CVE-2025-66373

Dec. 16, 2025, 8:58 p.m.

4.8
Medium

Description

Akamai Ghost on Akamai CDN edge servers before 2025-11-17 has a chunked request body processing error that can result in HTTP request smuggling. When Akamai Ghost receives an invalid chunked body that includes a chunk size different from the actual size of the following chunk data, under certain circumstances, Akamai Ghost erroneously forwards the invalid request and subsequent superfluous bytes to the origin server. An attacker could hide a smuggled request in these superfluous bytes. Whether this is exploitable depends on the origin server's behavior and how it processes the invalid request it receives from Akamai Ghost.

Product(s) Impacted

Vendor Product Versions
Akamai
  • Akamaighost
  • *

Weaknesses

Common security weaknesses mapped to this vulnerability.

CWE-444
Inconsistent Interpretation of HTTP Requests ('HTTP Request/Response Smuggling')
The product acts as an intermediary HTTP agent (such as a proxy or firewall) in the data flow between two entities such as a client and server, but it does not interpret malformed HTTP requests or responses in ways that are consistent with how the messages will be processed by those entities that are at the ultimate destination.

*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 akamai akamaighost / / / / / / / /

CVSS Score

4.8 / 10

CVSS Data - 3.1

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

    View Vector String

Timeline

Published: Dec. 4, 2025, 5:15 p.m.
Last Modified: Dec. 16, 2025, 8:58 p.m.

Status : Analyzed

CVE has had analysis completed and all data associations made.

More info

Source

cve@mitre.org

*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.