Sunday, June 06, 2021

Toolmarks: LNK Files in the news again

 As most regular readers of this blog can tell you, I'm a bit of a fan of LNK files...a LNK-o-phile, if you will. I'm not only fascinated by the richness of the structure, but as I began writing a parser for LNK files, I began too see some interesting aspects of intelligence that can be gleaned from LNK files, in particular, those created within a threat actors development environment, and deployed to targets/infrastructures. First, there are different ways to create LNK files using the Windows API, and what's really cool is that each method has it's own unique #toolmarks associated with it!  

Second, most often there is a pretty good amount of metadata embedded in the LNK file structure. There are file system time stamps, and often we'll see a NetBIOS system name, a volume S/N, a SID, or other pieces of information that we can use in a VirusTotal retro-hunt in order to build out a significant history of other similar LNK files.

In the course of my research, I was able to create the smallest possible functioning LNK file, albeit with NO (yes, you read that right...) metadata. Well, that's not 100% true...there is metadata within the LNK file. Specifically, the Windows version identifier is still there, and this is something I purposely left. Instead of zero'ing it out, I altered it to an as-yet-unseen value (in this case, 0x0a). You can also alter each version identifier to their own value, rather than keeping them all the same.

Microsoft recently shared some information about NOBELIUM sending LNK files embedded within ISO files, as did the Volexity team. Both discuss aspects of the NOBELIUM campaign; in fact, they do so in a similar manner, but each with different details. For example, the Volexity team specifically states the following (with respect to the LNK file):

It should be noted that nearly all of the metadata from the LNK file has been removed. Typically, LNK files contain timestamps for creation, modification, and access, as well as information about the device on which they were created.

Now, that's pretty cool! As someone who's put considerable effort into understanding the structure of LNK files, and done research into creating the smallest, minimal, functioning LNK file, this was a pretty interesting statement to read, and I wanted to learn more.

Taking a look at the metadata for the reports.lnk file (from fig 4 in the Microsoft blog post, and fig 3 of the Volexity blog post) we see

guid               {00021401-0000-0000-c000-000000000046}
shitemidlist    My Computer/C:\/Windows/system32/rundll32.exe
**Shell Items Details (times in UTC)**
  C:0                   M:0                   A:0                  Windows  (9)
  C:0                   M:0                   A:0                  system32  (9)
  C:0                   M:0                   A:0                  rundll32.exe  (9)

commandline  Documents.dll,Open
iconfilename   %windir%/system32/shell32.dll
hotkey             0x0
showcmd        0x1


GUID/ID pairs:
{46588ae2-4cbc-4338-bbfc-139326986dce}/4      SID: S-1-5-21-8417294525-741976727-420522995-1001

EnvironmentVariableDataBlock: %windir%/system32/explorer.exe

GUID  : {1ac14e77-02e7-4e5d-b744-2eb1ae5198b7}

While the file system time stamps embedded within the LNK file structure appear to have been zero'd out, a good deal of metadata still exists within the structure itself. For example, the Windows version information (i.e., "9") is still available, as are the contents of several ExtraData blocks. The SID listed in the PropertyStoreDataBlock can be used to search across repositories, looking for other LNK files that contain the same SID. Further, the fact that these blocks still exist in the structure gives us clues as to the process used to create the original LNK file, before the internal structure elements were manipulated.

I'm not sure that this is the first time this sort of thing has happened; after all, the MS blog post makes no mention of metadata being removed from the LNK file, so it's entirely possible that it's happened before but no one's thought that it was important enough to mention. However, items such as ExtraDataBlocks and which elements exist within the structure not only give us clues (toolmarks) as to how the file was created, but the fact that metadata elements were intentionally removed serve as additional toolmarks, and provide insight into the intentions of the actors.

But why use an ISO file? Well, interesting you should ask.  Matt Graeber said:

Adversaries choose ISO/IMG as a delivery vector b/c SmartScreen doesn't apply to non-NTFS volumes

In the ensuring thread, @arekfurt said:

Adversaries can also use the iso trick to put evade MOTW-based macro blocking with Office docs.

Ah, interesting points! The ISO file is downloaded from the Internet, and as such, would likely have a zone identifier ADS associated with it (I say, "likely" because I haven't seen it mentioned as a toolmark), whereas once the ISO file is mounted, the embedded files would not have zone ID ADSs associated with them. So, the decision to use an ISO file was intentional, and not just fact, it appears to have been intentionally used for defense evasion.

Testing, and taking DFIR a step further

One of Shakespeare's lines from Hamlet I remember from high school is, "...there are more things on heaven and earth, Horatio, than are dreamt of in your philosophy." And that's one of the great things about the #DFIR industry...there's always something new. I do not for a moment think that I've seen everything, and I, for one, find it fascinating when we find something that is either new, or that has been talked about but is being seen "in the wild" for the first time.

