CVE-2020-15204 (GCVE-0-2020-15204)
Vulnerability from cvelistv5 – Published: 2020-09-25 18:46 – Updated: 2024-08-04 13:08
VLAI
EPSS
VEX
Title
Segfault in Tensorflow
Summary
In eager mode, TensorFlow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1 does not set the session state. Hence, calling `tf.raw_ops.GetSessionHandle` or `tf.raw_ops.GetSessionHandleV2` results in a null pointer dereference In linked snippet, in eager mode, `ctx->session_state()` returns `nullptr`. Since code immediately dereferences this, we get a segmentation fault. The issue is patched in commit 9a133d73ae4b4664d22bd1aa6d654fec13c52ee1, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.
Severity
5.3 (Medium)
CWE
- CWE-476 - {"CWE-476":"NULL Pointer Dereference"}
Assigner
References
4 references
| URL | Tags |
|---|---|
| https://github.com/tensorflow/tensorflow/releases… | x_refsource_MISC |
| https://github.com/tensorflow/tensorflow/security… | x_refsource_CONFIRM |
| https://github.com/tensorflow/tensorflow/commit/9… | x_refsource_MISC |
| http://lists.opensuse.org/opensuse-security-annou… | vendor-advisoryx_refsource_SUSE |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| tensorflow | tensorflow |
Affected:
< 1.15.4
Affected: >= 2.0.0, < 2.0.3 Affected: >= 2.1.0, < 2.1.2 Affected: >= 2.2.0, < 2.2.1 Affected: >= 2.3.0, < 2.3.1 |
guessed |
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"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:L",
"version": "3.1"
},
"exploitabilityScore": 3.9,
"impactScore": 1.4,
"source": "security-advisories@github.com",
"type": "Secondary"
},
{
"cvssData": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "LOW",
"baseScore": 5.3,
"baseSeverity": "MEDIUM",
"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:L",
"version": "3.1"
},
"exploitabilityScore": 3.9,
"impactScore": 1.4,
"source": "nvd@nist.gov",
"type": "Primary"
}
]
},
"published": "2020-09-25T19:15:15.713",
"references": [
{
"source": "security-advisories@github.com",
"tags": [
"Mailing List",
"Third Party Advisory"
],
"url": "http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html"
},
{
"source": "security-advisories@github.com",
"tags": [
"Patch",
"Third Party Advisory"
],
"url": "https://github.com/tensorflow/tensorflow/commit/9a133d73ae4b4664d22bd1aa6d654fec13c52ee1"
},
{
"source": "security-advisories@github.com",
"tags": [
"Third Party Advisory"
],
"url": "https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1"
},
{
"source": "security-advisories@github.com",
"tags": [
"Exploit",
"Third Party Advisory"
],
"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-q8gv-q7wr-9jf8"
},
{
"source": "af854a3a-2127-422b-91ae-364da2661108",
"tags": [
"Mailing List",
"Third Party Advisory"
],
"url": "http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html"
},
{
"source": "af854a3a-2127-422b-91ae-364da2661108",
"tags": [
"Patch",
"Third Party Advisory"
],
"url": "https://github.com/tensorflow/tensorflow/commit/9a133d73ae4b4664d22bd1aa6d654fec13c52ee1"
},
{
"source": "af854a3a-2127-422b-91ae-364da2661108",
"tags": [
"Third Party Advisory"
],
"url": "https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1"
},
{
"source": "af854a3a-2127-422b-91ae-364da2661108",
"tags": [
"Exploit",
"Third Party Advisory"
],
"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-q8gv-q7wr-9jf8"
}
],
"sourceIdentifier": "security-advisories@github.com",
"vulnStatus": "Modified",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-476"
}
],
"source": "security-advisories@github.com",
"type": "Primary"
}
]
}
},
"suse_vex": {
"aggregate_severity": "moderate",
"current_release_date": "2025-03-15T09:51:43Z",
"cve": "CVE-2020-15204",
"id": "CVE-2020-15204",
"initial_release_date": "2023-02-15T03:56:58Z",
"product_status:recommended": "24",
"source": "SUSE CSAF VEX",
"status": "interim",
"title": "SUSE CVE CVE-2020-15204",
"url": "https://ftp.suse.com/pub/projects/security/csaf-vex/cve-2020-15204.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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