Here is a short post on what I found out about the "link" between Lazarus and WannaCry.
To me, the function referenced looks a lot like only a generator for a TLS 1.0 client hello.
On 2017-05-15 19:02 Neel Mehta tweeted the following:
Neel Mehta tweet, linking the samples of WannaCry and Lazarus / Contopee
Some more indicators for this claim that we look at Client Hellos is the usage in a function a bit up in the call chain:
Up the call stack, looking in which context the function / buffer is used.
Here our buffer generated by 0x402560 is send to localhost listening on typical TOR ports.
Maybe some part of the TOR communication capability (or adapter) was directly embedded in this earlier version of WannaCry?
Assessment:
While I agree that the compiled functions from both samples (A: WannaCry, B: Lazarus) originate very likely from the same source code and that they were compiled with similar tooling (there are some more indicators for this in how the generated code look likes, e.g. padding, thunks, ...), the exclusivity of the code defines the strength of the link.
This function provides a rather generic network-based
functionality (yet in a strongly specific way), so I would not be surprised if eventually the respective source code
appears as being publicly accessible in some corner of the wild and open Internet. In that case we could be looking at a super weird coincidence.
Hashes:
3e6de9e2baacf930949647c399818e7a2caea2626df6a468407854aaa515eed9 - WannaCry, February 2017
After hacking away for some days in the code chamber, I'm finally satisfied with the outcome and happy to announce the release of my new library: "ApiScout".
The main goal of ApiScout is to allow a faster migration from memory dumps to effective static analysis.
While reverse engineering "things" (especially malware), analysts often find themselves in a position where no API information is immediately available for use in IDA or other disassemblers.
This is pretty unfortunate, since API information is probably the single most useful feature for orientation in unknown binary code and a prime resource for recovery of meaning.
Usually, this information has to be recovered first: for example by rebuilding the PE ("clean unpacking", using ImpRec, Scylla, or similar) or by recording information about DLLs/APIs from the live process to be able to apply it later on (see Alex Hanel's blog post).
Both methods are potentially time-consuming and require manual effort to achieve their results. From my experience, clean unpacked files are often not even needed to conduct an efficient analysis of a target.
As I did a lot of dumping when reversing malware over the last years (and especially for malpedia - project outlook slides here), I craved for a more efficient solution.
Initially, I used a very hacky idapython script to "guess" imports in a given dump versus an offline DB - the limitations: 32bit and a single reference OS only.
After talking to some folks who liked the approach, I decided to refactor it properly and also integrate support for 64bit including ASLR.
To show the usefulness of this library, I have written both a command line tool and IDA plugin, which are explained in the remainder of this blog post.
First, let's have a look at a more or less common situation.
Executing it yields a very suspicious new svchost.exe process.
Running the Asprox sample results in a new suspicious scvhost.exe process.
Inspecting the memory of this new process reveals a not less suspicious memory section with RWX access rights and a decent size of 0x80000 bytes.
However, apparently the PE header got lost as can be seen on the left:
Looking closer at the process memory, we find a RWX segment @0x008D0000.
Luckily the import information is readily available:
Left (Hex view) /Right (Address view): Import Address Table (IAT) as found inside of the RWX segment.
With ImpRec or Scylla, we would now have to point to the correct IAT instead of using the handy IAT autosearch, because autosearch would identify the IAT of svchost.exe instead of Asprox' (see comparison left vs. right).
Left: Scylla IAT Autosearch gives IAT of svchost.exe, but we want ...
Right: IAT of Asprox - which we can't dump since PE header is missing.
But we now encounter another issue: Because there is no PE header available, Scylla fails to rebuild the binary and with that, the imports.
Granted, many injected memory sections will have more or less correct PE headers or we could write one from scratch...
But remember, I promised "painless" recovery in this blog post's title.
ApiScout: command-line mode
As I explained before, if we have all relevant API information available, we can directly locate IATs like the one of the above example.
So let's first build an API DB:
Running DatabaseBuilder.py to collect Windows API information from a running system.
While DatabaseBuilder.py is fully configurable, using Auto-Mode should yield good results already.