Someone mentioned recently that Microsoft's Antimalware Scan Interface (i.e., AMSI) could be used for persistence, and that got me very interested.  This isn't something specifically or explicitly covered by the MTRE ATT&CK framework, and I wanted to dig into this a bit more to understand it. As it can be used for persistence, it offers not only an opportunity for a detection, but also for a #DFIR detection and artifact constellation that can provide insight into threat actor sophistication and intent, as well as attribution. 

AMSI was introduced in 2015, and discussions of issues with it and bypassing it date back to 2016. However, the earliest discussion of the use of AMSI for persistence that I could find is from just last year. An interesting aspect of this means of persistence isn't so much as a detection itself, but rather how it's investigated. I've worked with analysis and response teams over the years, and one of the recurring questions I've had when something "bad" is detected is where that event occurred in relation to others. For example, whether you're using EDR telemetry or a timeline of system activity, all events tend to have one thing in common...a time stamp indicating the time at which they occurred. That is, either the event itself has an associated time stamp (file system time stamp, Registry key LastWrite time, PE file compile time, etc.), or some monitoring functionality is able to associate a time stamp with the observed event. As such, determining when a "bad" event occurred in relation to other events, such as a system reboot or a user login, can provide insight into determining if the event is the result of some persistence mechanism. This is necessary, as while EDR telemetry in particular can provide a great deal of insight, it is largely blind to a great deal of on-system artifacts (for example, Windows Event Log records). However, adding EDR telemetry to on-system artifact constellations significantly magnifies the value of both.

As I started researching this issue, the first resource to really jump out at me was this blog post from PenTestLab. It's thorough, and provides a good deal of insight as well as resources. For example, this post links to not only another blog post from b4rtik, but to a compiled DLL from netbiosX that you can download and use in testing. As a side note, be sure to install the Visual C++ redistributable on your system if you don't already have it, in order to get the DLL registration to work (Thanks to @netbiosX for the assist with that!)

I found that there are also other resources on the topic from Microsoft, as well.

Following the PenTestLab blog post, I registered the DLL, waited for a bit, and then collected a copy of the Software hive via the following command:

C:\Users\Public>reg save HKLM\Software software.sav

This testing provides the basis for developing #DFIR resources, including a RegRipper plugin. This allows detections to persist, particularly in larger environments, so that corporate knowledge is maintained.

It also sets the stage for further, additional testing. For example, you can install Sysmon, open a Powershell command prompt, submit the test phrase, and then create a timeline of system activity once calc.exe opens, to determine (or begin developing) the artifact constellation associated with this use of 

Speaking of PenTestLab, Sysmon, and taking things a step further, there's this blog post from PenTestLab on Threat Hunting AMSI Bypasses, including not just a Yara rule to run across memory, but also a Registry modification that indicates yet another AMSI bypass.

Monday, April 12, 2021

On #DFIR Analysis, pt III - Benefits of a Structured Model

 In my previous post, I presented some of the basic design elements for a structured approach to describing artifact constellations, and leveraging them to further DFIR analysis. As much of this is new, I'm sure that this all sounds like a lot of work, and if you've read the other posts on this topic, you're probably wondering about the benefits to all this work. In this post, I'll take shot at netting out some of the more obvious benefits.

Maintaining Corporate Knowledge

Regardless of whether you're talking about an internal corporate position or a consulting role, analysts are going to see and learn new things based on their analysis. You're going to see new applications or techniques used, and perhaps even see the same threat actor making small changes to their TTPs due to some "stimulus". You may find new artifacts based on the configuration of the system, or what applications are installed. A number of years ago, a co-worker was investigating a system that happened to have LANDesk installed, along with the software monitoring module. They'd found that the LANDesk module maintains a listing of executables run, including the first run time, the last run time, and the user account responsible for the last execution, all of which mapped very well in to a timeline of system activity, making the resulting timeline much richer in context.

When something like this is found, how is that corporate knowledge currently maintained? In this case, the analyst wrote a RegRipper plugin, that still exists and is in use today. But how are organizations (both internal and consulting teams) maintaining a record of artifacts and constellations that analysts discover?

Maybe a better question is, are organizations doing this at all?  

For many organizations with a SOC capability, detections are written, often based on open reporting, and then tested and put into production. From there, those detections may be tuned; for internal teams, the tuning would be based on one infrastructure, but for MSS or consulting orgs, the tuning would be based on multiple (and likely an increasing number of) infrastructures. Those detections and their tuning are based on the data source (i.e., SIEM, EDR, or a combination), and serve to preserve corporate knowledge. The same approach can/should be taken with DFIR work, as well.


One of the challenges inherent to large corporate teams, and perhaps more so to consulting teams, is that analysts all have their own way of doing things. I've mentioned previously that analysis is nothing more that an individual applying the breadth of their knowledge and experience to a data source. Very often, analysts will receive data for a case, and approach that data initially based on their own knowledge and experience. Given that each analyst has their own individual approach, the initial parsing of collected data can be a highly inefficient endeavor when viewed across the entire team. And because the approach is often based solely on the analyst's own individual experience, items of importance can be missed. 

