GHSA-MQQC-3GQH-H2X8
Vulnerability from github – Published: 2026-01-05 23:09 – Updated: 2026-01-06 16:06
VLAI
Summary
AIOHTTP has unicode match groups in regexes for ASCII protocol elements
Details
Summary
The parser allows non-ASCII decimals to be present in the Range header.
Impact
There is no known impact, but there is the possibility that there's a method to exploit a request smuggling vulnerability.
Patch: https://github.com/aio-libs/aiohttp/commit/c7b7a044f88c71cefda95ec75cdcfaa4792b3b96
Severity
{
"affected": [
{
"database_specific": {
"last_known_affected_version_range": "\u003c= 3.13.2"
},
"package": {
"ecosystem": "PyPI",
"name": "aiohttp"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "3.13.3"
}
],
"type": "ECOSYSTEM"
}
]
}
],
"aliases": [
"CVE-2025-69225"
],
"database_specific": {
"cwe_ids": [
"CWE-444"
],
"github_reviewed": true,
"github_reviewed_at": "2026-01-05T23:09:30Z",
"nvd_published_at": "2026-01-06T00:15:47Z",
"severity": "LOW"
},
"details": "### Summary\n\nThe parser allows non-ASCII decimals to be present in the Range header.\n\n### Impact\n\nThere is no known impact, but there is the possibility that there\u0027s a method to exploit a request smuggling vulnerability.\n\n----\n\nPatch: https://github.com/aio-libs/aiohttp/commit/c7b7a044f88c71cefda95ec75cdcfaa4792b3b96",
"id": "GHSA-mqqc-3gqh-h2x8",
"modified": "2026-01-06T16:06:44Z",
"published": "2026-01-05T23:09:30Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/aio-libs/aiohttp/security/advisories/GHSA-mqqc-3gqh-h2x8"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2025-69225"
},
{
"type": "WEB",
"url": "https://github.com/aio-libs/aiohttp/commit/c7b7a044f88c71cefda95ec75cdcfaa4792b3b96"
},
{
"type": "PACKAGE",
"url": "https://github.com/aio-libs/aiohttp"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:N/VI:L/VA:N/SC:N/SI:N/SA:N/E:U",
"type": "CVSS_V4"
}
],
"summary": "AIOHTTP has unicode match groups in regexes for ASCII protocol elements"
}
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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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