Link: Exploring Key Features of Cisco ISE Release 2.6

In July I wrote for the CDW blog about the new version of the Cisco Identity Services Engine (ISE) software.

Exploring Key Features of Cisco ISE Release 2.6

The latest version of this cybersecurity tool offers unique device identification and an IoT protocol.

DIGOO DG-HOSA – Part 1 (Teardown and Hardware)

Banggood page

This project started with the idea of purchasing a cheap security system off one of the Chinese stores. After a little hunting, I found Digoo DG HOSA 433MHz 2G&GSM&WIFI Smart Home Security Alarm System Protective Shell Alert with APP which looked interesting so picked one up to tear apart. I was curious about how various communication methods were implemented.

This is the first part of this adventure the next part will be exploring the firmware of the device. With that let's take a look at the hardware.

Teardown Time

After the device showed up, I quickly got down to taking the device apart. In my haste, I didn't take many good photos of it intact. The front side of the board is straight forward; it contains the screen, button array for all user input, and a lot of useful test points. The front side is pictured below.

Board Front

The most significant information found on the front side of the board is the notation PG-103, which is also found in the firmware (spoiler). After some searching, I found this device is also branded as the PGST PG-103. This kind of rebranding of hardware is not unusual for a lot of Chinese devices.

Now switching to the back of the board, which is the business side of the board with the main chips and modules providing the various communication methods. When opening that device I encountered the intrusion detection button. This button causes the device to go into an alarm mode and require a reset of the device to come back online. For my testing, I bypassed this button bridging both sides of it.

Back of Board
Board Back

Component List

When inspecting the board, I found a few significant components and modules on the board. I was not surprised to see that most of the major communication parts are off the shelf modules. The components listed below are highlighted in the image above and the relevant data sheets where available are linked.

The main processor is a GigaDevice GD32 chip which is a series that is very similar to the of STMicroelectronics STM32 chips. The GD32F105 chip uses an ARM-based instruction set and has the same pinout as the STM32F105 component.

Block Diagram

The high-level block diagram for the device is pretty straight forward. The GD32F105 chip is the primary processing and control of the external communication modules. This allows for a modular architecture all of the peripherals.

 +-----------------------+
 |  Cellular             +-----------+
 |  Quictel M26          |           |
 +-----------------------+           |
 +-----------------------+  +--------+-------+
 |  WIFI                 +--+   CPU          |
 |  HF-LPB120-1          |  |   GD32F105RCT6 |
 +-----------------------+  +--------+-+-----+
 +-----------------------+           | |
 |  433mhz receiver      |           | |
 |  SYN511R              +-----------+ |
 +-----------------------+             |
 +-----------------------+             |
 |  Keypad Controller    +-------------+
 |  Holtek BS83B16A-3    |
 +-----------------------+

Pin Out

When exploring the board there are many test points on the board and tracing them out I was able to trace out most of the pins to where they connect on the controller.

  • SYN515R Pin 10 (DO) -> CPU PB9 (62)
  • Unknown -> CPU PA5
  • Unknown -> CPU PA6
  • Unknown -> CPU PA8
  • U7 SCL -> Unknown
  • U7 SDA -> Unknown
  • DAC_OUT -> CPU PA4 (20)
  • WIFI UART TX -> CPU PA2 (16)
  • WIFI UART RX -> CPU PA3 (17)
  • GSM UART TX -> CPU PA12 (45)
  • GSM UART RX -> CPU PA13 (46)
  • U1 (F117) Pin 6 -> CPU PB 8

Summary?

After investigating the hardware I was able to extract the firmware and start the reversing process. I will cover what I have found in future posts. For now, if you are interested in more higher resolution photos of the board I have posted them on my Flickr account.

BSidesNH 2019 Recap

Badge

Back on May 18th, I attended the inaugural BsidesNH event. It was a fantastic one-day event. The day started pretty early for me driving down from Maine arriving at Southern NH University. I arrived to pick up the fantastic badge made out of an old 3.5" disk. After grabbing some coffee and a snack I settled into the auditorium and for a day of great talks. There were a few that stood out to me from the day that I will talk about.

