CVE-2026-21620

Feb. 20, 2026, 1:49 p.m.

2.3
Low

Description

Relative Path Traversal, Improper Isolation or Compartmentalization vulnerability in erlang otp erlang/otp (tftp_file modules), erlang otp inets (tftp_file modules), erlang otp tftp (tftp_file modules) allows Relative Path Traversal. This vulnerability is associated with program files lib/tftp/src/tftp_file.erl, src/tftp_file.erl. This issue affects otp: from 17.0, from 07b8f441ca711f9812fad9e9115bab3c3aa92f79; otp: from 5.10 before 7.0; otp: from 1.0.

Product(s) Impacted

Vendor Product Versions
Erlang
  • Otp
  • 17.0, <7.0, 1.0

Weaknesses

Common security weaknesses mapped to this vulnerability.

CWE-23
Relative Path Traversal
The product uses external input to construct a pathname that should be within a restricted directory, but it does not properly neutralize sequences such as ".." that can resolve to a location that is outside of that directory.

*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 erlang otp 17.0 / / / / / / /
a erlang otp <7.0 / / / / / / /
a erlang otp 1.0 / / / / / / /

CVSS Score

2.3 / 10

CVSS Data - 4.0

  • Attack Vector: NETWORK
  • Attack Complexity: LOW
  • Attack Requirements: PRESENT
  • Privileges Required: LOW
  • User Interaction: NONE
  • Scope:
  • Confidentiality Impact: LOW
  • Integrity Impact: LOW
  • Availability Impact: NONE
  • Exploit Maturity: NOT_DEFINED
  • CVSS:4.0/AV:N/AC:L/AT:P/PR:L/UI:N/VC:L/VI:L/VA:N/SC:N/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: Feb. 20, 2026, 11:15 a.m.
Last Modified: Feb. 20, 2026, 1:49 p.m.

Status : Awaiting Analysis

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

More info

Source

6b3ad84c-e1a6-4bf7-a703-f496b71e49db

*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.