Tool

When configuring a new device, achieving an acceptable Lynis hardening score is a challenge most practitioners are familiar with.

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During our recent research, we experimented with different Bluetooth USB dongles. There are tons of options, and sometimes, it’s challenging to determine what chipset a dongle actually contains, what Bluetooth features it supports, and whether it works on Linux. Inspired by the recent ESP32 Bluetooth research, we wondered whether we could turn our Raspberry Pi Pico Ws into a functioning Bluetooth dongle. We had a few lying around, and the advantage here is that we know exactly which Bluetooth controller it uses – the Infineon CYW43439. It’s also very easy to get one. You can just buy the Pico W for a few bucks, even cheaper than some Bluetooth dongles. You also have a controller family that has been researched quite a bit in the internalblue project. However, there was one disadvantage. We did not find any code that exposes the CYW43439’s HCI interface via USB. So we had to write that on our own.

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Using a static passkey for Bluetooth Low Energy pairing is insecure. Recent versions of the Bluetooth specification contain an explicit warning about this. However, in practice, we often see static passkeys being used. Moreover, there are no public implementations of proofs-of-concept that can practically show why using a static passkey is an issue. This is why we implemented one.

In a recent assessment, we were testing a device that offered a Bluetooth interface for data export and configuration. This device uses Bluetooth Low Energy (BLE), and a static passkey (or PIN) is required to pair with it. This passkey is displayed for a few seconds when the device is booted and stays the same on each reboot. In fact, it is derived from static, device-specific data.

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Introduction

In this post, I will introduce fpicker. Fpicker is a Frida-based coverage-guided, mostly in-process, blackbox fuzzing suite. Its most significant feature is the AFL++ proxy mode which enables blackbox in-process fuzzing with AFL++ on platforms supported by Frida. In practice, this means that fpicker enables fuzzing binary-only targets with AFL++ on potentially any system that is supported by Frida. For example, it allows fuzzing a user-space application on the iOS operating system, such as the Bluetooth daemon bluetoothd – which was part of the original motivation to implement fpicker.

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TLDR: This blogpost presents devi, a tool that can help you devirtualize virtual calls in C++ binaries. It uses Frida to trace the execution of a binary and uncover the call sources and destinations of virtual calls. The collected information can then be viewed in IDA Pro, Binary Ninja, or Ghidra. The plugin adds the respective control-flow edges allowing further analysis (using different plugins) or simply providing more comfort when analyzing C++ binaries.

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The use of Internet of Things devices is continuously increasing: People buy devices, such as smart assistants, to make their lives more comfortable or fitness trackers to assess sports activities. According to the Pew Research Center [1], every fifth American wears a device to track their fitness. In Germany, the number increases likewise. The increasing number of fitness trackers in use can also be seen in criminal proceedings, as there exist more and more cases where these devices provide evidence.

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Generally speaking, I’m more of a Cat type of guy, but I have to say I really love BloodHound. And if you do too, you are in for a treat…
Last week, the ERNW Insight Active Directory Security Summit took place in Heidelberg. (More Info)
For this occasion, @Enno_Insinuator asked me if I would like to deliver a BloodHound Workshop, and of course I accepted the challenge…

We had a full class, I had a blast training it, and I hope the trainees enjoyed it as much as I did.
But that’s not all…
Another part of the deal was that I had to write a Training Guide that we would then share with the Community (aka you).
So here it is, fresh from the Heidelberg press and available for download:

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A new major version of our fuzzing framework dizzy has been released.

This blog post will cover the biggest changes and new features, as well as give you a short introduction into how to use them.

You can find the new version on github.

Installation

There are two supported ways of installing dizzy:

via pypi
$ pip install dizzy
via setup.py
$ git clone https://https://github.com/ernw/dizzy && cd dizzy && python setup.py install

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When I recently joined the Windows Security team at ERNW, Enno asked me if I wanted to write a ‘welcome’ blogpost on a topic of my choosing… Up for the challenge, and since I had been playing with BloodHound & Cypher for the last couple of months, I first thought I would do something on that topic.

However, after gathering my thoughts and some Cypher I had collected here and there, I realized that the topic of Bloodhound Cypher might actually require several blog posts… And so I changed my mind. I will keep the joys of Cypher for later, and in this post, I will talk about a tiny tool I wrote to query the Mitre ATT&CK™ knowledge base from the comfort of a PowerShell prompt.

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In one of the last pentests we’ve found an epmd (Erlang port mapper daemon) listening on a target system (tcp/4369). It is used to coordinate distributed erlang instances, but also can lead to a RCE, given one knows the so called “authentication cookie”. Usually, this cookie is located in ~/.erlang.cookie and is generated by erlang at the first start. If not modified or set manually it is a random string [A:Z] with a length of 20 characters. If an attacker gains this cookie, a RCE is quite easy – as I like to describe below.

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