CVE-2021-41220 (GCVE-0-2021-41220)
Vulnerability from cvelistv5 – Published: 2021-11-05 22:20 – Updated: 2024-08-04 03:08
VLAI
EPSS
VEX
Title
Use after free in `CollectiveReduceV2`
Summary
TensorFlow is an open source platform for machine learning. In affected versions the async implementation of `CollectiveReduceV2` suffers from a memory leak and a use after free. This occurs due to the asynchronous computation and the fact that objects that have been `std::move()`d from are still accessed. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, as this version is the only one that is also affected.
Severity
7.8 (High)
CWE
- CWE-416 - Use After Free
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:
>= 2.6.0, < 2.6.1
|
guessed |
{
"containers": {
"adp": [
{
"providerMetadata": {
"dateUpdated": "2024-08-04T03:08:31.404Z",
"orgId": "af854a3a-2127-422b-91ae-364da2661108",
"shortName": "CVE"
},
"references": [
{
"tags": [
"x_refsource_CONFIRM",
"x_transferred"
],
"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-gpfh-jvf9-7wg5"
},
{
"tags": [
"x_refsource_MISC",
"x_transferred"
],
"url": "https://github.com/tensorflow/tensorflow/commit/ca38dab9d3ee66c5de06f11af9a4b1200da5ef75"
}
],
"title": "CVE Program Container"
}
],
"cna": {
"affected": [
{
"product": "tensorflow",
"vendor": "tensorflow",
"versions": [
{
"status": "affected",
"version": "\u003e= 2.6.0, \u003c 2.6.1"
}
]
}
],
"descriptions": [
{
"lang": "en",
"value": "TensorFlow is an open source platform for machine learning. In affected versions the async implementation of `CollectiveReduceV2` suffers from a memory leak and a use after free. This occurs due to the asynchronous computation and the fact that objects that have been `std::move()`d from are still accessed. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, as this version is the only one that is also affected."
}
],
"metrics": [
{
"cvssV3_1": {
"attackComplexity": "LOW",
"attackVector": "LOCAL",
"availabilityImpact": "HIGH",
"baseScore": 7.8,
"baseSeverity": "HIGH",
"confidentialityImpact": "HIGH",
"integrityImpact": "HIGH",
"privilegesRequired": "LOW",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H",
"version": "3.1"
}
}
],
"problemTypes": [
{
"descriptions": [
{
"cweId": "CWE-416",
"description": "CWE-416: Use After Free",
"lang": "en",
"type": "CWE"
}
]
}
],
"providerMetadata": {
"dateUpdated": "2021-11-05T22:20:12.000Z",
"orgId": "a0819718-46f1-4df5-94e2-005712e83aaa",
"shortName": "GitHub_M"
},
"references": [
{
"tags": [
"x_refsource_CONFIRM"
],
"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-gpfh-jvf9-7wg5"
},
{
"tags": [
"x_refsource_MISC"
],
"url": "https://github.com/tensorflow/tensorflow/commit/ca38dab9d3ee66c5de06f11af9a4b1200da5ef75"
}
],
"source": {
"advisory": "GHSA-gpfh-jvf9-7wg5",
"discovery": "UNKNOWN"
},
"title": "Use after free in `CollectiveReduceV2`",
"x_legacyV4Record": {
"CVE_data_meta": {
"ASSIGNER": "security-advisories@github.com",
"ID": "CVE-2021-41220",
"STATE": "PUBLIC",
"TITLE": "Use after free in `CollectiveReduceV2`"
},
"affects": {
"vendor": {
"vendor_data": [
{
"product": {
"product_data": [
{
"product_name": "tensorflow",
"version": {
"version_data": [
{
"version_value": "\u003e= 2.6.0, \u003c 2.6.1"
}
]
}
}
]
},
"vendor_name": "tensorflow"
}
]
}
},
"data_format": "MITRE",
"data_type": "CVE",
"data_version": "4.0",
"description": {
"description_data": [
{
"lang": "eng",
"value": "TensorFlow is an open source platform for machine learning. In affected versions the async implementation of `CollectiveReduceV2` suffers from a memory leak and a use after free. This occurs due to the asynchronous computation and the fact that objects that have been `std::move()`d from are still accessed. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, as this version is the only one that is also affected."
