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2019-09-25 Juniper. Masad Stealer: Exfiltrating using Telegram

“Masad Clipper and Stealer” steals browser information, computer files, and automatically replaces cryptocurrency wallets from the clipboard with its own.It is written using Autoit scripts and then compiled into a Windows executable.
It uses Telegram to exfiltrate stolen information.

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Download. Email me if you need the password (see in my profile)

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Introduction

If you read our last blog post, you know that the Mobile Forensic team ran into some issues early on. We are happy to share that we have since overcome those issues, and we’ve hit the ground running with our project. We are no longer using the LG G6 devices mentioned last month due to issues rooting these devices. Instead we are using Nexus 7 tablets running Android 6 and 7.

Two apps we pulled and analyzed data from are Snapchat and Telegram. Before we set up accounts, we had to create different personas for each team member. The personas we came up with were Johan Smith, Tony Pepperoni, and Mallow Operator.

Snapchat

Before we started collecting our data, we had to figure out what actions we would go through so we all had data we could compare. Some of the actions to generate data for Snapchat included adding each other as friends, creating a group chat, and posing to a story. When generating data for analysis, we kept track of who sent chats to whom, what time we did each action, and if anything went wrong. After pulling the data with adb, we compared the timestamps and actions from the pull with our datagen log. We were successfully able to see what Snapchat saves and what we can find on the phone.

When we were setting up Telegram, we had to setup Google Voice numbers in order to create our profiles. With Telegram we also had to figure out what actions we wanted to take so that each person could get similar pull results—hence the creation of another datagen. With Telegram our actions included adding contacts, joining different groups, and sending videos and stickers. We kept track of timestamps again and then compared the data and the pull. We decided to use both Cellebrite and adb to see if there was any benefit of one tool over the other. At the moment, we’re still analyzing Telegram to see if there is anything noteworthy so stay tuned!

Conclusion

With these pulls we were able to see what data Snapchat and Telegram save on your phone. We were looking to see if any unusual data was saved by the applications. So far nothing has stood out with either Snapchat or Telegram. The next app we will be doing a datagen and pulling is LinkedIn.

Stay tuned for more updates to come and follow us on Twitter @ChampForensics, Instagram @ChampForensics, and Facebook @ChamplainLCDI.

The post Mobile Forensics Update 2 appeared first on The Leahy Center for Digital Investigation.