What if each analyst were instead able to approach the data sources based not just on their own knowledge and experience, but the collective experience of the team, regardless of the "state" (i.e., on vacation/PTO, left the organization, working their own case, etc.) of the other analysts? What if we were to use a parsing process that was not based on the knowledge, experience and skill of one analyst but instead on that of all analysts, as well as perhaps some developers? That process would normalize all available data sources, regardless of the knowledge and experience of an individual analyst, and the enrichment and decoration phase would also be independent of the knowledge and skill of a single analyst.

Now, something like this does not obviate the need for analysts to be able to conduct their own analysis, in their own way, but it does significantly increase efficiency, as analysts are no longer manually parsing individual data sources, particularly those selected based upon their own experience. Instead, the data sources are being parsed, and then enriched and decorated through an automated means, one that is continually improved upon. This would also reduce costs associated with commercial licenses, as teams would not have to purchase each analyst licenses for several products (i.e., "...I prefer this product to this work, and this other product for these other things...").

By approaching the initial phases of analysis in such a manner, efficiency is significantly increased, cost goes way down, and consistency goes through the roof. This can be especially true for organizations that encounter similar issues often. For example, internal organizations protecting corporate assets may regularly see certain issues across the infrastructure, such as malicious downloads or phishing with weaponized attachments. Similarly, consulting organizations may regularly see certain types of issues (i.e., ransomware, BEC, etc.) based on their business and customer base. Having an automated means for collecting, parsing, and enriching known data sources, and presenting them to the analyst saves time and money, gets the analyst to conducting analysis sooner, and provides for much more consistent and timely investigations.

Artifacts in Isolation

Deep within DFIR, behind the curtains and under the dust ruffle, when we see what really goes on, we often see analysts relying far too much on single sources of data or single artifacts for their analysis, in isolation from each other. This is very often the result of allowing analysts to "do their own thing", which while sounding like an authoritative way to conduct business, is highly inefficient and fraught with mistakes.

Not long ago, I heard a speaker at a presentation state that they'd based their determination of the "window of compromise" on a single data point, and one that had been misinterpreted. They'd stated that the ShimCache/AppCompatCache timestamp for a binary was the "time of execution", and extended the "window of compromise" in their report from a few weeks to four years, without taking time stomping into account. After the presentation was over, I had a chat with the speaker and walked through my reasoning. Unfortunately, the case they'd presented on had been completed (and adjudicated) two years prior to the presentation. For this case, the victim had been levied a fine based on the "window of compromise".

Very often, we'll see analysts referring to a single artifact (ShimCache entry, perhaps an entry in the AmCache.hve file, etc.) as definitive proof of execution. This is perhaps an understanding based on over-simplification of the nature of the artifact, and without corresponding artifacts from the constellation, will lead to inaccurate findings. It is often not until we peel back the layers of the analysis "onion" that it becomes evident that the finding, as well as the accumulated findings of the incident, were incorrectly based on individual artifacts, from data sources viewed in isolation from other pertinent data sources. Further, the nature of those artifacts being misinterpreted; rather than demonstrating program execution, they simply illustrate the existence of a file on the system.


Over the years that I've been doing DFIR work, little in the way of "analysis" has changed. We started out with getting a few hard drives that we imaged and analyzed, to going on-site to do collections of images, then to doing triage collections to scope the systems that needed to be analyzed. We then extended our reach further through the use of EDR telemetry, but analysis still came down to individual analysts applying just their own knowledge and experience to collected data sources. It's time we change this model, and leverage the capabilities we have on hand in order to provide more consistent, efficient, accurate, and timely analysis.

Saturday, April 10, 2021

On #DFIR Analysis, pt II - Describing Artifact Constellations

 I've been putting some serious thought into the topic of a new #DFIR model, and in an effort to extend and expand upon my previous post a bit, I wanted to take the opportunity to document and share some of my latest thoughts.

I've discussed toolmarks and artifact constellations previously in this blog, and how they apply to attribution. In discussing a new #DFIR model, the question that arises is, how do we describe an artifact or toolmark constellation in a structured manner, so that it can be communicated and shared?  

Of course, the next step after that, once we have a structured format for describing these constellations, is automating the sharing and "machine ingestion" of these constellation descriptions. But before we get ahead of ourselves, let's discuss a possible structure a bit more. 

The New #DFIR Model

First off, to orient ourselves, figure 1 illustrates the proposed "new" #DFIR model from my previous blog post. We still have the collect, parse, and enrich/decorate phases prior to the output and data going to the analyst, but in this case, I've highlighted the "enrich/decorate" phase with a red outline, as that is where the artifact constellations would be identified.

