CVE-2025-69230

Jan. 6, 2026, 12:15 a.m.

2.7
Low

Description

AIOHTTP is an asynchronous HTTP client/server framework for asyncio and Python. In versions 3.13.2 and below, reading multiple invalid cookies can lead to a logging storm. If the cookies attribute is accessed in an application, then an attacker may be able to trigger a storm of warning-level logs using a specially crafted Cookie header. This issue is fixed in 3.13.3.

Product(s) Impacted

Vendor Product Versions
Aiohttp
  • Aiohttp
  • <3.13.3

Weaknesses

Common security weaknesses mapped to this vulnerability.

CWE-779
Logging of Excessive Data
The product logs too much information, making log files hard to process and possibly hindering recovery efforts or forensic analysis after an attack.

*CPE(s)

Affected systems and software identified for this CVE.

Type Vendor Product Version Update Edition Language Software Edition Target Software Target Hardware Other Information
a aiohttp aiohttp <3.13.3 / / / / / /

References

https://github.com/aio-libs/aiohttp/commit/64629a0834f94e46d9881f4e99c41a137e1f3326
https://github.com/aio-libs/aiohttp/security/advisories/GHSA-fh55-r93g-j68g

Tags

security-advisories@github.com
CWE-779
2026-01-06
CVE-2025-69230

CVSS Score

2.7 / 10

CVSS Data - 4.0

  • Attack Vector: NETWORK
  • Attack Complexity: LOW
  • Attack Requirements: NONE
  • Privileges Required: NONE
  • User Interaction: NONE
  • Scope:
  • Confidentiality Impact: NONE
  • Integrity Impact: LOW
  • Availability Impact: LOW
  • Exploit Maturity: UNREPORTED

    • CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:N/VI:L/VA:L/SC:N/SI:N/SA:N/E:U/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:X

    View Vector String

Timeline

Published: Jan. 6, 2026, 12:15 a.m.
Last Modified: Jan. 6, 2026, 12:15 a.m.

Status : Received

CVE has been recently published to the CVE List and has been received by the NVD.

More info

Source

security-advisories@github.com

*Disclaimer: Some vulnerabilities do not have an associated CPE. To enhance the data, we use AI to infer CPEs based on CVE details. This is an automated process and might not always be accurate.