Our renowned RAM Analysis for Investigators class is now online, click here for details
The Advanced RAM Analysis course will be held in Bristol in the UK from the 4th to 7th December 2017. This is a rare chance to benefit from this course in the UK, spaces are limited so book early.
This course focuses on RAM analysis for the digital investigator and what can be obtained in addition to any disk analysis.
You can check out the syllabus here Advanced Live Forensics flyer. (We have now added Truecrypt and Bitlocker decryption!)
The cost will be £1650 + VAT or £1999 + VAT residential
To book fill out the form below and we will get in contact with you as soon as possible
Post expires at 11:32am on Monday December 4th, 2017
As the Court case between the FBI and Apple has gone away (for now), I offer a cautionary tale. To back door or not to back door – that is the question.
The King and the Apple
Once upon a time there was a Kingdom in the west ruled by a powerful King. The people were affluent and happy and ate a lot of apples. In the eastern part of the Kingdom was a man who made locks. His locks were so good that most people in the land used them, his locks had made him a very wealthy man.
Although it was a generally good Kingdom, as with everywhere, there were those that would undermine the security of the Land, would break into houses and steal possessions of others. There were even those that hated the Kingdom and would stop at nothing to terrorise it and its people.
One day the King visited the lock maker. The King asked that the lock maker make him a special key that would let him enter the houses of those that would cause him trouble. He promised that the key would only be used to open the doors of the worst criminals. This, he said, would make the Land more secure. The lock maker loved the King and wanted to do all he could to help. He wanted everyone to be secure, so he made him the special key.
For a while the King used the key to open the houses of just the worst criminals but it worked so well that the King realised that it could be used to open the houses of anyone he didn’t like if he had more keys. The lock maker wasn’t happy but the King had said that it would make the Land more secure so he made lots of keys for all his enforcement militia.
Although most of the militia were loyal to the King there was one man serving who was also giving information to the criminals for money. He gave them a key. Now the criminals could open all the doors. The people heard about it and didn’t feel more secure, quite the opposite.
Meanwhile in a Kingdom far away in the east they had also heard about the lock maker’s amazing locks and had been buying them for many years. However, the Emperor heard about the special key that could open any of the locks. Knowing that it could be done, he tasked all his wisest subjects to find a way to make a special key. It didn’t take long. Now the Emperor could open all the locks, including the locks in the western land.
The King in the west heard that the Emperor could open all the locks, he didn’t feel so secure anymore.
Later, a young lock maker’s apprentice heard from the girlfriend of the friend of a person in the militia that you could make a key to open all the locks. The young man was very intelligent and in no time at all had worked out how to make the key. He believed that everyone should know how to do it, so he wrote to every newspaper in every town in the Kingdom so that anyone could make such a key.
Now, no one felt secure, not the people, not the criminals, not the terrorists, not the militia, not even the King. No one bought the wonderful locks anymore and the wealthy lock maker went out of business.
However, in the land in the East a new clever young lock maker made an incredible lock that no one could open. Everyone bought his locks and felt secure again.
A few months later the Emperor of the eastern land stood before the young lock maker. The Emperor explained that if he could make him a key to open the new locks, that it would make everyone more secure.
The King in the West learned that he could no longer open locks, but the Emperor could, and tragically choked on the apple he was eating and expired.
The fully updated 4 day Advanced Open Source Intelligence Gathering Course will be taught in Bristol UK from the 11th to 14th September 2017. There are limited places so please do not delay.
The course cost is £1850 + VAT. This includes a 500 page colour manual, USB3 key with all needed software, a 2 week license of Maltego Classic, Maltego Case File and 3 months international VPN access.
To begin the booking process please fill in the form below.
Post expires at 1:48pm on Thursday September 14th, 2017
As I carry out a significant amount of OSInt work I often bump into the problem of needing to enumerate IP addresses. This can include knowing what my own external IP address is. Simply running ifconfig (or ipconfig in Windows) will provide my internal addresses but not the internet facing address from the router. This is especially important when trying to ensure that you are hidden from a target. It could be that I connect to a VPN or proxy elsewhere in the world but how can I be sure that my IP address is hidden?
