CVE-2025-10543

Dec. 2, 2025, 5:16 p.m.

6.3
Medium

Description

In Eclipse Paho Go MQTT v3.1 library (paho.mqtt.golang) versions <=1.5.0 UTF-8 encoded strings, passed into the library, may be incorrectly encoded if their length exceeds 65535 bytes. This may lead to unexpected content in packets sent to the server (for example, part of an MQTT topic may leak into the message body in a PUBLISH packet). The issue arises because the length of the data passed in was converted from an int64/int32 (depending upon CPU) to an int16 without checks for overflows. The int16 length was then written, followed by the data (e.g. topic). This meant that when the data (e.g. topic) was over 65535 bytes then the amount of data written exceeds what the length field indicates. This could lead to a corrupt packet, or mean that the excess data leaks into another field (e.g. topic leaks into message body).

Product(s) Impacted

Product Versions
paho_mqtt
  • 1.5.0
paho_mqtt
  • *

Weaknesses

Common security weaknesses mapped to this vulnerability.

CWE-197
Numeric Truncation Error
Truncation errors occur when a primitive is cast to a primitive of a smaller size and data is lost in the conversion.

CVSS Score

6.3 / 10

CVSS Data - 4.0

  • Attack Vector: NETWORK
  • Attack Complexity: LOW
  • Attack Requirements: PRESENT
  • Privileges Required: NONE
  • User Interaction: NONE
  • Scope:
  • Confidentiality Impact: LOW
  • Integrity Impact: NONE
  • Availability Impact: NONE
  • Exploit Maturity: NOT_DEFINED
  • CVSS:4.0/AV:N/AC:L/AT:P/PR:N/UI:N/VC:L/VI:N/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: Dec. 2, 2025, 9:15 a.m.
Last Modified: Dec. 2, 2025, 5:16 p.m.

Status : Awaiting Analysis

CVE has been marked for Analysis. Normally once in this state the CVE will be analyzed by NVD staff within 24 hours.

More info

Source

emo@eclipse.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.