Fig 1: New DFIR Model 
We can assume that we would start off by applying some known constellation descriptions to the parsed data during the "enrich/decorate" phase, so the process of identifying a toolmark constellation should also include some means of pulling information from the constellation, as well as "marking" or "tagging" the constellation in some manner, or facilitating some other means of notifying the analyst. From there, the expectation would be that new constellations would be defined and described through analysis, as well as through open sources, and applied to the process.

We're going to start "small" in this case, so that we can build on the structure later. What I mean by that is that we're going to start with just DFIR data; that is, data collected as either a full disk acquisition, or as part of triage response to an identified incident. We're going to start here because the data is fairly consistent across Windows systems at this point, and we can add EDR telemetry and input from a SIEM framework at a later date. So, just for the sake of  this discussion, we're going to start with DFIR data.

Describing Artifact Constellations

Let's start by looking a common artifact constellation, one for disabling Windows Defender. We know that there are a number of different ways to go about disabling Windows Defender, and that regardless of the size and composition of the artifact constellation they all result in the same MITRE ATT&CK sub-technique. One way to go about disabling Windows Defender is through the use of Defender Control, a GUI-based tool. As this is a GUI-based tool, the threat actor would need to have shell-based access to the system, such through a local or remote (Terminal Services/RDP) login. Beyond that point, the artifact constellation would look like:
  • UserAssist entry in the NTUSER.DAT indicating Defender Control was launched
  • Prefetch file created for Defender Control (file system/MFT; not for Windows server systems)
  • Registry values added/modified in the Software hive
  • "Microsoft-Windows-Windows Defender%4Operational.evtx" event records generated
Again, this constellation is based solely on DFIR or triage data collected from an endpoint. Notice that I point out that one artifact in the constellation (i.e., the Prefetch file) would not be available on Windows server systems. This tells us that when working with artifact constellations, we need to keep in mind that not all of the artifacts may be available, for a variety of reasons (i.e., version of Windows, system configuration, installed applications, passage of time, etc.). Other artifacts that may be available but are also heavily dependent upon the configuration of the endpoint itself include (but are not limited to) a Security-Auditing/4688 event in the Security Event Log pertaining to Defender Control, indicating the launch of the application, or possibly a Sysmon/1 event pertaining to Defender Control, again indicating the launch of the application. Again, the availability of these artifacts depends upon the specific nature and configuration of the endpoint system.

Another means to achieve the same end, albeit without requiring shell-based access, is with a batch file that modifies the specific Registry values (Defender Control modifies two Registry values) via the native LOLBIN, reg.exe. In this case, the artifact constellation would not need to (although it may be) be preceded by a Security-Auditing/4624 (login) event of either type 2 (console) or type 10 (remote). Further, there would be no expectation of a UserAssist entry (no GUI tool needs to be launched), and the Prefetch file creation/modification would be for reg.exe, rather than Defender Control.  However, the remaining two artifacts in the constellation would likely remain the same.

Fig 2: WinDefend Exclusions
Of course, yet another means for "disabling Windows Defender" could be as simple as adding an exclusion to the tool, in any one or more of the five subkeys illustrated in figure 2. For example, we've seen threat actors create exceptions for any file ending in ".exe", found in specific paths, or any process such as Powershell.

The point is that while there are different ways to achieve the same end, each method has its own unique toolmark constellation, and the constellations could then be used to apply attribution.  For example, the first method for disabling Windows Defender described above was observed being used by the Snatch ransomware threat actors during several attacks in May/June 2020. Something like this would not be exclusive, of course, as a toolmark constellation could be applied to more than one threat actor or group. After all, most of what we refer to as "threat actor groups" are simply how we cluster IOCs and TTPs, and a toolmark constellation is a cluster of artifacts associated with the conduct of particular activity. However, these constellations can be applied to attribution.

A Notional Description Structure

At this point, a couple of thoughts or ideas jump out at me.  First, the individual artifacts within the constellation can be listed in a fashion similar to what's seen in Yara rules, with similar "strings" based upon the source. Remember, by the time we're to the "enrich/decorate" phase, we've already normalized the disparate data sources into a common structure, perhaps something similar to the five-field TLN format used in (my) timelines. The time field of the structure would allow us to identify artifacts within a specified temporal proximity, and each description field would need to be treated or handled (that is, itself parsed) differently based upon the source field. The source field from the normalized structure could be used in a similar manner as the various 'string' identifiers in Yara (i.e., 'ascii', 'nocase', 'wide', etc.) in that they would identify the specific means by which the description field should be addressed. 