A student on my recent Advanced OSI course related a story of a colleague researching a very dangerous group and suddenly realising that their VPN software had crashed and that their Police IP address was now visible in their targets logs – not good!
Their are loads of tools, especially Firefox plugins, that will report your IP and the IP of the site you are on, WorldIP is a favourite. However, I wanted to write a small program that would monitor my IP and report if it changes. I also wanted to be able to write a tool to do batch look ups of domains and IP’s and extract their Geolocation information.
I stumbled across freegeoip.net. It is a simple IP look up site but with an API. It allows 10,000 look ups per day for free which is more than enough (for most days!).
To use just type into your browser –
and it will return information about your own external IP address into a CSV file. Lovely! The results look like this…
You can also specify /xml, /json and /jsonp.
By adding a URL or IP address to the query will return the information about that address…
…and it returns…
220.127.116.11,US,United States,NY,New York,Somers,10589,America/New_York,41.33,-73.70,501
or if you specify /xml…
<Response><IP>18.104.22.168</IP><CountryCode>US</CountryCode><CountryName>United States</CountryName><RegionCode>NY</RegionCode><RegionName>New York</RegionName><City>Somers</City><ZipCode>10589</ZipCode><TimeZone>America/New_York</TimeZone><Latitude>41.325</Latitude><Longitude>-73.698</Longitude><MetroCode>501</MetroCode></Response>
To do this programmatically perhaps from a Shell script I can just use wget
Using this I can write a simple background tool that monitors my IP address and notifies me of any change. It will also be easy to have a tool which can be pointed at a text file of IPs or domains and returns all the information to me. That will save loads of time.
I’ll post the tools when I’ve done them.
I was teaching RAM analysis at the Swedish Police Academy this week, which included a segment on parsing out the MFT. This is an extraordinary capability that opens up a view of the disk to an investigator, which they may not have. Perhaps the RAM was taken but the plug pulled on an encrypted disk or maybe because of covert imaging considerations.
Each MFT entry is 1024 bytes which is taken up by the file name, accessed, modified and created dates and so on. However, if this data plus the file data is less than the 1024 bytes then the raw file data, the hex, is written to the MFT itself rather than out onto the disk somewhere.
The MFT parser is simple to run once you have an instance of Volatility running (see https://code.google.com/p/volatility/)
Python vol.py mftparser –f pathtoRAM >> pathtoaTEXTfile
When you view the rather verbose output from the Volatility MFT parser you stumble across entries that look like Fig 1:-
Here we can see a GIF image named AU_bg_TopMiddle.gif created way back in May 2005. The text output contains 3 columns: the virtual file offset addresses, the raw hex and the ASCII interpretation of the hex.
One of the students said it would be cool if we could ‘carve’ the original file out of the MFT result. Of course you could simply use Foremost, Photorec or a host of other data carvers on the RAM dump itself and the image would be found but it would have no file name, no metadata and be completely unattributable.
So, this seemed like a good idea to try.
I copied the $DATA chunk out of the text file, but being a text file it was completely unfriendly, dragging the other 2 columns with it. See Fig 2.
Next I manually deleted all the addresses, then all the interpreted ASCII to leave myself with the raw data. (Fig 3)
Then I fired up the awesome WINHEX and attempted to import it as ASCII-HEX but it was rather unhappy with the carriage returns and spaces. After about 20 minutes of faffing about I eventually managed to have the raw data sat in the Winhex window and it saved as a tiny, pointless GIF. But it worked!
That evening at the hotel I decided that a Python script was in order and a few hours later I had finished MFT2File.py. You can download it here.
Life is now much easier. Copy the chunk of data out of the MFT from below the $DATA line to the end of the Interpreted ASCII into a text editor like Notepad++ and save it in the same folder as MFT2File.py (to make life easier). Also, make a note of the original file name.