Next we can use the database to directly extract API information from our dump of memory section 0x008D0000:
Resultof running scout.py with the freshly build API DB against a memory dump of our injected Asprox.
Since this cmdline tool is just a demo for using the library, this should give you an idea of what can be achieved here.
For our example memory dump (76kb), I timed the full recovery (loading API DB, searching, shell output) on my system at about 0.3 seconds, so it's actually quite fast.
I am aware that this may occasionally lead to False Positives but there is also a filter option as a simple but effective measure: It requires that there is at least another identified API address within n bytes of neighbourhood - from my experience this is already enough to reduce the already very few FPs to an absolute minimum.
IDA ApiScout: fast-tracking import recovery
In this section, I want to showcase the beautified version of my old hacky script.
I assume it can be similarly adapted for others disassemblers like radare2, Hopper, or BinaryNinja.
Loading ida_scout.py as a script in IDA shows the following dialog in which an appropriate API DB can be selected.
Note that imports are not resolved as we loaded the memory as a binary (not PE) at fixed offset 0x008D0000:
ida_scout.py shows the available API DBs or can be used to load a DB from another place.
Executing the search with the WinXP profile from which Asprox was dumped, we now get a preview of the APIs that can be annotated:
Selection/Filter step of identified API candidates.
Aaaaand here we go, annotated API information:
Yay, annotated offsets in IDA as if we had a proper import table!
And yes, it's just as fast as it seems, clicking through both windows and having API information ready to go took less than 10 seconds.
That's what I call painless. :)
Dealing with ASLR
For simplicity's sake the above example was executed on WinXP 32bit, with no ASLR available.
However, it works just as fine for more recent versions (I use Windows 7 64bit), both for 64bit dumps or 32bit compatibility mode dumps.
In case you haven't disabled ASLR on your reference system, this section explains how ASLR offsets are obtained for all DLLs that are later stored in the DB.
I will skip explaining ASLR in detail, but feel free to read up on it, e.g. this report by Symantec.
The first step of DLL discovery is identical to non-ASLR systems and performed by DatabaseBuilder.py.
At the end of the crawling process (which involves collecting the ImageBase addresses as stated in the PE headers of all DLLs), we perform a heuristic check if ASLR is activated: We obtain a handle (which equals the in-memory BaseAddress) to three DLLs (user32.dll, kernel32.dll, and ntdll.dll) via GetModuleHandle() and check if the respective corresponding file as identified with GetModuleFileName() shows an identical ImageBase. If at least one DLL differs, we assume ASLR is active.
Since every DLL receives a individual ASLR offset, we will have to make sure that every DLL of interest has been loaded at least once.
For this purpose, I wrote a little helper binary "DllBaseChecker[32|64].exe" which simply performs a LoadLibrary() on a given DLL path and returns the load address.
Iterating through all DLLs identified in the discovery step, we are now able to determine each individual ASLR offset by subtracting file ImageBase and load address.
Closing Note
While this approach probably is certainly no magic or rocket science, I haven't seen it published in this form elsewhere yet. At least to me, it provides great convenience in several ways and I hope that one or the other can benefit from it as well.
For future use, I imagine it being used manually as shown in the post or potentially in automated analysis post-processing chains, where this functionality may come in handy.
I have to admit that I misjudged the effort to do code this in a nice way (by about a week of release-time) but I want to thank @herrcore for motivating me to rewrite and release it and @_jsoo_ for pushing me to address ASLR properly with the initial release version.
After some abstinence, I thought it might be a good idea to write something again. The perfect occasion came yesterday when I decided to build myself a new VM base image to be used for future malware analysis.
In that sense, this post is not immediately a tutorial for setting up a hardened virtual machine as there are so many other great resources for this already (see VM Creation). Maybe there is a good hint or two for you readers in here but it's mostly a write-up driven by my personal experience.
The main idea of this post is to outline some pitfalls I ran into yesterday, when relying on said resources. To have others avoid the same mistakes, I hope this post will fulfil its putpose.
