SUSE-SU-2026:20919-1
Vulnerability from csaf_suse - Published: 2026-03-20 10:55 - Updated: 2026-09-16 19:37Summary
Security update for python-tornado6
Severity
Important
Notes
Title of the patch: Security update for python-tornado6
Description of the patch: This update for python-tornado6 fixes the following issues:
- CVE-2026-31958: parsing large multipart bodies with many parts can cause a denial of service (bsc#1259553).
- incomplete validation of cookie attributes allows for injection of user-controlled values in other cookie attributes
(bsc#1259630).
Patchnames: SUSE-SLES-16.0-420
Terms of use: CSAF 2.0 data is provided by SUSE under the Creative Commons License 4.0 with Attribution (CC-BY-4.0).
7.5 (High)
Affected products
Recommended
8 products
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: SUSE Linux Enterprise Server 16.0:python313-tornado6-6.5-160000.4.1.aarch64 | — |
Vendor Fix
|
|
| Unresolved product id: SUSE Linux Enterprise Server 16.0:python313-tornado6-6.5-160000.4.1.ppc64le | — |
Vendor Fix
|
|
| Unresolved product id: SUSE Linux Enterprise Server 16.0:python313-tornado6-6.5-160000.4.1.s390x | — |
Vendor Fix
|
|
| Unresolved product id: SUSE Linux Enterprise Server 16.0:python313-tornado6-6.5-160000.4.1.x86_64 | — |
Vendor Fix
|
|
| Unresolved product id: SUSE Linux Enterprise Server for SAP applications 16.0:python313-tornado6-6.5-160000.4.1.aarch64 | — |
Vendor Fix
|
|
| Unresolved product id: SUSE Linux Enterprise Server for SAP applications 16.0:python313-tornado6-6.5-160000.4.1.ppc64le | — |
Vendor Fix
|
|
| Unresolved product id: SUSE Linux Enterprise Server for SAP applications 16.0:python313-tornado6-6.5-160000.4.1.s390x | — |
Vendor Fix
|
|
| Unresolved product id: SUSE Linux Enterprise Server for SAP applications 16.0:python313-tornado6-6.5-160000.4.1.x86_64 | — |
Vendor Fix
|
Threats
Impact
important
References
9 references
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"vulnerabilities": [
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"notes": [
{
"category": "general",
"text": "Tornado is a Python web framework and asynchronous networking library. In versions of Tornado prior to 6.5.5, the only limit on the number of parts in multipart/form-data is the max_body_size setting (default 100MB). Since parsing occurs synchronously on the main thread, this creates the possibility of denial-of-service due to the cost of parsing very large multipart bodies with many parts. This vulnerability is fixed in 6.5.5.",
"title": "CVE description"
}
],
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"url": "https://bugzilla.suse.com/1259552"
}
],
"remediations": [
{
"category": "vendor_fix",
"details": "To install this SUSE Security Update use the SUSE recommended installation methods like YaST online_update or \"zypper patch\".\n",
"product_ids": [
"SUSE Linux Enterprise Server 16.0:python313-tornado6-6.5-160000.4.1.aarch64",
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]
}
],
"scores": [
{
"cvss_v3": {
"baseScore": 7.5,
"baseSeverity": "HIGH",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
"version": "3.1"
},
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"threats": [
{
"category": "impact",
"date": "2026-03-20T10:55:14Z",
"details": "important"
}
],
"title": "CVE-2026-31958"
}
]
}
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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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