Heap & File Carving

Along with the newly released 2.9 version of Profiler Advanced, we have improved support for memory images.

Before going into the main topics of this post, it is worth mentioning that loading and scanning times have been drastically improved for memory images. Apart from the important internal optimizations, the user is now given the choice of scanning or not the files in memory.

If the user chooses not to scan the files in memory right away (they are still scanned once individually opened in the UI), the loading process of the memory image takes only a few seconds if that.

One of the main news in 2.9 is the support for heap parsing for all Windows versions from Windows XP to Windows 10. The only limitation is that we don’t yet support the new heap type found in Metro/Modern applications. This new type of heap can also be found in certain Windows 10 processes such as: smss.exe, csrss.exe, services.exe, lsass.exe, svchost.exe, MicrosoftEdgeC, etc.

The heap of every process can be inspected in the UI.

It’s not trivial to parse a heap which might also be corrupted because of paged out memory regions. We put effort into parsing what is available.

You might wonder why the effort of parsing the heap in the first place. The main reason was due to a new feature introduced in Profiler 2.9, namely file identification in memory regions. Since we anyway wanted to identify files loaded in memory regions, we thought it was a good idea to try to identify files in the heap as well. Hence, the user can now enable file identification in memory regions and heap.

Of course, the user can inspect all the identified files in the UI. This for instance is an image found in the heap of the dwm process.

The user may also choose to dump all identified files to a specific folder.

The dump output is divided into directories, one for each process.

Each process contains one or all of the following directories and an info.txt file.

The info.txt file contains a list of dumped files and their respective names in the Profiler UI, along with possible errors:

We’ll further improve this feature in the next time, so stay tuned!

Windows Memory Forensics: Close to Release

We’re extremely proud to announce that the upcoming 2.8 version of Profiler Advanced comes with full-fledged support for raw Windows memory images! As few of our users might remember a two years old demo about this topic. Thanks to the work of the past months of our team, we could finalize that idea into a real product.

This is a hex view showing the user space regions of a process on Win8 x64.

We currently support WinXP to Win10 both x86 and x64. And, of course, the support for Windows memory forensics is available on all platforms which Profiler runs on: Windows, OS X and Linux.

Opening and exploring a raw memory image in Profiler is extremely simple. The first step is to open the memory image from the UI.

Profiler automatically tries to identify the correct Windows version and the user is presented with an options dialog, which allows modifications to the default parameters.

If the user decides to modify the parameters, he can verify the correctness of the results by exploring processes and other parts.

Once the users is satisfied with the configuration, he may press “OK” and let Profiler analyse the image. Once the detection and analysis of embedded modules and files is finished, the user is presented with the workspace.

In the workspace the user can explore executables loaded in memory (Wow64 is supported).

He may explore the PEB of a process.

Or its VAD tree.

The System Service Descriptor Table (SSDT).

The Processor Control Block (KPRCB).

And of course explore kernel memory and drivers as well.

As usual, once the initial analysis is finished, everything can be saved into a project along with notes, bookmarks, layouts and so on. Loading a memory image from a project is immediate and saves a lot of time when analysing the same memory image multiple times.

This is just the beginning: we have many ideas and expect to release more frequently than in the past. I’m sure, we’ll be able to pleasantly surprise you!

Windows Memory Forensics

Let’s begin with an image:

Yep. That’s an icon. In an executable. In a process address space. In a raw memory dump.

And here is the video demonstration:

This is just a proof-of-concept. We still haven’t decided whether to develop this further. It really depends on whether the forensic community is interested in having such a product. So, even a re-tweet will have an impact on our decision. 🙂 We wanted to show what is currently possible and, of course, it’s not the end of the cool things which are possible.

In case we decide to go ahead with the development, we will probably create a beta-test group of potential customers and decide with them a roadmap for a 1.0 version, taking into consideration all those features which are essential to them. What we already support are the Windows versions that go from XP to 10 on the following architectures: x86, x86-PAE, x64. And, of course, the software itself, just like Profiler, runs on Windows, OS X and Linux.