Some elements of the artifact constellation may not be required, and this could easily be addressed through something similar to Yara 'conditions', in that the various artifacts could be grouped with parens, as well as 'and' and 'or', identifying those artifacts that may not be required for the constellation to be effective, although not complete. From the above examples, the Registry values being modified would be "required", as without them, Windows Defender would not be disabled. However, a Prefetch file would not be "required", particularly when the platform being analyzed is a Windows server. This could be addressed through the "condition" statement used in Yara rules, and a desirable side effect of having a "scoring value" would be that an identified constellation would then have something akin to a "confidence rating", similar to what is seen on sites such as VirusTotal (i.e., "this sample was identified as malicious by 32/69 AV engines"). For example, from the above bulleted artifacts that make up the illustrated constellation, the following values might be applied:

  • Required - +1
  • Not required - +1, if present
  • +1 for each of the values, depending upon the value data
  • +1 for each event record
If all elements of the constellation are found within a defined temporal proximity, then the "confidence rating" would be 6/6. All of this could be handled automatically by the scanning engine itself.

A notional example constellation description based on something similar to Yara might then look something like the following:


    $str1 = UserAssist entry for Defender Control
    $str2 = Prefetch file for Defender Control
    $str3 = Windows Defender DisableAntiSpyware value = 1
    $str4 = Windows Defender event ID 5010 generated
    $str5 = Windows Defender DisableRealtimeMonitoring value = 1
    $str6 = Windows Defender event ID 5001 generated


    $str1 or $str2 and ($str3 and $str4 and $str5 and $str6);

Again, temporal proximity/dispersion would need to be addressed (most likely within the scanning engine itself), either with an automatic 'value' set, or by providing a user-defined value within the rule metadata. Additionally, the order of the individual artifacts would be important, as well. You wouldn't want to run this rule and in the output find that $str1 was found 8 days after the conditions for $str3 and $str5 being met. Given that the five-field TLN format includes a time stamp as its first field, it would be pretty trivial to compute a temporal "Hamming distance", of sorts, a well as ensure proper sequencing of the artifacts or toolmarks themselves.  That is to say that $str1 should appear prior to $str3, rather than after it, but not so far so as to be unreasonable and create a false positive detection.

Finally, similar to Yara rules, the rule name would be identified in the output, along with a "confidence rating" of 6/6 for a Windows 10 system (assuming all artifacts in the cluster were available), or 5/6 for Windows Server 2019.


Something else that we need to account for when addressing artifact constellations is counter-forensics, even that which is unintentional, such as the passage of time. Specifically, how do we deal with identifying artifact constellations when artifacts have been removed, such as application prefetching being disabled on Windows 10 (which itself may be part of a different artifact constellation), or files being deleted, or something like CCleaner being run?

Maybe a better question is, do we even need to address this circumstance? After all, the intention here is not to address every possible eventuality or possible circumstance, and we can create artifact constellations for various Windows functionality being disabled (or enabled).

Thursday, April 01, 2021

LNK Files, Again

 I ran across SharpWebServer via Twitter recently...the first line of the file states, "A Red Team oriented simple HTTP & WebDAV server written in C# with functionality to capture Net-NTLM hashes." I thought this was fascinating because it ties directly to a technique MITRE refers to as "Forced Authentication".  What this means is that a threat actor can (and has...we'll get to that shortly) modify Windows shortcut/LNK files such that the iconfilename field points to an external resource. What happens is that when LNK file is launched, Explorer will reach out to the external resource and attempt to authenticate, sending NTLM hashes across the wire.  As such, SharpWebServer is built to capture those hashes.

What this means is that a threat actor can gain access to an infrastructure, and as has been observed, use various means to maintain persistence...drop backdoors or RATs, create accounts on Internet-facing systems, etc.  However, many (albeit not all) of these means of persistence can be overcome via the judicious use of AV, EDR monitoring, and a universal password change.

Modifying the iconfilename field of an LNK file is a means of persisting beyond password changes, because even after passwords are change, the updated hashes will be sent across the wire.

Now, I did say earlier that this has been used before, and it has.  CISA Alert TA18-074A includes a section named "Persistence through LNK file manipulation". 

Note that from the alert, when looking at the "Contents of enu.cmd", "Persistence through LNK file manipulation", and "Registry Modification" sections, we can see a pretty comprehensive set of toolmarks associated with this threat actor.  This is excellent intrusion intelligence, and should be incorporated into any and all #DFIR parsing, enrichment and decoration, as well as threat hunting.

However, things are even better! This tweet from bohops illustrates how to apply this technique to MSWord docs.

On #DFIR Analysis

I wanted to take the opportunity to discuss DFIR analysis; when discussing #DFIR analysis, we have to ask the question, "what _is_ "analysis"?"

In most cases, what we call analysis is really just parsing some data source (or sources) and either viewing the output of the tools, or running keyword searches.  When this is the entire process, it is not's running keyword searches. Don't get me wrong, there is nothing wrong with keyword searches, as they're a great way to orient yourself to the data and provide pivot points into further analysis.  However, these searches should not be considered the end of your analysis; rather, they are simply be beginning, or at least early stages of the analysis. The issue is that parsing data sources in isolation from each other and just running keyword searches in an attempt to conduct "analysis" is insufficient for the task, simply due to the fact that the results are missing context.