The second talk of the day was Ghost in the Shell: When AppSec Goes Wrong by Tony Martin. Tony first talked about covered some basics of web application security. He framed these issues around the research he has done into various NAS devices and vulnerabilities he has discovered. Including the ability to create shadow users that have administrative access to devices but are not visible through the administrative interfaces of the device.

After lunch was Chinese and Russian Hacking Communities presented by Winnona DeSombre and Dan Byrnes, Intelligence Analyst from Recorded Future. They covered operations and cultures of Chinese and Russian underground groups. This was a very entertaining presentation and a summary of the information contained in the report: Thieves and Geeks: Russian and Chinese Hacking Communities.

The second to last talk of the day was Hunting for Lateral Movement: Offense, Defense, and Corgis presented by Ryan Nolette. He covered the ways attackers move around and infiltrate further into a network...Corgies. A great quote that stuck with me from his talk was: “If you teach an analyst how to think they will punch above their weight.” I feel this quote not only applies to security analysts but all levels of IT professionals.

BsidesNH was a well run and enjoyable event and a great addition to the Security events in New England. Thanks to all of the organizers and sponsors. I look forward to attending next year!

Hashcat in AWS EC2

Intro

During my OSCP studies, I realized I needed a more efficient system for cracking password hashes. The screaming CPU fans and high CPU usage became a problem. I first tried using hashcat and the GPU on my MacBook Pro in OS X. There are some bugs and problems with hashcat on OS X that would make it crash in the middle of cracking a hash. Also, I was not interested in investing a server with a bunch of GPUs, the high costs to do this would outweigh the amount of time I need the system. All of this lead me to do a little research and found the instructions in the following link to build an AWS instance for password cracking.

https://medium.com/@iraklis/running-hashcat-v4-0-0-in-amazons-aws-new-p3-16xlarge-instance-e8fab4541e9b

Since that post was created there have been some changes to the offerings in AWS EC2 leading me write this post.

If you wish to skip ahead I have created scripts to automate the processes in the rest of this post. They are both in my github and can be downloaded at the following links.

https://github.com/suidroot/AWSScripts/blob/master/aws-ec2-create-kracker.sh
https://github.com/suidroot/AWSScripts/blob/master/configure-kracker.sh

For the rest of the article I will cover some of the instance options in EC2, installation of the needed Linux packages, the basic setup of Hashcat, running Hashcat, and finally monitoring and benchmarks of an EC2 instance.

AWS EC2 Options

There are many options for EC2 instances, they have a huge range in cost and scale.

I found the g3 instances to be the more cost effective tier. For my testing I opted to use the g3.4xlarge tier. Next to choose the AMI image, appropriate the appropriate operating system.

AMI images

There are two options that are I tested hashcat on they are both Ubuntu based. I’m sure there are many other available options that will work too, but I am familiar with Ubuntu systems. The first option is a standard Ubuntu image, there is nothing special about this image and it requires configuration to add the GPU drivers and a little more work.

Standard Ubuntu

The next option is a Deep Learning image, this image is preconfigured with the GPU drivers and was originally designed for machine learning applications. I found the the pre-configuration allowed for me skip a few steps in building out a new system.

Deep learning Ubuntu GPU driver preloaded

Instance Build and config

Once you have the instance deployed there are a few steps to get the Instance prepared for hashcat, the steps are a little bit different between a Standard and a Deep Learning Ubuntu instance.

An apt cronjob may already be running and you will have to wait it out.

Prepare Machine (Standard Ubuntu)

This script will install all the required packages and the Nvidia GPU drivers on a vanilla Ubuntu installation.