And now to the more technical side if you’re interested. What we have shown in this demonstration is just a Python extension for Profiler. To be more specific, it’s only about 1000 lines of Python code and this includes all the UI views. The bulk of the work went into exposing all the necessary capabilities of our SDK to Python. Of course, all this work also benefits other extensions, not just the memory forensics ones. So, if this project ends with this post, it’s really not a tragedy, as we haven’t lost any significant time developing specific stuff for it.

So why did we choose to write our memory forensics support in Python, rather than in C++, which would’ve taken us a lot less time? The reasons are several. The memory forensic field is always changing rapidly and setting code in stone by compiling it wouldn’t be a good idea. Also, we wanted to give our customers the possibility to inspect the code and to modify it. Just by looking at existing code it’s extremely easy to write new utilities. While on the other hand, having our core engine and UI written in C++, makes our tool very fast. We think this is the perfect combination.

If you’re wondering why we didn’t use Volatility as a backbone, the answer is that it would’ve been incompatible on a licensing level and way too difficult to fit nicely into our existing framework to accomplish what we wanted to do.

We hope you enjoyed the demo and we would be happy to receive your feedback!

Torrent Support

Following our recent introduction to Scan Providers, here’s a first implementation example. In this post we’ll see how to add support for Torrent files in Profiler. Of course, the implementation shown in this post will be available in the upcoming 2.5.0 release.

Let’s start by creating an entry in the configuration file:

For the automatic signature recognition we may rely on a simple one:

Torrent files are encoded dictionaries and they usually start with the announce item. There’s no guarantee for that, but for now this simple matching should be good enough.

The encoded dictionary is in the Beconde format. Fortunately, someone already wrote the Python code to decode it:

We can now load the file and decode its dictionary:

We call the GetDictionary method first time in the _initObject method, so that the parsing occurs when we’re in another thread and we don’t stall the UI.

Let’s display the parsed dictionary to the user:

Dictionary

This is the description extracted from Wikipedia of some of the keys:

  • announce—the URL of the tracker
  • info—this maps to a dictionary whose keys are dependent on whether one or more files are being shared:
    • name—suggested filename where the file is to be saved (if one file)/suggested directory name where the files are to be saved (if multiple files)
    • piece length—number of bytes per piece. This is commonly 28 KiB = 256 KiB = 262,144 B.
    • pieces—a hash list, i.e., a concatenation of each piece’s SHA-1 hash. As SHA-1 returns a 160-bit hash, pieces will be a string whose length is a multiple of 160-bits.
    • length—size of the file in bytes (only when one file is being shared)
    • files—a list of dictionaries each corresponding to a file (only when multiple files are being shared). Each dictionary has the following keys:
      • path—a list of strings corresponding to subdirectory names, the last of which is the actual file name
      • length—size of the file in bytes.

While the dictionary already could suffice to extract all the information the user needs, we may want to present parts of the dictionary in an easier way to read.

First, we’d like to show to the user some meta-data information, which may be contained in the dictionary. To do that, we add a meta-data scan entry:

We also warn the user if the file exceeds the allowed maximum. We perform the whole scan logic in the UI thread, since we’re not doing any CPU intensive operation and thus we return SCAN_RESULT_FINISHED, which causes the _threadScan method not be called.

Here we return the meta-data to the UI:

MetaData

Also it would be convenient to see the list of trackers and files. Let’s start with the trackers:

Trackers

When retrieving data from the dictionary, we also make sure that it is in the correct type, so that the code which handles this data won’t end up generating an exception when trying to process an unexpected type.

And now the files:

Files

And that’s it. Now again the whole code for a better overview:

We could still extract more information from the torrent file. For instance, we could show the list of hashes and to which portion of which file they belong to. If that’s interesting for forensic purposes, we can easily add this view in the future.

Raw File System Analysis (FAT32 File Recovery)

This post isn’t about upcoming features, it’s about things you can already do with Profiler. What we’ll see is how to import structures used for file system analysis from C/C++ sources, use them to analyze raw hex data, create a script to do the layout work for us in the future and at the end we’ll see how to create a little utility to recover deleted files. The file system used for this demonstration is FAT32, which is simple enough to avoid making the post too long.