We "do analysis" when we take in data sources, perhaps apply some parsing, and then apply our knowledge and experience to those data sources.  This is pretty much how it's worked since I got started in the field over 20 yrs ago, and I'll admit, I was just following what I had seen being done before me.  Very often, this "apply our knowledge and experience" process has been abstracted through a commonly used commercial forensic analysis tool or framework (i.e., EnCase, X-Ways, FTK, Autopsy, to name a few...). 

The process of collecting data from systems has been addressed by many at this point. There are a number of both free and commercially available tools for collecting information from systems. As such, all analysts need to do at this point is keep up with changes and updates to the target operating systems and applications, and ensure the appropriate sources are included in their collections.

Over time, some have worked to make the parsing and analysis process more efficient, by automating various aspects of the process, by either setting up processes via the commercial tools, or by using some external means.  For example, looking way back in the mists of time when Chris "CPBeefcake" Pogue and I were working PCI engagements as part of the IBM ISS ERS team, we worked to automate (as much as possible) the various searches (hashes, files names, path names) required by Visa (at the time) so that they were done in as complete, accurate, and consistent manner as possible. Further, tools such as plaso, RegRipper, and others provide a great deal of (albeit incomplete) parsing capability. This is not simply restricted to freely available tools; back when I was using commercial tool suites, I extended my use of ProDiscover, while I watched others simply use other commercial tools as they were "out of the box".

A great example of extending something already available in order to meet your needs can be found in this FireEye blog post, where the authors state:

We adapted the...parsing code to make it more robust and support all features necessary for parsing QMGR databases.

Overall, a broad, common issue with both collection and parsing tools is not with the tools themselves, but how they're viewed and used.  Analysts using such tools very often do little really identify their own needs, and then to update or extend those tools, looking at their interaction with the tools as the end of their involvement in the process, rather than the beginning.

So, while this automates some tasks, the actual analysis is still left to the experience and knowledge of the individual analyst, and for the most part, does not extend much beyond that. This includes not only what data sources and artifacts to look to, but also the context and meaning of those (and other) data sources and artifacts. However, as Jim Mattis stated in his book, "Call Sign Chaos", "...your personal experiences alone are not broad enough to sustain you." While this statement was made specifically within the context of a warfighter, the same thing is true for DFIR analysts. So, the question becomes, how can we implement something like this in DFIR, how do we broaden the scope of our own personal experiences, and build up the knowledge and experience of all analysts, across the board, in a consistent manner?

The answer is that, much like McChrystal's "Team of Teams", we need a new model.

Fig 1: Process Schematic
A New DFIR Model

Back in the day...I love saying that, because I'm at the point in my career where I can..."DFIR" meant getting a call and going on-site to collect data, be it images, logs, triage data, etc. 

As larger, more extensive incidents were recognized and became more commonplace, there was a shift in the industry to where some DFIR consulting firms were providing EDR tools to the customer to install, the telemetry for which reported back to a SOC. Initial triage and scoping could occur either prior to an analyst arriving on-site, or the entire engagement could be run, from a technical perspective, remotely.  

Whether the engagement starts with a SOC alert, or with a customer calling for DFIR support and EDR being pushed out, at some point, data will need to be collected from a subset of systems for more extensive analysis. EDR telemetry alone does not provide all the visibility we need to respond to incidents, and as such, collecting triage data is a very valuable part of the overall process.  Data is collected, parsed, and then "analyzed". For the most part, there's been a great deal of work in this area, including here, and here. The point is that there have been more than a few variations of tools to collect triage data from live Windows systems.

Where the DFIR industry, in the general sense, falls short in this process (see fig 1) is right around the "analysis" phase. This is due to the fact that, again, "analysis" consists of each analyst applying the sum total of their own knowledge and experience to the data sources (triage data collected from systems, log data, EDR telemetry, etc.). 

Why does it "fall short"?  Well, I'll be the first to tell you, I don't know everything. I've seen a lot of ransomware and targeted ("nation state", "cybercrime") threat actors during my time, but I haven't seen all of them.  Nor have I ever done a BEC engagement. Ever. I haven't avoided them or turned them down, I've just never encountered one. This means that the analysis phase of the process is where things fall short. 

So how do we fix that?  One way is that if I take everything I findings, lessons learned, anything I find via open sources...and "bake it back into" the overall process via a feedback loop.  Now, this is something that I've done partially through several tools that I use regularly, including RegRipper, eventmap.txt, etc. This way, I don't have to rely on my fallible memory; instead, I add this new information to the automated process, so that when I parse data sources, I also "enrich" and "decorate" appropriate fields.  I'm already automating the parsing so that I don't miss something important, and now, I can increase visibility and context by automating the enrichment and decoration phase.

