CVE-2026-42561 (GCVE-0-2026-42561)
Vulnerability from cvelistv5 – Published: 2026-05-13 20:55 – Updated: 2026-08-07 12:04
VLAI
EPSS
VEX
Title
Python-Multipart: Denial of Service via unbounded multipart part headers
Summary
Python-Multipart is a streaming multipart parser for Python. Prior to 0.0.27, python-multipart has a denial of service vulnerability in multipart part header parsing. When parsing multipart/form-data, MultipartParser previously had no limit on the number of part headers or the size of an individual part header. An attacker could send a request with either many repeated headers without terminating the header block or a single very large header value, causing excessive CPU work before request rejection or completion. This vulnerability is fixed in 0.0.27.
Severity
7.5 (High)
SSVC
Exploitation: none
Automatable: yes
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-05-14 16:04 UTC
CWE
Assigner
References
12 references
| URL | Tags |
|---|---|
| https://github.com/Kludex/python-multipart/securi… | x_refsource_CONFIRM |
| https://access.redhat.com/security/cve/CVE-2026-42561 | vdb-entryx_refsource_REDHAT |
| https://bugzilla.redhat.com/show_bug.cgi?id=2477309 | issue-trackingx_refsource_REDHAT |
| https://security.access.redhat.com/data/csaf/v2/v… | x_sadp-csaf-vex |
| https://access.redhat.com/errata/RHSA-2026:42132 | vendor-advisoryx_refsource_REDHAT |
| https://access.redhat.com/errata/RHSA-2026:42142 | vendor-advisoryx_refsource_REDHAT |
| https://access.redhat.com/errata/RHSA-2026:43038 | vendor-advisoryx_refsource_REDHAT |
| https://access.redhat.com/errata/RHSA-2026:37275 | vendor-advisoryx_refsource_REDHAT |
| https://access.redhat.com/errata/RHSA-2026:44696 | vendor-advisoryx_refsource_REDHAT |
| https://access.redhat.com/errata/RHSA-2026:51357 | vendor-advisoryx_refsource_REDHAT |
| https://access.redhat.com/errata/RHSA-2026:51194 | vendor-advisoryx_refsource_REDHAT |
| https://access.redhat.com/errata/RHSA-2026:51219 | vendor-advisoryx_refsource_REDHAT |
Impacted products
17 products
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| Kludex | python-multipart |
Affected:
< 0.0.27
|
guessed | |
| Red Hat | Red Hat Ansible Automation Platform 2.6 |
Unaffected:
1783832856 , < *
(rpm)
cpe:/a:redhat:ansible_automation_platform:2.6::el9 |
||
| Red Hat | Red Hat Ansible Automation Platform 2.6 |
Unaffected:
1783920640 , < *
(rpm)
cpe:/a:redhat:ansible_automation_platform:2.6::el9 |
||
| Red Hat | Red Hat Ansible Automation Platform 2.7 |
Unaffected:
1783923914 , < *
(rpm)
cpe:/a:redhat:ansible_automation_platform:2.7::el9 |
||
| Red Hat | Red Hat Ansible Automation Platform 2.7 |
Unaffected:
1783918403 , < *
(rpm)
cpe:/a:redhat:ansible_automation_platform:2.7::el9 |
||
| Red Hat | Red Hat Migration Toolkit for Applications 8.2 |
Unaffected:
1784109883 , < *
(rpm)
cpe:/a:redhat:migration_toolkit_applications:8.2::el9 |
||
| Red Hat | Red Hat OpenShift AI 3.3 |
Unaffected:
1782887848 , < *
(rpm)
cpe:/a:redhat:openshift_ai:3.3::el9 |
||
| Red Hat | Red Hat Satellite 6.17 |
Unaffected:
1784834402 , < *
(rpm)
cpe:/a:redhat:satellite:6.17::el9 |
||
| Red Hat | Red Hat Satellite 6.19 |
Unaffected:
1785929994 , < *
(rpm)
cpe:/a:redhat:satellite:6.19::el9 |
||
| Red Hat | Red Hat Satellite 6.19 |
Unaffected:
1785806554 , < *
(rpm)
cpe:/a:redhat:satellite:6.19::el9 |
||
| Red Hat | Exploit Intelligence |
cpe:/a:redhat:exploit_intelligence:0
|
||
| Red Hat | OpenShift Lightspeed |
cpe:/a:redhat:openshift_lightspeed
|
||
| Red Hat | Red Hat AI Inference Server |
cpe:/a:redhat:ai_inference_server:3
|
||
| Red Hat | Red Hat Ansible Automation Platform 2 |
cpe:/a:redhat:ansible_automation_platform:2
|
||
| Red Hat | Red Hat Enterprise Linux AI (RHEL AI) 3 |
cpe:/a:redhat:enterprise_linux_ai:3
|
||
| Red Hat | Red Hat OpenShift AI (RHOAI) |
cpe:/a:redhat:openshift_ai
|
||
| Red Hat | Red Hat Satellite 6 |
cpe:/a:redhat:satellite:6
|
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"time": "2026-05-13T22:01:05.319Z",
"value": "Reported to Red Hat."
},
{
"lang": "en",
"time": "2026-05-13T20:55:11.767Z",
"value": "Made public."
}
],
"title": "python-multipart: python-multipart: Denial of Service via excessive multipart part headers",
"x_adpType": "supplier",
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"content": {
"id": "CVE-2026-42561",
"options": [
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],
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"timestamp": "2026-05-14T16:04:33.782234Z",
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}
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Drag nodes to arrange them. Double-click a related vulnerability to open it, or a source to visit its link. The Physics button on the left tunes the layout and the simulation; the legend at the bottom left hides or shows nodes by kind and by sighting type.
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.