Note: Before starting you might want to update. The 1.0.1 version is out and contains few small fixes. Among them the ‘signed char’ type wasn’t recognized by the CFFStruct internal engine and the FAT32 structures I imported do use it. While ‘signed char’ may seem redundant, it does make sense, since C compilers can be instructed to treat char types as unsigned.

Import file system structures

Importing file system structures from C/C++ sources is easy thanks to the Header Manager tool. In fact, it took me less than 30 minutes to import the structures for the most common file systems from different code bases. Click here to download the archive with all the headers.

Here’s the list of headers I have created:

  • ext – ext2/3/4 imported from FreeBSD
  • ext2 – imported from Linux
  • ext3 – imported from Linux
  • ext4 – imported from Linux
  • fat – imported from FreeBSD
  • hfs – imported from Darwin
  • iso9660 – imported from FreeBSD
  • ntfs – imported from Linux
  • reiserfs – imported from Linux
  • squashfs – imported from Linux
  • udf – imported from FreeBSD

Copy the files to your user headers directory (e.g. “AppData\Roaming\CProfiler\headers”). It’s better to not put them in a sub-directory. Please note that apart from the FAT structures, none of the others have been tried out.

Note: Headers created from Linux sources contain many additional structures, this is due to the includes in the parsed source code. This is a bit ugly: in the future it would be a good idea to add an option to import only structures belonging to files in a certain path hierarchy and those referenced by them.

Since this post is about FAT, we’ll see how to import the structures for this particular file system. But the same steps apply for other file systems as well and not only for them. If you’ve never imported structures before, you might want to take a look at this previous post about dissecting an ELF and read the documentation about C++ types.

We open the Header Manager and configure some basic options like ‘OS’, ‘Language’ and ‘Standard’. In this particular case I imported the structures from FreeBSD, so I just set ‘freebsd’, ‘c’ and ‘c11’. Then we need to add the header paths, which in my case were the following:

Then in the import edit we insert the following code:

Now we can click on ‘Import’.

Import FAT structures

That’s it! We now have all the FAT structures we need in the ‘fat’ header file.

It should also be mentioned that I modified some fields of the direntry structure from the Header Manager, because they were declared as byte arrays, but should actually be shown as short and int values.

Parse the Master Boot Record

Before going on with the FAT analysis, we need to briefly talk about the MBR. FAT partitions are usually found in a larger container, like a partitioned device.

To perform my tests I created a virtual hard-disk in Windows 7 and formatted it with FAT32.

VHD MBR

As you might be able to spot, the VHD file begins with a MBR. In order to locate the partitions it is necessary to parse the MBR first. The format of the MBR is very simple and you can look it up on Wikipedia. In this case we’re only interested in the start and size of each partition.

Profiler doesn’t yet support the MBR format, although it might be added in the future. In any case, it’s easy to add the missing feature: I wrote a small hook which parses the MBR and adds the partitions as embedded objects.

Here’s the cfg data:

And here’s the Python script:

And now we can inspect the partitions directly (do not forget to enable the hook from the extensions).

VHD Partitions

Easy.

Analyze raw file system data

The basics of the FAT format are quite simple to describe. The data begins with the boot sector header and some additional fields for FAT32 over FAT16 and for FAT16 over FAT12. We’re only interested in FAT32, so to simplify the description I will only describe this particular variant. The boot sector header specifies essential information such as sector size, sectors in clusters, number of FATs, size of FAT etc. It also specifies the number of reserved sectors. These reserved sectors start with the boot sector and where they end the FAT begins.

The ‘FAT’ in this case is not just the name of the file system, but the File Allocation Table itself. The size of the FAT, as already mentioned, is specified in the boot sector header. Usually, for data-loss prevention, more than one FAT is present. Normally there are two FATs: the number is specified in the boot sector header. The backup FAT follows the first one and has the same size. The data after the FAT(s) and right until the end of the partition includes directory entries and file data. The cluster right after the FAT(s) usually starts with the Root Directory entry, but even this is specified in the boot sector header.

