FKIE_CVE-2020-15265
Vulnerability from fkie_nvd - Published: 2020-10-21 21:15 - Updated: 2026-06-17 02:567.5 (High) - CVSS:3.1/
| URL | Tags | ||
|---|---|---|---|
| security-advisories@github.com | https://github.com/tensorflow/tensorflow/commit/eccb7ec454e6617738554a255d77f08e60ee0808 | Patch, Third Party Advisory | |
| security-advisories@github.com | https://github.com/tensorflow/tensorflow/issues/42105 | Patch, Third Party Advisory | |
| security-advisories@github.com | https://github.com/tensorflow/tensorflow/security/advisories/GHSA-rrfp-j2mp-hq9c | Exploit, Patch, Third Party Advisory | |
| af854a3a-2127-422b-91ae-364da2661108 | https://github.com/tensorflow/tensorflow/commit/eccb7ec454e6617738554a255d77f08e60ee0808 | Patch, Third Party Advisory | |
| af854a3a-2127-422b-91ae-364da2661108 | https://github.com/tensorflow/tensorflow/issues/42105 | Patch, Third Party Advisory | |
| af854a3a-2127-422b-91ae-364da2661108 | https://github.com/tensorflow/tensorflow/security/advisories/GHSA-rrfp-j2mp-hq9c | Exploit, Patch, Third Party Advisory |
| Vendor | Product | Version | |
|---|---|---|---|
| tensorflow | * |
{
"affected": [
{
"affectedData": [
{
"product": "tensorflow",
"vendor": "tensorflow",
"versions": [
{
"status": "affected",
"version": "\u003c 2.4.0"
}
]
}
],
"source": "security-advisories@github.com"
}
],
"configurations": [
{
"nodes": [
{
"cpeMatch": [
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"criteria": "cpe:2.3:a:google:tensorflow:*:*:*:*:-:*:*:*",
"matchCriteriaId": "837BA051-B044-46A7-BCDF-81785C1E1FF9",
"versionEndExcluding": "2.4.0",
"vulnerable": true
}
],
"negate": false,
"operator": "OR"
}
]
}
],
"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "In Tensorflow before version 2.4.0, an attacker can pass an invalid `axis` value to `tf.quantization.quantize_and_dequantize`. This results in accessing a dimension outside the rank of the input tensor in the C++ kernel implementation. However, dim_size only does a DCHECK to validate the argument and then uses it to access the corresponding element of an array. Since in normal builds, `DCHECK`-like macros are no-ops, this results in segfault and access out of bounds of the array. The issue is patched in eccb7ec454e6617738554a255d77f08e60ee0808 and TensorFlow 2.4.0 will be released containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved."
},
{
"lang": "es",
"value": "En Tensorflow versiones anteriores a 2.4.0, un atacante puede pasar un valor de \"axis\" no v\u00e1lido a la funci\u00f3n \"tf.quantization.quantize_and_dequantize\".\u0026#xa0;Esto resulta en el acceso a una dimensi\u00f3n fuera del rango del tensor de entrada en la implementaci\u00f3n del kernel de C++.\u0026#xa0;Sin embargo, dim_size solo hace un DCHECK para comprobar el argumento y luego lo usa para acceder al elemento correspondiente de una matriz.\u0026#xa0;Dado que en las compilaciones normales, las macros similares a \"DCHECK\" no son operativas, esto resulta en un fallo de segmentaci\u00f3n y un acceso fuera de los l\u00edmites de la matriz.\u0026#xa0;El problema est\u00e1 parcheado en eccb7ec454e6617738554a255d77f08e60ee0808 y TensorFlow versi\u00f3n 2.4.0 se publicar\u00e1 con el parche.\u0026#xa0;Los paquetes nocturnos de TensorFlow despu\u00e9s de esta confirmaci\u00f3n tambi\u00e9n resolver\u00e1n el problema"
}
],
"id": "CVE-2020-15265",
"lastModified": "2026-06-17T02:56:23.503",
"metrics": {
"cvssMetricV2": [
{
"acInsufInfo": false,
"baseSeverity": "MEDIUM",
"cvssData": {
"accessComplexity": "LOW",
"accessVector": "NETWORK",
"authentication": "NONE",
"availabilityImpact": "PARTIAL",
"baseScore": 5.0,
"confidentialityImpact": "NONE",
"integrityImpact": "NONE",
"vectorString": "AV:N/AC:L/Au:N/C:N/I:N/A:P",
"version": "2.0"
},
"exploitabilityScore": 10.0,
"impactScore": 2.9,
"obtainAllPrivilege": false,
"obtainOtherPrivilege": false,
"obtainUserPrivilege": false,
"source": "nvd@nist.gov",
"type": "Primary",
"userInteractionRequired": false
}
],
"cvssMetricV31": [
{
"cvssData": {
"attackComplexity": "HIGH",
"attackVector": "NETWORK",
"availabilityImpact": "HIGH",
"baseScore": 5.9,
"baseSeverity": "MEDIUM",
"confidentialityImpact": "NONE",
"integrityImpact": "NONE",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H",
"version": "3.1"
},
"exploitabilityScore": 2.2,
"impactScore": 3.6,
"source": "security-advisories@github.com",
"type": "Secondary"
},
{
"cvssData": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "HIGH",
"baseScore": 7.5,
"baseSeverity": "HIGH",
"confidentialityImpact": "NONE",
"integrityImpact": "NONE",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
"version": "3.1"
},
"exploitabilityScore": 3.9,
"impactScore": 3.6,
"source": "nvd@nist.gov",
"type": "Primary"
}
]
},
"published": "2020-10-21T21:15:12.257",
"references": [
{
"source": "security-advisories@github.com",
"tags": [
"Patch",
"Third Party Advisory"
],
"url": "https://github.com/tensorflow/tensorflow/commit/eccb7ec454e6617738554a255d77f08e60ee0808"
},
{
"source": "security-advisories@github.com",
"tags": [
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],
"url": "https://github.com/tensorflow/tensorflow/issues/42105"
},
{
"source": "security-advisories@github.com",
"tags": [
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"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-rrfp-j2mp-hq9c"
},
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"source": "af854a3a-2127-422b-91ae-364da2661108",
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"Third Party Advisory"
],
"url": "https://github.com/tensorflow/tensorflow/commit/eccb7ec454e6617738554a255d77f08e60ee0808"
},
{
"source": "af854a3a-2127-422b-91ae-364da2661108",
"tags": [
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"url": "https://github.com/tensorflow/tensorflow/issues/42105"
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"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-rrfp-j2mp-hq9c"
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],
"sourceIdentifier": "security-advisories@github.com",
"vulnStatus": "Modified",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-125"
}
],
"source": "security-advisories@github.com",
"type": "Secondary"
}
]
}
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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
How durations are calculated
Following the VTEM framework (Black Hat USA 2015), time to patch starts when the vendor or a coordinator was informed, total time to patch at discovery, and disclosure to patch at public disclosure; vendor response time runs from the report to its acknowledgement. Time to exploit and time unpatched start at the earliest discovery or report, falling back to public disclosure. Unpatched time ends at the first recorded patch, or the calculation time when no patch is recorded. Exploitation to patch shows the interval between those two events; a published proof of concept is a public exploit, not exploitation.
Each result shows its source dates. A negative interval means the end event came first; when date-only inputs leave the order open, the result says so. Date-only inputs are approximate. The earliest evidence wins: KEV report dates and sightings are labelled as such. Records describing another vulnerability are listed but never calculated from, and a PoC alone does not establish exploitation. Missing patch evidence does not prove that no patch exists. Filters below change the event list only.
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