#!/bin/bash

# mostly copied from: https://medium.com/@iraklis/running-hashcat-v4-0-0-in-amazons-aws-new-p3-16xlarge-instance-e8fab4541e9b
#
sudo apt-get update -yq
sudo apt-get install -yq build-essential linux-headers-$(uname -r) unzip p7zip-full linux-image-extra-virtual
sudo apt-get install -yq ocl-icd-libopencl1 opencl-headers clinfo
#sudo apt-get install -yq libhwloc-plugins libhwloc5 libltdl7 libpciaccess0 libpocl2 libpocl2-common ocl-icd-libopencl1 pocl-opencl-icd
sudo apt-get install -yq python3-pip 
pip3 install psutil

sudo touch /etc/modprobe.d/blacklist-nouveau.conf
sudo bash -c "echo 'blacklist nouveau' >> /etc/modprobe.d/blacklist-nouveau.conf"
sudo bash -c "echo 'blacklist lbm-nouveau' >> /etc/modprobe.d/blacklist-nouveau.conf"
sudo bash -c "echo 'options nouveau modeset=0' >> /etc/modprobe.d/blacklist-nouveau.conf"
sudo bash -c "echo 'alias nouveau off' >> /etc/modprobe.d/blacklist-nouveau.conf"
sudo bash -c "echo 'alias lbm-nouveau off' >> /etc/modprobe.d/blacklist-nouveau.conf"

sudo touch /etc/modprobe.d/nouveau-kms.conf
sudo bash -c "echo 'options nouveau modeset=0' >>  /etc/modprobe.d/nouveau-kms.conf"
sudo update-initramfs -u
sudo reboot

### Install nVidia Drivers
wget http://us.download.nvidia.com/tesla/410.104/NVIDIA-Linux-x86_64-410.104.run
sudo /bin/bash NVIDIA-Linux-x86_64-410.104.run --ui=none --no-questions --silent -X

Prepare Machine (Deep Learning Ubuntu)

In comparison the previous script there is a much simpler script to prepare the Deep Learning instance. The main focus is installing the needed archive extraction tools.

#!/bin/bash

sudo apt update
sudo apt upgrade
sudo apt install clinfo unzip p7zip-full
sudo apt install build-essential linux-headers-$(uname -r) # Optional 
sudo apt-get install -yq python3-pip 
pip3 install psutil

Hashcat Setup

Now we need to download and extract the star of the show Hashcat. The link in the wget below points to the the most recent version as of writing however you might want to check to see if there is a more recent version at the main site: https://hashcat.net/hashcat/

wget https://hashcat.net/files/hashcat-5.1.0.7z
7z x hashcat-5.1.0.7z

Download wordlists

You will need some wordlists for hashcat to use to crack passwords, he commands listed are for some wordlists I like to use when cracking. You should however add whichever lists are your favories.

mkdir ~/wordlists
git clone https://github.com/danielmiessler/SecLists.git ~/wordlists/seclists
wget -nH http://downloads.skullsecurity.org/passwords/rockyou.txt.bz2 -O ~/wordlists/rockyou.txt.bz2
cd ~/wordlists
bunzip2 ./rockyou.txt.bz2
cd ~

Running hashcat

Now it is time to run hashcat and crack some passwords. When running hashcat I had the best performance with the arguments-O -w 3. Below is an example command line I've used inclusing a rules file.

./hashcat-5.1.0/hashcat64.bin --username -m 1800 ./megashadow256.txt wordlists/rockyou.txt -r hashcat-5.1.0/rules/best64.rule -O -w 3

Monitoring the Nvidia GPU

The nvidia-smi utility can be used to show the GPU processor usage and what processes are utilizing the GPU(s). The first example is is showing an idle GPU.

ubuntu@ip-172-31-17-6:~$ sudo nvidia-smi
Fri Apr 26 14:43:49 2019
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 410.104      Driver Version: 410.104      CUDA Version: 10.0     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|===============================+======================+======================|
|   0  Tesla M60           Off  | 00000000:00:1E.0 Off |                    0 |
| N/A   37C    P0    42W / 150W |      0MiB /  7618MiB |     97%      Default |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                       GPU Memory |
|  GPU       PID   Type   Process name                             Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+

This example shows a GPU being used by hashcat.

ubuntu@ip-172-31-17-6:~$ sudo nvidia-smi
Fri Apr 26 14:44:44 2019
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 410.104      Driver Version: 410.104      CUDA Version: 10.0     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|===============================+======================+======================|
|   0  Tesla M60           Off  | 00000000:00:1E.0 Off |                    0 |
| N/A   46C    P0   141W / 150W |    828MiB /  7618MiB |    100%      Default |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                       GPU Memory |
|  GPU       PID   Type   Process name                             Usage      |
|=============================================================================|
|    0     11739      C   ./hashcat-5.1.0/hashcat64.bin                817MiB |
+-----------------------------------------------------------------------------+

Conclusion and Benchmarks

Finally here is a benchmark I ran on a g3.4xlarge instance. This instance type contains 1 GPU. These results give an idea of performance for this AWS EC2 instance type.

ubuntu@ip-172-31-17-6:~$ ./hashcat-5.1.0/hashcat64.bin -O -w 3 -b
hashcat (v5.1.0) starting in benchmark mode...

