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CVE-2017-10724

Status Candidate

Overview

Recently it was discovered as a part of the research on IoT devices in the most recent firmware for Shekar Endoscope that an attacker connected to the device Wi-Fi SSID can exploit a memory corruption issue and execute remote code on the device. This device acts as an Endoscope camera that allows its users to use it in various industrial systems and settings, car garages, and also in some cases in the medical clinics to get access to areas that are difficult for a human being to reach. Any breach of this system can allow an attacker to get access to video feed and pictures viewed by that user and might allow them to get a foot hold in air gapped networks especially in case of nation critical infrastructure/industries. The firmware contains binary uvc_stream that is the UDP daemon which is responsible for handling all the UDP requests that the device receives. The client application sends a UDP request to change the Wi-Fi name which contains the following format: "SETCMD0001+0002+[2 byte length of wifipassword]+[Wifipassword]. This request is handled by "control_Dev_thread" function which at address "0x00409AE4" compares the incoming request and determines if the 10th byte is 02 and if it is then it redirects to 0x0040A7D8, which calls the function "setwifipassword". The function "setwifipassword" uses a memcpy function but uses the length of the payload obtained by using strlen function as the third parameter which is the number of bytes to copy and this allows an attacker to overflow the function and control the $PC value.

Related Files

Shekar Endoscope Weak Default Settings / Memory Corruption
Posted Jun 9, 2019
Authored by Mandar Satam

Shekar Endoscope has telnet enabled by default, default wifi credentials, a flaw where an attacker can change the wifi password without any additional authentication, and four memory corruption vulnerabilities.

tags | exploit, vulnerability
advisories | CVE-2017-10718, CVE-2017-10719, CVE-2017-10720, CVE-2017-10721, CVE-2017-10722, CVE-2017-10723, CVE-2017-10724
MD5 | d85c615496ac548a0c78e08a5863f4d4
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