In total I spent about 5 hours, 2 hours for setup and probably another 3 hours for testing but more about that later. This could have easily been only one hour or less if I knew everything I'll write down here beforehand. So here you go. :)
The remainder of this post is structured as follows:
Before starting out, it's good to know and plan where we are heading.
My Needs: I'm mostly interested in doing some rapid unpacking/dumping to feed my static analysis toolchain and then occasional do some debugging of malware to speed up my reasoning of selected code areas.
For this, I wanted a new base VM image that is able to run as much malware natively as possible, without me having to worry about Anti-Analysis methods.
Potentially, I want to deploy this image later as well for automation.
I don't aim for a perfect solution (perfection is the enemy of efficiency) but a reasonably good one.
OS choice: Windows 7 is still the most popular OS it seems, but since 64bit malware is getting more popular, we should take that into concern as well. So I go with Win7 x64 SP1 as base operating system.
Why not Win10: Well, I want a convenient way to disable ASLR and NX globbaly to allow my malware&exploits to flourish. Since I don't know if it's as easy in Win10 as it is in Win7, I stick with what I know for now.
Since I wanted to understand all the steps, I took VMCloak only for theoretical background. VBoxHardenedLoader is targeting a Win7 x64 as host system, however I use Ubuntu 16.04 with VirtualBox 5.0.24 so this wasn't immediately usable as well. But it's another excellent theoretical background resource.
Ultimately I ended up using antivmdetection as base for my endeavour.
Since I trial&error'd myself through the usage (in retrospect: I should do more RTFM and less fanatic doing), here's a summary of things you want to do before starting:
1) Download VolumeID (for x64)
2) Download DevManView (for x64)
3) # apt-get install acpidump (used by Python Script to fetch your system's parameters)
4) # apt-get install libcdio-utils (contains "cd-drive", used to read these params)
5) # apt-get install python-dmidecode (the pip-version of dmidecode is incompatible and useless for our purpose, so fetch the right one)
6) $ git clone https://github.com/nsmfoo/antivmdetection.git
7) $ cd antivmdetection :)
8) $ echo "some-username" > user.lst (with your desired in-VM username(s))
9) $ echo "some-computername" > computer.lst
First, I simply created a new empty Win7 x64 VM.
I used the following specs:
* CPU: 2 cores
* RAM: 4 GB
* HDD: 120 GB
* VT-x activated (needed for x64)
* GPU: 64 MB RAM (no acceleration)
* NIC: 1x Host-Only adapter (we don't want Internet connectivity right away or Windows may develop the idea of updating itself)
Important: Before mounting the Windows ISO, now is the time to use antivmdetection.py.
It will create 2 shell scripts:
1) <DmiSystemProduct>.sh <- Script to be used from outside the VM
2) <DmiSystemProduct>.ps1 <- Script to be used from inside the VM post installation
Run Antivmdetection (outside VM): For me <DmiSystemProduct> resulted in "AllSeries" because I run an ASUS board.
Okay, next step: execute <DmiSystemProduct>.sh - For me, this immediately resulted in a VM I could not start. Responsible for this were the 3 entries
1) DmiBIOSVersion
2) DmiBoardAssetTag
3) DmiBoardLocInChass
Which were set by <DmiSystemProduct>.sh to an integer value and VirtualBox was pretty unhappy with that fact, expecting a string. Changing these to random strings fixed the issue though. So this may be one of the pitfalls you may run into when using the tool. Setting the ACPI CustomTable however worked fine.
Now we have a fresh Windows 7 installation, time to mess it up.
Windows Configuration: Here are some steps to consider that may depend on personal taste.
1) Deactivate Windows Defender - Yes. Because. Malware.
2) Deactivate Windows Updates - We want to keep our system DLL versions fixed to be able to statically infer imported APIs later on.
