CVE-2025-13836 (GCVE-0-2025-13836)
Vulnerability from cvelistv5 – Published: 2025-12-01 18:02 – Updated: 2026-03-03 14:41
VLAI
EPSS
VEX
Title
Excessive read buffering DoS in http.client
Summary
When reading an HTTP response from a server, if no read amount is specified, the default behavior will be to use Content-Length. This allows a malicious server to cause the client to read large amounts of data into memory, potentially causing OOM or other DoS.
Severity
SSVC
Exploitation: none
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-12-01 18:32 UTC
CWE
- CWE-400 - Uncontrolled Resource Consumption
Assigner
References
9 references
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| Python Software Foundation | CPython |
Affected:
0 , < 3.10.20
(python)
Affected: 3.11.0 , < 3.11.15 (python) Affected: 3.12.0 , < 3.12.13 (python) Affected: 3.13.0 , < 3.13.11 (python) Affected: 3.14.0 , < 3.14.1 (python) Affected: 3.15.0a1 , < 3.15.0a3 (python) |
guessed |
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"url": "https://github.com/python/cpython/commit/289f29b0fe38baf2d7cb5854f4bb573cc34a6a15"
},
{
"tags": [
"patch"
],
"url": "https://github.com/python/cpython/commit/14b1fdb0a94b96f86fc7b86671ea9582b8676628"
},
{
"tags": [
"patch"
],
"url": "https://github.com/python/cpython/commit/5dc101675fd22918facbbe0fecdc821502beaaf0"
},
{
"tags": [
"patch"
],
"url": "https://github.com/python/cpython/commit/afc40bdd3dd71f343fd9016f6d8eebbacbd6587c"
}
],
"source": {
"discovery": "UNKNOWN"
},
"title": "Excessive read buffering DoS in http.client",
"x_generator": {
"engine": "Vulnogram 0.5.0"
}
}
},
"cveMetadata": {
"assignerOrgId": "28c92f92-d60d-412d-b760-e73465c3df22",
"assignerShortName": "PSF",
"cveId": "CVE-2025-13836",
"datePublished": "2025-12-01T18:02:38.483Z",
"dateReserved": "2025-12-01T17:54:40.759Z",
"dateUpdated": "2026-03-03T14:41:48.702Z",
"state": "PUBLISHED"
},
"dataType": "CVE_RECORD",
"dataVersion": "5.2"
}
}
}
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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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