}
]
},
"impact": {
"cvss": {
"attackComplexity": "LOW",
"attackVector": "LOCAL",
"availabilityImpact": "HIGH",
"baseScore": 7.8,
"baseSeverity": "HIGH",
"confidentialityImpact": "HIGH",
"integrityImpact": "HIGH",
"privilegesRequired": "LOW",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H",
"version": "3.1"
}
},
"problemtype": {
"problemtype_data": [
{
"description": [
{
"lang": "eng",
"value": "CWE-416: Use After Free"
}
]
}
]
},
"references": {
"reference_data": [
{
"name": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-gpfh-jvf9-7wg5",
"refsource": "CONFIRM",
"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-gpfh-jvf9-7wg5"
},
{
"name": "https://github.com/tensorflow/tensorflow/commit/ca38dab9d3ee66c5de06f11af9a4b1200da5ef75",
"refsource": "MISC",
"url": "https://github.com/tensorflow/tensorflow/commit/ca38dab9d3ee66c5de06f11af9a4b1200da5ef75"
}
]
},
"source": {
"advisory": "GHSA-gpfh-jvf9-7wg5",
"discovery": "UNKNOWN"
}
}
}
},
"cveMetadata": {
"assignerOrgId": "a0819718-46f1-4df5-94e2-005712e83aaa",
"assignerShortName": "GitHub_M",
"cveId": "CVE-2021-41220",
"datePublished": "2021-11-05T22:20:12.000Z",
"dateReserved": "2021-09-15T00:00:00.000Z",
"dateUpdated": "2024-08-04T03:08:31.404Z",
"state": "PUBLISHED"
},
"dataType": "CVE_RECORD",
"dataVersion": "5.1",
"vulnerability-lookup:meta": {
"epss": {
"cve": "CVE-2021-41220",
"date": "2026-09-23",
"epss": "0.00211",
"percentile": "0.11652"
},
"fkie_nvd": {
"configurations": [
{
"nodes": [
{
"cpeMatch": [
{
"criteria": "cpe:2.3:a:google:tensorflow:*:*:*:*:*:*:*:*",
"matchCriteriaId": "5D68D8D1-DB27-4395-9D3D-2BED901B852C",
"versionEndExcluding": "2.6.1",
"versionStartIncluding": "2.6.0",
"vulnerable": true
},
{
"criteria": "cpe:2.3:a:google:tensorflow:2.7.0:rc0:*:*:*:*:*:*",
"matchCriteriaId": "A58EDA5C-66D6-46F1-962E-60AFB7C784A7",
"vulnerable": true
},
{
"criteria": "cpe:2.3:a:google:tensorflow:2.7.0:rc1:*:*:*:*:*:*",
"matchCriteriaId": "89522760-C2DF-400D-9624-626D8F160CBA",
"vulnerable": true
}
],
"negate": false,
"operator": "OR"
}
]
}
],
"descriptions": [
{
"lang": "en",
"value": "TensorFlow is an open source platform for machine learning. In affected versions the async implementation of `CollectiveReduceV2` suffers from a memory leak and a use after free. This occurs due to the asynchronous computation and the fact that objects that have been `std::move()`d from are still accessed. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, as this version is the only one that is also affected."
