CVE-2025-30211

March 28, 2025, 6:11 p.m.

7.5
High

Description

Erlang/OTP is a set of libraries for the Erlang programming language. Prior to versions OTP-27.3.1, 26.2.5.10, and 25.3.2.19, a maliciously formed KEX init message can result with high memory usage. Implementation does not verify RFC specified limits on algorithm names (64 characters) provided in KEX init message. Big KEX init packet may lead to inefficient processing of the error data. As a result, large amount of memory will be allocated for processing malicious data. Versions OTP-27.3.1, OTP-26.2.5.10, and OTP-25.3.2.19 fix the issue. Some workarounds are available. One may set option `parallel_login` to `false` and/or reduce the `max_sessions` option.

Product(s) Impacted

Vendor Product Versions
Erlang
  • Otp
  • <25.*, <26.2.5.10, <27.3.1

Weaknesses

Common security weaknesses mapped to this vulnerability.

CWE-789
Memory Allocation with Excessive Size Value
The product allocates memory based on an untrusted, large size value, but it does not ensure that the size is within expected limits, allowing arbitrary amounts of memory to be allocated.

*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 <25.* / / / / / / /
a erlang otp <26.2.5.10 / / / / / / /
a erlang otp <27.3.1 / / / / / / /

CVSS Score

7.5 / 10

CVSS Data - 3.1

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

    View Vector String

Timeline

Published: March 28, 2025, 3:15 p.m.
Last Modified: March 28, 2025, 6:11 p.m.

Status : Awaiting Analysis

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.