Next, open a command shell, ‘cd’ into the folder with the .py and text files in and run:-
First it will ask for the name of the original file. Make sure you at least get the extension right.
Second, it will ask you for the filename of the text file you made (and path if you didn’t put it in the same folder like I suggested). Fig 3
That’s it. The file will be magically recreated in the same folder as the .py file.
These files are small and some are text anyway like cookies and HTML file etc. However, it is great to see them as the original file and a fun project.
P.S.- Michael Ligh from the Volatility dev team just let me know that Volatility 2.4 will have a dump option to achieve this which is superb!
Whilst teaching my recent OSI course we had spent a good deal of time mapping the online infrastructure of a company using Maltego. The footprinting ‘machines’ are really superb and if you haven’t played with the tool go get it now!
Later in the day we were extracting company employee data from resources such as Data.com and LinkedIn and one of the students tried mapping the data with the Import option in Maltego. He had mapped employee name to office location and the map provided an immediate view of the approximate physical infrastructure with larger numbers of employees naturally oriented to HQ’s and small numbers to sub-offices. It was interesting to see. We did some standard research and the ‘map’ had been correct in ID’ing the primary HQ and sub offices.
Of course, the output is only as good as the data but this is where a tool called Jigsaw comes in (http://www.pentestgeek.com/tools/). Jigsaw was a business style social network where, if you uploaded your Contacts database you had access to the huge online repository. It became so good, especially in the US that it was bought by SalesForce and re-branded Data.com. The Jigsaw tool was incredibly good as you could extract vast amounts of information from the Jigsaw database on company employees so the data was obfuscated by SalesForce to make it fairly useless to the researcher. However, it still provides a partial name, job role, office location and other useful data if we are purely looking for sets of information.
The Jigsaw tool is no longer available to the public by can be found on Kali (www.kali.org). I won’t talk you through running it, its pretty self-explanatory but you start with simply running a search on the company of choice.
jigsaw -s BankofAmerica
Searching for Bank of America provided over 9000 employee records which I duly downloaded to a csv file. Next, do a ‘data’ import into Excel, comma delimited, and save as an xls file.
Next use the import tool in Maltego and map the Employee field to a Person entity, Department to the Shop entity and the City to the Location entity. When I tried to import the entire 9000 records, Maltego tried to generate over 28000 nodes and edges and simply fell over, however I re-imported selecting every 3rd record which worked fine.
During the import process you are asked to map columns to eachother. Map Person to Location and Person to Shop.
Once imported select the bubble view and interactive organic mode. This will have the effect of clustering related data together. What is interesting is that employees are naturally ‘drawn’ to their City and departments primarily located in those Cities are also attracted.
We can straight away see the largest Yellow node (Location) bang in the middle of the cluster map is Charlotte, essentially most employees in the database say they work there.
A quick check online shows that Charlotte is indeed the BoA HQ. The next ones are New York and the surprising locations of San Francisco, Miami, Plano and Wilmington. This helps us to ID at a glance the primary locations.
Next, the grey nodes are Departments. Again the map shows that most work, unsurprisingly in Finance and Administration followed by IT & IS, Support Marketing and Operations. This can really help us to visually map out the organisation, giving us an idea of the comparative sizes of departments.
I am going to do a little more work on clustering Department to Location to help us know where primary departments are located. Im not suggesting that this leaks anything particularly bad or dangerous but is an interesting view for a social engineering attack to begin. It could be that a company hides (or at least doesn’t actively publish) its Research department locations but this approach could identify it.
OK, I’ve spent another hour playing around and there is some interesting data you can get from this view. I mapped purely Location to Department and it was immediately apparent that I could quickly see where departments were and more importantly were not.
