CVE-2025-61689

Oct. 10, 2025, 5:15 p.m.

8.7
High

Description

HTTP.jl is an HTTP client and server functionality for the Julia programming language. Prior to version 1.10.19, HTTP.jl did not validate header names/values for illegal characters, allowing CRLF-based header injection and response splitting. This enables HTTP response splitting and header injection, leading to cache poisoning, XSS, session fixation, and more. This issue is fixed in HTTP.jl `v1.10.19`.

Product(s) Impacted

Vendor Product Versions
Julia
  • Http.jl
  • <1.10.19, 1.10.19

Weaknesses

Common security weaknesses mapped to this vulnerability.

CWE-113
Improper Neutralization of CRLF Sequences in HTTP Headers ('HTTP Request/Response Splitting')
The product receives data from an HTTP agent/component (e.g., web server, proxy, browser, etc.), but it does not neutralize or incorrectly neutralizes CR and LF characters before the data is included in outgoing HTTP headers.

*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 julia http.jl <1.10.19 / / / / / / /
a julia http.jl 1.10.19 / / / / / / /

CVSS Score

8.7 / 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: HIGH
  • Availability Impact: NONE
  • Exploit Maturity: PROOF_OF_CONCEPT
  • CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:N/VI:H/VA:N/SC:N/SI:H/SA:N/E:P/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: Oct. 10, 2025, 5:15 p.m.
Last Modified: Oct. 10, 2025, 5:15 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.