CVE-2026-53782

June 11, 2026, 8:50 p.m.

6.3
Medium

Description

Summarize before 0.17.0 contains a server-side request forgery vulnerability that allows attackers who control a podcast RSS feed to direct the host to fetch transcript content from loopback addresses, link-local addresses, RFC 1918 private ranges, or other reserved destinations by supplying malicious podcast:transcript URL values. Attackers can bypass protections through DNS rebinding and redirect-based techniques, as redirect targets are not revalidated and hostnames are not resolved before request dispatch, exposing internal service responses through the summarization flow.

Product(s) Impacted

Vendor Product Versions
Summarize
  • Summarize
  • *

Weaknesses

Common security weaknesses mapped to this vulnerability.

CWE-918
Server-Side Request Forgery (SSRF)
The web server receives a URL or similar request from an upstream component and retrieves the contents of this URL, but it does not sufficiently ensure that the request is being sent to the expected 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 summarize summarize / / / / / / / /

CVSS Score

6.3 / 10

CVSS Data - 4.0

  • Attack Vector: NETWORK
  • Attack Complexity: LOW
  • Attack Requirements: NONE
  • Privileges Required: NONE
  • User Interaction: PASSIVE
  • Scope:
  • Confidentiality Impact: NONE
  • Integrity Impact: NONE
  • Availability Impact: NONE
  • Exploit Maturity: NOT_DEFINED
  • CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:P/VC:N/VI:N/VA:N/SC:H/SI:N/SA:N/E:X/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: June 11, 2026, 8:16 p.m.
Last Modified: June 11, 2026, 8:50 p.m.

Status : Deferred

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

More info

*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.