The FAT itself is just an array of 32-bit indexes pointing to clusters. The first 2 indexes are special: they specify the range of EOF values for indexes. It works like this: a directory entry for a file (directories and files share the same structure) specifies the first cluster of said file, if the file is bigger than one cluster, the FAT is looked up at the index representing the current cluster, this index specifies the next cluster belonging to the file. If the index contains one of the values in the EOF range, the file has no more clusters or perhaps contains a damaged cluster (0xFFFFFFF7). Indexes with a value of zero are marked as free. Cluster index are 2-based: cluster 2 is actually cluster 0 in the data region. This means that if the Root Directory is specified to be located at cluster 2, it is located right after the FATs.

Hence, the size of the FAT depends on the size of the partition, and it must be big enough to accommodate an array large enough to represent every cluster in the data area.

So, let’s perform our raw analysis by adding the boot sector header and the additional FAT32 fields:

Add struct

Note: When adding a structure make sure that it’s packed to 1, otherwise field alignment will be wrong.

Boot sector

Then we highlight the FATs.

FATs

And the Root Directory entry.

Root Directory

This last step was just for demonstration, as we’re currently not interested in the Root Directory. Anyway, now we have a basic layout of the FAT to inspect and this is useful.

Let’s now make our analysis applicable to future cases.

Automatically create an analysis layout

Manually analyzing a file is very useful and it’s the first step everyone of us has to do when studying an unfamiliar file format. However, chances are that we have to analyze files with the same format in the future.

That’s why we could write a small Python script to create the analysis layout for us. We’ve already seen how to do this in the post about dissecting an ELF.

Here’s the code:

We can create an action with this code or just run it on the fly with Ctrl+Alt+R.

Recover deleted files

Now that we know where the FAT is located and where the data region begins, we can try to recover deleted files. There’s more than one possible approach to this task (more on that later). What I chose to do is to scan the entire data region for file directory entries and to perform integrity checks on them, in order to establish that they really are what they seem to be.

Let’s take a look at the original direntry structure:

Every directory entry has to be aligned to 0x20. If the file has been deleted the first byte of the deName field will be set to SLOT_DELETED (0xE5). That’s the first thing to check. The directory name should also not contain certain values like 0x00. According to Wikipedia, the following values aren’t allowed:

  • ” * / : < > ? \ |
    Windows/MS-DOS has no shell escape character
  • + , . ; = [ ]
    They are allowed in long file names only.
  • Lower case letters a–z
    Stored as A–Z. Allowed in long file names.
  • Control characters 0–31
  • Value 127 (DEL)

We can use these rules to validate the short file name. Moreover, certain directory entries are used only to store long file names:

We can exclude these entries by making sure that the deAttributes/weAttributes isn’t ATTR_WIN95 (0xF).

Once we have confirmed the integrity of the file name and made sure it’s not a long file name entry, we can validate the deAttributes. It should definitely not contain the flags ATTR_DIRECTORY (0x10) and ATTR_VOLUME (8).

Finally we can make sure that deFileSize isn’t 0 and that deHighClust combined with deStartCluster contains a valid cluster index.

It’s easier to write the code than to talk about it. Here’s a small snippet which looks for deleted files and prints them to the output view:

This script is to be run on the fly with Ctrl+Alt+R. It’s not complete, otherwise I would have added a wait box, since like it’s now the script just blocks the UI for the entire execution. We’ll see later how to put everything together in a meaningful way.

The output of the script is the following:

We can see many false positives in the list. The results would be cleaner if we allowed only ascii characters in the name, but this wouldn’t be correct, because short names do allow values above 127. We could make this an extra option, generally speaking it’s probably better to have some false positives than missing valid entries. Among the false positives we can spot four real entries. What I did on the test disk was to copy many files from the System32 directory of Windows and then to delete four of them, exactly those four found by the script.