Now, imagine how powerful this would be if we took several steps.  First, we make this available to all analysts on the team. What one analyst learns instantly becomes available to all analysts, as the experience and knowledge of one is shared with many. Steve learns something new, and it's immediately available to David, Emily, Margo, and all of the other analysts. You do not have to wait until Steve works directly with Emily on an engagement, and you do not have to hope that the subject comes up. The great thing is that if you make this part of the DFIR culture, it works even if Steve goes on paternity leave or a family vacation, and it persists beyond any one analyst leaving the organization entirely.

Second, we further extend our enrichment and decoration capability by incorporating threat intelligence. If we do so initially using open reporting, we can greatly extend that open reporting by providing actual intrusion intelligence. We can use open reporting to take what others see on engagements that we have yet to experience, and use that to extend our own experience. Further, the threat intelligence produced (if that's something you're doing) now incorporates actual intrusion intel, which is tied directly to on-system artifacts. For example, while open reporting may state that a particular threat actor group "disables Windows Defender", intrusion intel from those incidents will tell us how they do so, and when during the attack cycle they take these actions. This can provide insight into better tooling and visibility, earlier detection of threat actors, and a much more granular picture of what occurred on the system.

Third, because this is all tied to the SOC, we can further extend our capabilities by baking new DFIR findings back into the SOC in the form of detections. This feedback loop leads to higher fidelity detections that provide greater context to the SOC alerts themselves. A great example of this feedback process can be seen here; while this blog post just passed it's 5th birthday, all that means is that the process worked then and is still equally, if not more, valid today. The use of WMI persistence led directly to the creation of new high-fidelity SOC EDR detections, which provided significantly greater efficacy and context.

While everyone else is talking about 'big data', or the 'lack of cybersecurity skills', there is a simple approach to addressing those issues, and more...all we need to do is change the business model used to drive DFIR, and change the DFIR culture. 

Tuesday, March 23, 2021

Extracting Toolmarks from Open Source Reporting, pt II

On the heels of my previous post on this subject, I ran across this little gem from Microsoft regarding the print spooler EOP exploitation. I like articles like this because they illustrate threat actor activities outside the "norm", or what we usually tend to see in open reporting, if such things are illustrated in detail.

Fig 4 (in step 1) in the article illustrates a new printer port being added to a Windows system as a step toward privilege escalation. This serves as one of the more-than-a-few interesting EDR-style tidbits from the article (i.e., detect the Powershell commandline), and also results in a fantastic toolmark that can be applied to DFIR "threat hunting". 

The article illustrates, via fig 4, Powershell being used to add a printer port to the system, and that command results in a value being added to the Registry. There are other ways to go about this, of course, and this is only one example of how to achieve adding a printer port to a Windows system.  For example, you can use "win32_tcpipPrinterPort" via WMI to add a TCP printer port.

Whichever means is used, the end result is a value being added to the Registry, which means if there is no EDR capability on the system at the time that the port is created, we can still determine if a port was, in fact, created. This would be reflected in a new value being added to the Registry key, and the key LastWrite time being updated accordingly. The RegRipper plugin will extract this information, and creating a timeline that includes Registry key LastWrite times will likely show the Registry key modification within the timeline.

Other RegRipper plugins that address toolmarks from open reporting associated with printers on Windows include: - retrieves printer attributes, looking for indications that printers were set to not delete print jobs after they were spooled and sent to the printer (per Project TajMahal open reporting), enabling local data staging. - this open reporting on Winnti indicates that print processors have been used for persistence.

The purpose of this blog post (as well as the previous one) has been to illustrate a means by which open reporting can be incorporated into any analyst's process. In this example, I've shared the names of RegRipper plugins I've created, so that the toolmarks, along with the appropriate MITRE ATT&CK mappings and analysis tips, are always available to me during my analysis. As I've included the online resources in the headers of the plugins, I have those resources available should I need to refer to them. Overall, this adds breadth, depth, and most importantly, consistency to my analysis process, letting me get to the actual analysis much sooner. From this point, I can then curate any new findings or lessons learned based on that analysis, and bake that back into my process, extending that capability yet again.

There are, of course, other means to go about baking new findings and lessons learned back into your analysis process.  However, it depends largely upon what you're able to ingest or incorporate.  For example, if you're doing malware analysis or any sort of log analysis, Yara rules might provide a good option for you. The Nuix Investigate product includes extensions for both RegRipper and Yara rules, extending the capabilities of the product.

Yet another means is to use something like wevtx.bat, which uses eventmap.txt to provide a small modicum of enrichment to specific Windows Event Log records as they're added to a timeline.

These are all very basic means for extending your current analysis process with new toolmarks from open reporting, as well as baking new findings and lessons learned back into the process.