3) Deactivate ASLR - We don't want our system DLL import addresses randomized later on. Basically, just create the following registry key (Credit to Ulbright's Blog):
[HKEY_LOCAL_MACHINE\SYSTEM\CurrentControlSet\Control\Session Manager\Memory Management] - “MoveImages”=dword:00000000
4) Deactivate NX - Whatever may help our malware to run... Basically, just run this in Windows command line (again Credit to Ulbright's Blog):
bcdedit.exe /set {current} nx AlwaysOff
5) Allow execution of powershell scripts - Enter a powershell and run:
> Set-ExecutionPolicy Unrestricted
Run Antivmdetection (in VM): Now we are good to execute the second script <DmiSystemProduct>.ps1.
Some of its benefits:
* ensure our registry looks clean
* remove the VirtualBox VGA device
* modify our ProductKey and VolumeID
* change the user and computer name
* create and delete a bunch of random files to make the system appear more "used".
* associate media files with Windows Media Player
* clean itself up and reboot.
I fiddled a bit with the powershell script to customize it further. Also, after reboot, I removed the file manipulation and reboot code itself to be able to run it whenever I need to after deploying my VM to new environments (additionally, this reduces the runtime from several minutes to <5sec).
Dependencies: Because malware and packers often require Visual C and NET runtimes, we install them as well. I used:
* MSVCRT 2005 x86 and x64
* MSVCRT 2008 x86 and x64
* MSVCRT 2010 x86 and x64
* MSVCRT 2012 x86 and x64
* MSVCRT 2013 x86 and x64
* MSVCRT 2015 x86 and x64
* MS.NET 4.5.2
Snapshot time! I decided to pack my VM now into an OVA to archive it and make it available for future use.
Now feel free to inflict further harm to your fresh VM.
Installing MS Office, Adobe Acrobat, Flash, Chrome, Firefox all come to mind.
Certainly DO NOT install VBoxGuestAdditions. The only benefits are better adaption of screen resolution and easy shared folders. For shared folders you can also just check out impacket's smbserver.py which gives you about the same utility with a one-liner from your host shell.
PAfish looking good:
Very good yet not perfect result. We happily ignore the VM exit technique.
This is no longer an issue when updating to VirtualBox version 5.1.4+, read below.
As initially mentioned, I spent another 3 hours with optimization and trying to get rid of the hypervisor detection.
Note that modifying the HostCPUID via VBoxManage does not fix the identity of VirtualBox which I basically learned the hard way.
Paravirtualization settings: VirtualBox allows you to choose a paravirtualization profile. They expose different combinations of hypervisor bit (HVB) and Hypervisor Vendor Leaf Name (HVN):
1) none (HVB=0, HVN="VBoxVBoxVBox")
2) default (HVB=1 HVN="VBoxVBoxVBox" but can be modified by patching /usr/lib/virtualbox/VBoxVMM.so as shown above, where we have "vbvbvbvbvbvb" instead)
3) legacy (HVB=0, HVN="VBoxVBoxVBox")
4) minimal (HVB=1, HVN="VBoxVBoxVBox")
5) Hyper-V (HVB=0, HVN="VBoxVBoxVBox" but this can also be modified like default mode)
6) KVM (HVB=0, HVN="KVMKVMKVMKVM")
I will probably spent some more time trying to figure out where the "VBoxVBoxVBox" string is exactly coming from (could not find it in other virtualbox binaries, nor in the src used by DKMS to build vboxdrv) and think it can be ultimately binary patched as well.
However, the issue itself is tied to my setup of VirtualBox, otherwise, I'm pretty sure that my VM itself is looking rather solid now in terms of anti-analysis detection, so we can conclude this write-up.
UPDATE 2017-02-06: nsmfoo suggested upgrading to VirtualBox 5.1.4+ to get rid of the hypervisor detection.
So I took his advice, moved up to VirtualBox version 5.1.14 (using this guide and this fix) and he was absolutely right:
This post ended up being a walkthrough of how I spent my last Saturday afternoon and evening.
I found nsmfoo's tool antivmdetection super useful but sadly ran into some initial trouble that cost me some time. Ultimately I ended up with a VM I am very happy with, although there remains an issue of VirtualBox's Hypervisor identification.
I wrote this post while listening through Infected Mushroom's new album "Return to the Sauce" which I can also heavily recommend. :)