* Device #2: Not a native Intel OpenCL runtime. Expect massive speed loss.
             You can use --force to override, but do not report related errors.
nvmlDeviceGetFanSpeed(): Not Supported

OpenCL Platform #1: NVIDIA Corporation
======================================
* Device #1: Tesla M60, 1904/7618 MB allocatable, 16MCU

OpenCL Platform #2: The pocl project
====================================
* Device #2: pthread-Intel(R) Xeon(R) CPU E5-2686 v4 @ 2.30GHz, skipped.

Benchmark relevant options:
===========================
* --optimized-kernel-enable
* --workload-profile=3

Hashmode: 0 - MD5

Speed.#1.........: 11611.6 MH/s (90.74ms) @ Accel:512 Loops:512 Thr:256 Vec:4

Hashmode: 100 - SHA1

Speed.#1.........:  4050.2 MH/s (65.01ms) @ Accel:512 Loops:128 Thr:256 Vec:2

Hashmode: 1400 - SHA2-256

Speed.#1.........:  1444.5 MH/s (91.98ms) @ Accel:256 Loops:128 Thr:256 Vec:1

Hashmode: 1700 - SHA2-512

Speed.#1.........:   499.4 MH/s (66.78ms) @ Accel:128 Loops:64 Thr:256 Vec:1

Hashmode: 2500 - WPA-EAPOL-PBKDF2 (Iterations: 4096)

Speed.#1.........:   189.8 kH/s (42.76ms) @ Accel:128 Loops:64 Thr:256 Vec:1

Hashmode: 1000 - NTLM

Speed.#1.........: 18678.1 MH/s (56.58ms) @ Accel:512 Loops:512 Thr:256 Vec:2

Hashmode: 3000 - LM

Speed.#1.........: 10529.6 MH/s (50.60ms) @ Accel:128 Loops:1024 Thr:256 Vec:1

Hashmode: 5500 - NetNTLMv1 / NetNTLMv1+ESS

Speed.#1.........: 10650.8 MH/s (49.60ms) @ Accel:512 Loops:256 Thr:256 Vec:1

Hashmode: 5600 - NetNTLMv2

Speed.#1.........:   829.3 MH/s (80.24ms) @ Accel:256 Loops:64 Thr:256 Vec:1

Hashmode: 1500 - descrypt, DES (Unix), Traditional DES

Speed.#1.........:   442.0 MH/s (37.81ms) @ Accel:4 Loops:1024 Thr:256 Vec:1

Hashmode: 500 - md5crypt, MD5 (Unix), Cisco-IOS $1$ (MD5) (Iterations: 1000)

Speed.#1.........:  4209.1 kH/s (51.39ms) @ Accel:1024 Loops:500 Thr:32 Vec:1

Hashmode: 3200 - bcrypt $2*$, Blowfish (Unix) (Iterations: 32)

Speed.#1.........:     7572 H/s (33.02ms) @ Accel:16 Loops:4 Thr:8 Vec:1

Hashmode: 1800 - sha512crypt $6$, SHA512 (Unix) (Iterations: 5000)

Speed.#1.........:    76958 H/s (83.99ms) @ Accel:512 Loops:128 Thr:32 Vec:1

Hashmode: 7500 - Kerberos 5 AS-REQ Pre-Auth etype 23

Speed.#1.........:   149.4 MH/s (56.00ms) @ Accel:128 Loops:64 Thr:64 Vec:1

Hashmode: 13100 - Kerberos 5 TGS-REP etype 23

Speed.#1.........:   152.1 MH/s (55.00ms) @ Accel:128 Loops:64 Thr:64 Vec:1

Hashmode: 15300 - DPAPI masterkey file v1 (Iterations: 23999)