Thursday, January 28, 2021

Extracting Toolmarks from Open Source Intel

I've talked about toolmarks before...what they are, why (I believe) they're important, that sort of thing.  I've also described how I've implemented them, and about toolmarks specific to different artifacts, such as LNK files. The primary source for toolmarks should be the investigations you're performing; when you do data collection pursuant to an investigation, those toolmarks that you develop should be baked back into your analysis process.  For #DFIR consulting businesses, this is a truly powerful use of the petabytes of data flowing through your organization on a monthly basis, driving toward increasingly efficient analysis and reducing the engagement/SOW lifecycle.

While your own investigations should be the primary source of toolmarks, you can also take advantage of open source reporting to extend this capability. In some cases, open source reports are full of unrealized toolmarks, which any organization can leverage to extend their detection and threat hunting (including #DFIR threat hunting) capabilities. 

I know what you're would you go about doing that?  How do you turn open source reporting into something actionable, leveraging toolmarks to extend your organization's capabilities?  Well, let's take a look...

Recently, Microsoft published a security blog regarding the 2nd stage activation from SunBurst, and based on the information they provided in the article, I thought that this would be a good opportunity to illustrate how to extract or realize toolmarks from open source reporting. The Microsoft article is a great example, because it is chock full of intrusion intel that leads directly to toolmarks.

For example, consider fig. 3 in the article; step 3 shows an "Image File Execution Options" Debugger value being set for the dllhost.exe executable.  The toolmark here is obvious; a new subkey is created, and a new value is added to that subkey. In step 6 we see that the Debugger value is deleted; at this point, the question is, is the dllhost.exe subkey left in place?  If so, the LastWrite time of the key would correspond to when the Debugger value was deleted; if not, and the dllhost.exe subkey is also deleted, then the residual toolmark becomes the LastWrite time of the "Image File Execution Options" key.  As a result, if the time stamp toolmark in question falls within the window of other interesting activity, then you likely have an actionable toolmark associated with this activity.

Make sense?

A couple of paragraphs below the figure, we see the following statement:

Finally, the VBScript removes the previously created IFEO value to clean up any traces of execution (step #6) and also deletes the following registry keys related to HTTP proxy:

Nomenclature alert...the two subsequent paths listed are actually to Registry values, not keys. As a result, in this case, the LastWrite time of the "Internet Settings" key would correspond closely to the above toolmark (i.e., IFEO key LastWrite time).  These two time stamps are very specific set of toolmarks related to this specific activity.

Now, let's mosy on down to the section of the article that mentions "anti-forensic behavior" because, well, this is the fun stuff.  The second bullet in that section includes the statement, "...WMI persistence filters were created..."; basically what this tells us is, be sure you're parsing the OBJECTS.DATA file!  So, different data source (i.e., not the Registry in this case), but a definite toolmark.

The third bullet in the "anti-forensic" section states, "...the attackers took care of disabling event logging using AUDITPOL and re-enabling it afterward."  Oh, now THIS is cool! We see in the table following this section that the command used is:

auditpol /set /category:”Detailed Tracking” /success:disable /failure:disable

This command modifies the "Default" value beneath the Policy\PolAdEvt key in the Security hive, which in turn causes the LastWrite time of the key to be updated.  The article then states that when the threat actor completes their activity, they set "success" and "failure" to "enable"; at this point, the toolmark is the LastWrite time of the key, and the value settings themselves.

Next, when the threat actor modifies the Windows firewall by adding a rule via netsh.exe, the action results in a modification to the Windows Registry, as does the use of sc.exe and reg.exe to disable Windows services remotely and locally, respectively, and the use of net.exe to map a OneDrive share. All of these actions result in a modification to the system that is evident tithin the Windows Registry, and all you need to do is pull them out in a timeline to see how close they are, temporally.

Another example of extracting toolmarks from open source reporting can be found in this article that describes how to use the finger.exe client to transfer files between systems. The article describes the use of netsh.exe to set up a port proxy in order to get port 79 TCP traffic out of the network.  The command used is similar to the one described in this Mandiant article on RDP tunneling, which results in a modification to the HKLM\System\CurrentControlSet\services\PortProxy\v4tov4\tcp key, adding a value. When the port proxy configuration is removed, the action can be indicated by the LastWrite time of the tcp key being updated.

An important aspect to keep in mind is detection and response time with respect to the toolmarks being created.  What I mean by that is that if response occurs relatively close to the action occurring (i.e., a Registry key and/or value being added), then the modification may exist in the transaction log, and may yet to be written to the hive.  This is also true with respect to the deletion.  Also, if the key LastWrite times correspond to the window of suspicious activity, but the keys/values do not exist, be sure to parse unallocated space within the hive file to determine if the deleted nodes can still be found and extracted.

While your own investigations should continue to be the primary source of actionable toolmarks that you apply back to or bake back into your analysis process, incorporating toolmarks developed and leveraged from open source reporting can significantly extend your reach and capabilities.