},
{
"lang": "es",
"value": "TensorFlow es una plataforma de c\u00f3digo abierto para el aprendizaje autom\u00e1tico. En las versiones afectadas, la implementaci\u00f3n as\u00edncrona de \"CollectiveReduceV2\" sufre una perdida de memoria y un uso de memoria previamente liberada. Esto ocurre debido al c\u00e1lculo as\u00edncrono y al hecho de que se sigue accediendo a los objetos que han sido \"std::move()\"d. La correcci\u00f3n ser\u00e1 incluida en TensorFlow versi\u00f3n 2.7.0. Tambi\u00e9n seleccionaremos este commit en TensorFlow versi\u00f3n 2.6.1, ya que esta versi\u00f3n es la \u00fanica que tambi\u00e9n est\u00e1 afectada"
}
],
"id": "CVE-2021-41220",
"lastModified": "2024-11-21T06:25:48.677",
"metrics": {
"cvssMetricV2": [
{
"acInsufInfo": false,
"baseSeverity": "MEDIUM",
"cvssData": {
"accessComplexity": "LOW",
"accessVector": "LOCAL",
"authentication": "NONE",
"availabilityImpact": "PARTIAL",
"baseScore": 4.6,
"confidentialityImpact": "PARTIAL",
"integrityImpact": "PARTIAL",
"vectorString": "AV:L/AC:L/Au:N/C:P/I:P/A:P",
"version": "2.0"
},
"exploitabilityScore": 3.9,
"impactScore": 6.4,
"obtainAllPrivilege": false,
"obtainOtherPrivilege": false,
"obtainUserPrivilege": false,
"source": "nvd@nist.gov",
"type": "Primary",
"userInteractionRequired": false
}
],
"cvssMetricV31": [
{
"cvssData": {
"attackComplexity": "LOW",
"attackVector": "LOCAL",
"availabilityImpact": "HIGH",
"baseScore": 7.8,
"baseSeverity": "HIGH",
"confidentialityImpact": "HIGH",
"integrityImpact": "HIGH",
"privilegesRequired": "LOW",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H",
"version": "3.1"
},
"exploitabilityScore": 1.8,
"impactScore": 5.9,
"source": "security-advisories@github.com",
"type": "Secondary"
}
]
},
"published": "2021-11-05T23:15:08.350",
"references": [
{
"source": "security-advisories@github.com",
"tags": [
"Patch",
"Third Party Advisory"
],
"url": "https://github.com/tensorflow/tensorflow/commit/ca38dab9d3ee66c5de06f11af9a4b1200da5ef75"
},
{
"source": "security-advisories@github.com",
"tags": [
"Exploit",
"Patch",
"Third Party Advisory"
],
"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-gpfh-jvf9-7wg5"
},
{
"source": "af854a3a-2127-422b-91ae-364da2661108",
"tags": [
"Patch",
"Third Party Advisory"
],
"url": "https://github.com/tensorflow/tensorflow/commit/ca38dab9d3ee66c5de06f11af9a4b1200da5ef75"
},
{
"source": "af854a3a-2127-422b-91ae-364da2661108",
"tags": [
"Exploit",
"Patch",
"Third Party Advisory"
],
"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-gpfh-jvf9-7wg5"
}
],
"sourceIdentifier": "security-advisories@github.com",
"vulnStatus": "Modified",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-416"
}
],
"source": "security-advisories@github.com",
"type": "Primary"
}
]
},
"nvd": {
"cve": {
"affected": [
{
"affectedData": [
{
"product": "tensorflow",
"vendor": "tensorflow",
"versions": [
{
"status": "affected",
"version": "\u003e= 2.6.0, \u003c 2.6.1"
}
]
}
],
"source": "security-advisories@github.com"
}
],
"configurations": [
{
"nodes": [
{
"cpeMatch": [
{
"criteria": "cpe:2.3:a:google:tensorflow:*:*:*:*:*:*:*:*",
"matchCriteriaId": "5D68D8D1-DB27-4395-9D3D-2BED901B852C",
"versionEndExcluding": "2.6.1",
"versionStartIncluding": "2.6.0",
"vulnerable": true
},
{
"criteria": "cpe:2.3:a:google:tensorflow:2.7.0:rc0:*:*:*:*:*:*",
"matchCriteriaId": "A58EDA5C-66D6-46F1-962E-60AFB7C784A7",
"vulnerable": true
},
{
"criteria": "cpe:2.3:a:google:tensorflow:2.7.0:rc1:*:*:*:*:*:*",
"matchCriteriaId": "89522760-C2DF-400D-9624-626D8F160CBA",
"vulnerable": true
}
],
"negate": false,
"operator": "OR"
}
]
}
],
"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "TensorFlow is an open source platform for machine learning. In affected versions the async implementation of `CollectiveReduceV2` suffers from a memory leak and a use after free. This occurs due to the asynchronous computation and the fact that objects that have been `std::move()`d from are still accessed. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, as this version is the only one that is also affected."