We can see that IT & IS are in virtually every office, however:-
…we can see that Human Resources departments only appear to be represented in about 8 primary locations. This would be vital information for a social engineer who could make a simple error by saying in a Phishing phone call that they were in HR in Atlanta, but its possible that there is no (or at least not large) HR department in Atlanta.
Interesting stuff, have a play and let me know your findings.
Having spent the best part of the last decade working on Live Forensic techniques I’ve begun to turn my attention to OSX. I’m an unashamed MacHead but have not spent much time thinking about ways to extract data from a live machine.
Tucked away in a SQL Lite table is a large list of ‘Recent Contacts’
Knowing who a suspect speaks to or emails can be very useful in an investigation and so I’ve started looking at the email system in OSX. The inbuilt email app, Mail is very widely used and connects to the OSX Address Book for the management of contact data. However, tucked away in a SQL Lite table is a large list of ‘Recent Contacts’, which contains the name and email address of recently contacted people who may or may not be in your standard contacts.
You can see this list by opening OSX Mail and browsing to Window – Previous Recipients. This opens a box with all the recent contacts, but apart from being able to add the contact to your main contacts, there is no way to export them.
I’ve written a small shell script to extract the name and email from the SQL table and pop them in a csv file for you.
The code is very simple, just 2 lines:-
echo 'First Name,Surname,Email Address' > ~/Desktop/recentcontacts.csv
This simply writes the column heads to a CSV file on your Desktop
sqlite3 -csv ~/Library/Application\ Support/AddressBook/MailRecents-v4.abcdmr 'select ZFIRSTNAME, ZLASTNAME, ZEMAIL from ZABCDMAILRECENT;' >> ~/Desktop/recentcontacts.csv
This opens the MailRecents SQL file and pulls out the first name, last name and email address, writing them to the CSV file on your Desktop.
For ease just drop the file somewhere, ‘cd’ to it and run – ./recentexport.sh
If it doesn’t run you might have a permissions issue so just type – chmod +x recentexport.sh
Hope its useful to you.
This tool stems from the need to extract unencrypted Skype chat from a RAM dump.
Its a bit old now and needs some work but people still have good results from it:-
1. Run Strings against your RAM dump
2. Run the Skypeex tool against the resulting Strings file
3. It will carve out all the Skype chat lines it can see as well as trying to find and extract all the Skype sessions and ‘orphan’ chats that have been created.
It’s interesting to note that the latter process even seems to find the ‘spam’ message sessions that you sometimes receive.
This has been tested on dump files from Windows XP2, XP3 and 7 with Skype 3.8 through 4.2.
Please do not hesitate to get in touch with ideas and improvements.
skypeex26 is designed for use under Python 2.6
For best testing results, have several Skype IM chats with friends and then image your RAM. On a windows box, use any tool to grab RAM (tested on Win XP SP2/3):
I recommend dumpit from Matthieu Suiche – http://windd.msuiche.net/
Run strings against the RAM image (e.g. Windows version can be found in Helix distro)
example: strings c:\ramdump.dd > c:\stringsout.txt
On linux box do:
strings ramdump.dd > stringsout.txt
Script usage –
from command shell – python skypeex.py – then, when prompted, simply provide the path to the strings output file.
The output files will be written to the folder where the script is run from. The output is a CSV file with chats (incl headers) and a txt file with extracted skype sessions and carved orphan chats. Please expect many duplicates and some false positives.
In the CSV file the ‘Timestamp’ column is the date and time of the message in UNIX time. Sorting on this column gives you a timeline of messages. I’m writing a UNIX time decoder but it doesn’t work yet.
The primary message content is in the ‘body_xml’ column.
ivMeta is a tool designed to extract useful forensic metadata from iPhone video. It was written by Robin Wood from digininja.org (Top pen tester) in response to some research I posted about finding useful tags and data inside the iphone .mov filetype. (Read that here)
Unzip the zip container and read the readme file. You know, the thing you never read because you can figure it out!
The tool will extract phone make, software version, created data, GPS data etc.
Drop us a line with any bugs.