_MISC |
| https://github.com/tensorflow/tensorflow/releases… | x_refsource_MISC |
| https://github.com/tensorflow/tensorflow/releases… | x_refsource_MISC |
| https://github.com/tensorflow/tensorflow/releases… | x_refsource_MISC |
| https://github.com/tensorflow/tensorflow/security… | x_refsource_CONFIRM |
| https://github.com/tensorflow/tensorflow/commit/2… | x_refsource_MISC |
| https://github.com/tensorflow/tensorflow/commit/8… | x_refsource_MISC |
| https://github.com/tensorflow/tensorflow/blob/f3b… | x_refsource_MISC |
Impacted products
1 product
| Vendor | Product | Version | |
|---|---|---|---|
| tensorflow | tensorflow |
Affected:
< 2.6.4
Affected: >= 2.7.0rc0, < 2.7.2 Affected: >= 2.8.0rc0, < 2.8.1 Affected: >= 2.9.0rc0, < 2.9.0 |
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"data_version": "4.0",
"description": {
"description_data": [
{
"lang": "eng",
"value": "TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of `tf.raw_ops.UnsortedSegmentJoin` does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack. The code assumes `num_segments` is a positive scalar but there is no validation. Since this value is used to allocate the output tensor, a negative value would result in a `CHECK`-failure (assertion failure), as per TFSA-2021-198. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue."
}
]
},
"impact": {
"cvss": {
"attackComplexity": "LOW",
"attackVector": "LOCAL",
"availabilityImpact": "HIGH",
"baseScore": 5.5,
"baseSeverity": "MEDIUM",
"confidentialityImpact": "NONE",
"integrityImpact": "NONE",
"privilegesRequired": "LOW",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H",
"version": "3.1"
}
},
"problemtype": {
"problemtype_data": [
{
"description": [
{
"lang": "eng",
"value": "CWE-191: Integer Underflow (Wrap or Wraparound)"
}
]
},
{
"description": [
{
"lang": "eng",
"value": "CWE-20: Improper Input Validation"
}
]
}
]
},
"references": {
"reference_data": [
{
"name": "https://github.com/tensorflow/tensorflow/blob/master/tensorflow/security/advisory/tfsa-2021-198.md",
"refsource": "MISC",
"url": "https://github.com/tensorflow/tensorflow/blob/master/tensorflow/security/advisory/tfsa-2021-198.md"
},
{
"name": "https://github.com/tensorflow/tensorflow/releases/tag/v2.6.4",
"refsource": "MISC",
"url": "https://github.com/tensorflow/tensorflow/releases/tag/v2.6.4"
},
{
"name": "https://github.com/tensorflow/tensorflow/releases/tag/v2.7.2",
"refsource": "MISC",
"url": "https://github.com/tensorflow/tensorflow/releases/tag/v2.7.2"
},
{
"name": "https://github.com/tensorflow/tensorflow/releases/tag/v2.8.1",
"refsource": "MISC",
"url": "https://github.com/tensorflow/tensorflow/releases/tag/v2.8.1"
},
{
"name": "https://github.com/tensorflow/tensorflow/releases/tag/v2.9.0",
"refsource": "MISC",
"url": "https://github.com/tensorflow/tensorflow/releases/tag/v2.9.0"
},
{
"name": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hx9q-2mx4-m4pg",
"refsource": "CONFIRM",
"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hx9q-2mx4-m4pg"
},
{
"name": "https://github.com/tensorflow/tensorflow/commit/20cb18724b0bf6c09071a3f53434c4eec53cc147",
"refsource": "MISC",
"url": "https://github.com/tensorflow/tensorflow/commit/20cb18724b0bf6c09071a3f53434c4eec53cc147"
},
{
"name": "https://github.com/tensorflow/tensorflow/commit/84563f265f28b3c36a15335c8b005d405260e943",
"refsource": "MISC",
"url": "https://github.com/tensorflow/tensorflow/commit/84563f265f28b3c36a15335c8b005d405260e943"
},
{
"name": "https://github.com/tensorflow/tensorflow/blob/f3b9bf4c3c0597563b289c0512e98d4ce81f886e/tensorflow/core/kernels/unsorted_segment_join_op.cc#L83-L14",
"refsource": "MISC",
"url": "https://github.com/tensorflow/tensorflow/blob/f3b9bf4c3c0597563b289c0512e98d4ce81f886e/tensorflow/core/kernels/unsorted_segment_join_op.cc#L83-L14"
}
]
},
"source": {
"advisory": "GHSA-hx9q-2mx4-m4pg",
"discovery": "UNKNOWN"
}
}
}
},
"cveMetadata": {
"assignerOrgId": "a0819718-46f1-4df5-94e2-005712e83aaa",
"assignerShortName": "GitHub_M",
"cveId": "CVE-2022-29204",
"datePublished": "2022-05-20T22:40:13.000Z",
"dateReserved": "2022-04-13T00:00:00.000Z",
"dateUpdated": "2025-04-22T17:58:11.932Z",
"state": "PUBLISHED"
},
"dataType": "CVE_RECORD",
"dataVersion": "5.1"
}
}
}
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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.
Vulnerability timeline
Sightings and recorded dates, grouped by day · UTC
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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.