},
{
"lang": "es",
"value": "TensorFlow es una plataforma de c\u00f3digo abierto para el aprendizaje autom\u00e1tico. En las versiones afectadas, la implementaci\u00f3n as\u00edncrona de \"CollectiveReduceV2\" sufre una perdida de memoria y un uso de memoria previamente liberada. Esto ocurre debido al c\u00e1lculo as\u00edncrono y al hecho de que se sigue accediendo a los objetos que han sido \"std::move()\"d. La correcci\u00f3n ser\u00e1 incluida en TensorFlow versi\u00f3n 2.7.0. Tambi\u00e9n seleccionaremos este commit en TensorFlow versi\u00f3n 2.6.1, ya que esta versi\u00f3n es la \u00fanica que tambi\u00e9n est\u00e1 afectada"
}
],
"id": "CVE-2021-41220",
"lastModified": "2026-06-17T04:08:07.313",
"metrics": {
"cvssMetricV2": [
{
"acInsufInfo": false,
"baseSeverity": "MEDIUM",
"cvssData": {
"accessComplexity": "LOW",
"accessVector": "LOCAL",
"authentication": "NONE",
"availabilityImpact": "PARTIAL",
"baseScore": 4.6,
"confidentialityImpact": "PARTIAL",
"integrityImpact": "PARTIAL",
"vectorString": "AV:L/AC:L/Au:N/C:P/I:P/A:P",
"version": "2.0"
},
"exploitabilityScore": 3.9,
"impactScore": 6.4,
"obtainAllPrivilege": false,
"obtainOtherPrivilege": false,
"obtainUserPrivilege": false,
"source": "nvd@nist.gov",
"type": "Primary",
"userInteractionRequired": false
}
],
"cvssMetricV31": [
{
"cvssData": {
"attackComplexity": "LOW",
"attackVector": "LOCAL",
"availabilityImpact": "HIGH",
"baseScore": 7.8,
"baseSeverity": "HIGH",
"confidentialityImpact": "HIGH",
"integrityImpact": "HIGH",
"privilegesRequired": "LOW",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H",
"version": "3.1"
},
"exploitabilityScore": 1.8,
"impactScore": 5.9,
"source": "security-advisories@github.com",
"type": "Secondary"
}
]
},
"published": "2021-11-05T23:15:08.350",
"references": [
{
"source": "security-advisories@github.com",
"tags": [
"Patch",
"Third Party Advisory"
],
"url": "https://github.com/tensorflow/tensorflow/commit/ca38dab9d3ee66c5de06f11af9a4b1200da5ef75"
},
{
"source": "security-advisories@github.com",
"tags": [
"Exploit",
"Patch",
"Third Party Advisory"
],
"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-gpfh-jvf9-7wg5"
},
{
"source": "af854a3a-2127-422b-91ae-364da2661108",
"tags": [
"Patch",
"Third Party Advisory"
],
"url": "https://github.com/tensorflow/tensorflow/commit/ca38dab9d3ee66c5de06f11af9a4b1200da5ef75"
},
{
"source": "af854a3a-2127-422b-91ae-364da2661108",
"tags": [
"Exploit",
"Patch",
"Third Party Advisory"
],
"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-gpfh-jvf9-7wg5"
}
],
"sourceIdentifier": "security-advisories@github.com",
"vulnStatus": "Modified",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-416"
}
],
"source": "security-advisories@github.com",
"type": "Secondary"
}
]
}
},
"redhat_vex": {
"current_release_date": "2026-03-27T13:31:20+00:00",
"cve": "CVE-2021-41220",
"id": "CVE-2021-41220",
"initial_release_date": "2021-01-01T00:00:00+00:00",
"product_status:known_not_affected": "1",
"source": "Red Hat CSAF VEX",
"status": "final",
"title": "Use after free in `CollectiveReduceV2`",
"url": "https://security.access.redhat.com/data/csaf/v2/vex/2021/cve-2021-41220.json",
"version": "3"
},
"suse_vex": {
"aggregate_severity": "important",
"current_release_date": "2025-03-15T08:39:52Z",
"cve": "CVE-2021-41220",
"id": "CVE-2021-41220",
"initial_release_date": "2023-02-15T03:37:33Z",
"product_status:recommended": "2",
"source": "SUSE CSAF VEX",
"status": "interim",
"title": "SUSE CVE CVE-2021-41220",
"url": "https://ftp.suse.com/pub/projects/security/csaf-vex/cve-2021-41220.json",
"version": "6"
}
}
}
Loading…
Loading…
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.
Loading…
Loading…
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.
Loading…
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.
Loading…