Speed.#1.........:    32703 H/s (84.02ms) @ Accel:256 Loops:64 Thr:256 Vec:1

Hashmode: 15900 - DPAPI masterkey file v2 (Iterations: 7999)

Speed.#1.........:    21692 H/s (96.24ms) @ Accel:256 Loops:128 Thr:32 Vec:1

Hashmode: 7100 - macOS v10.8+ (PBKDF2-SHA512) (Iterations: 35000)

Speed.#1.........:     5940 H/s (40.09ms) @ Accel:64 Loops:32 Thr:256 Vec:1

Hashmode: 11600 - 7-Zip (Iterations: 524288)

Speed.#1.........:     4522 H/s (55.87ms) @ Accel:256 Loops:128 Thr:256 Vec:1

Hashmode: 12500 - RAR3-hp (Iterations: 262144)

Speed.#1.........:    18001 H/s (56.74ms) @ Accel:4 Loops:16384 Thr:256 Vec:1

Hashmode: 13000 - RAR5 (Iterations: 32767)

Speed.#1.........:    18135 H/s (55.93ms) @ Accel:128 Loops:64 Thr:256 Vec:1

Hashmode: 6211 - TrueCrypt PBKDF2-HMAC-RIPEMD160 + XTS 512 bit (Iterations: 2000)

Speed.#1.........:   121.7 kH/s (59.39ms) @ Accel:128 Loops:32 Thr:256 Vec:1

Hashmode: 13400 - KeePass 1 (AES/Twofish) and KeePass 2 (AES) (Iterations: 6000)

Speed.#1.........:    68380 H/s (158.89ms) @ Accel:512 Loops:256 Thr:32 Vec:1

Hashmode: 6800 - LastPass + LastPass sniffed (Iterations: 500)

Speed.#1.........:  1088.7 kH/s (48.51ms) @ Accel:128 Loops:62 Thr:256 Vec:1

Hashmode: 11300 - Bitcoin/Litecoin wallet.dat (Iterations: 199999)

Speed.#1.........:     2107 H/s (78.97ms) @ Accel:128 Loops:64 Thr:256 Vec:1

Started: Fri Apr 26 14:36:56 2019
Stopped: Fri Apr 26 14:42:03 2019

If you've made it this far congratulation and happy cracking!

March 2019 NX-OS Vulnerability Dump

On March 6th Cisco released 29 high and medium rated PSIRT notices for NX-OS based platforms. These platforms include the Cisco Nexus 3000 - 9000 series and Nexus adjacent platforms FX-OS and UCS Fabric Interconnect platforms. Not all advisories affect all platforms but all platforms are affected by at least one high rated vulnerability. The vulnerabilities range from command and code execution, privilege escalation, denial of service, and arbitrary file read vulnerabilities. This is just about everything bad that could affect core infrastructure devices.

If you haven't updated your switch in a while this is probably the time too. Within some of the advisories Cisco notes that they are providing free updates:

Cisco has released free software updates that address the vulnerability described in this advisory. Customers may only install and expect support for software versions and feature sets for which they have purchased a license.

I've included a table of the fixed in versions notes as of the writing of this post.  I would recommend looking at the advisories to assist in selecting the best version as there are other code versions that have integrated the fixes.

PlatformVersion
Nexus 1000v5.2(1)SM3(2.1) (Hyper-V)
5.2(1)SV3(4.1a) (VMWare)
Nexus 3000
Nexus 3500
Nexus 3600
9.2(2)
Nexus 5500, 5600, and 6000
Nexus 7000 and 7700
8.3(3)
Nexus 9000 and 95009.2(2)
UCS 6200 and 6300 Series Fabric Interconnects
UCS 6400 Series Fabric Interconnects
4.0(2a)

Cisco has a bundled advisory for all of the high rated notices at the following link,

Cisco Event Response: March 2019 Cisco FXOS and NX-OS Software Security Advisory Bundled Publication

I have also included a laundry list of notices including both high and medium rated vulnerabilities for your reference.

Happy patching!