CVE-2025-62706

Oct. 22, 2025, 10:15 p.m.

6.5
Medium

Description

Authlib is a Python library which builds OAuth and OpenID Connect servers. Prior to version 1.6.5, Authlib’s JWE zip=DEF path performs unbounded DEFLATE decompression. A very small ciphertext can expand into tens or hundreds of megabytes on decrypt, allowing an attacker who can supply decryptable tokens to exhaust memory and CPU and cause denial of service. This issue has been patched in version 1.6.5. Workarounds for this issue involve rejecting or stripping zip=DEF for inbound JWEs at the application boundary, forking and add a bounded decompression guard via decompressobj().decompress(data, MAX_SIZE)) and returning an error when output exceeds a safe limit, or enforcing strict maximum token sizes and fail fast on oversized inputs; combine with rate limiting.

Product(s) Impacted

Vendor Product Versions
Authlib
  • Authlib
  • <1.6.5

Weaknesses

Common security weaknesses mapped to this vulnerability.

CWE-400
Uncontrolled Resource Consumption
The product does not properly control the allocation and maintenance of a limited resource, thereby enabling an actor to influence the amount of resources consumed, eventually leading to the exhaustion of available resources.

*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 authlib authlib <1.6.5 / / / / / / /

CVSS Score

6.5 / 10

CVSS Data - 3.1

  • Attack Vector: NETWORK
  • Attack Complexity: LOW
  • Privileges Required: LOW
  • Scope: UNCHANGED
  • Confidentiality Impact: NONE
  • Integrity Impact: NONE
  • Availability Impact: HIGH
  • CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H

    View Vector String

Timeline

Published: Oct. 22, 2025, 10:15 p.m.
Last Modified: Oct. 22, 2025, 10:15 p.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.