The next step is recovering the content of the deleted files. The theory here is that we retrieve the first cluster of the file from the directory entry and then use the FAT to retrieve more entries until the file size is satisfied. The cluster indexes in the FAT won’t contain the next cluster value and will be set to 0. We look for adjacent 0 indexes to find free clusters which may have belonged to the file. Another approach would be to dump the entire file size starting from the first cluster, but that approach is worse, because it doesn’t tolerate even a little bit of fragmentation in the FAT. Of course, heavy fragmentation drastically reduces the chances of a successful recovery.

However, there’s a gotcha which I wasn’t aware of and it wasn’t mentioned in my references. Let’s take a look at the deleted directory entry of ‘notepad.exe’.

Notepad directory entry

In FAT32 the index of the first cluster is obtained by combining the high-word deHighClust with the low-word deStartCluster in order to obtain a 32-bit index.

The problem is that the high-word has been zeroed. The actual value should be 0x0013. Seems this behavior is common on Microsoft operating systems as mentioned in this thread on Forensic Focus.

This means that only files with a cluster index equal or lower than 0xFFFF will be correctly pointed at. This makes another approach for FAT32 file recovery more appealing: instead of looking for deleted directly entries, one could directly look for cluster indexes with a value of 0 in the FAT and recognize the start of a file by matching signatures. Profiler offers an API to identify file signatures (although limited to the file formats it supports), so we could easily implement this logic. Another advantage of this approach is that it doesn’t require a deleted file directory entry to work, increasing the possibility to recover deleted files. However, even that approach has certain disadvantages:

  1. Files which have no signature (like text files) or are not identified won’t be recovered.
  2. The name of the files won’t be recovered at all, unless they contain it themselves, but that’s unlikely.

Disadvantages notwithstanding I think that if one had to choose between the two approaches the second one holds higher chances of success. So why then did I opt to do otherwise? Because I thought it would be nice to recover file names, even though only partially and delve a bit more in the format of FAT32. The blunt approach could be generalized more and requires less FAT knowledge.

However, the surely best approach is to combine both systems in order to maximize chances of recovery at the cost of duplicates. But this is just a demonstration, so let’s keep it relatively simple and let’s go back to the problem at hand: the incomplete start cluster index.

Recovering files only from lower parts of the disk isn’t really good enough. We could try to recover the high-word of the index from adjacent directory entries of existing files. For instance, let’s take a look at the deleted directory entry:

Deleted entry

As you can see, the directory entry above the deleted one represents a valid file entry and contains an intact high-word we could use to repair our index. Please remember that this technique is just something I came up with and offers no guarantee whatsoever. In fact, it only works under certain conditions:

  1. The cluster containing the deleted entry must also contain a valid file directory entry.
  2. The FAT can’t be heavily fragmented, otherwise the retrieved high-word might not be correct.

Still I think it’s interesting and while it might not always be successful in automatic mode, it can be helpful when trying a manual recovery.

This is how the code to recover partial cluster indexes might look like:

It tries to find a valid file directory entry before and after the deleted entry, remaining in the same cluster. Now we can write a small function to recover the file content.

All the pieces are there, it’s time to bring them together.

Create a recovery tool

With the recently introduced logic provider extensions, it’s possible to create every kind of easy-to-use custom utility. Until now we have seen useful pieces of code, but using them as provided is neither user-friendly nor practical. Wrapping them up in a nice graphical utility is much better.

Home view

What follows is the source code or at least part of it: I have omitted those parts which haven’t significantly changed. You can download the full source code from here.

Here’s the cfg entry:

And the Python code:

When the tool is activated it will ask for the disk file to be selected, then it will show an options dialog.

Options

In our case we can select the option ‘Ascii only names’ to exclude false positives.

The options dialog asks for a directory to save the recovered files. In the future it will be possible to save volatile files in the temporary directory created for the report, but since it’s not yet possible, it’s the responsibility of the user to delete the recovered files if he wants to.

The end results of the recovery operation:

Results

All four deleted files have been successfully recovered.

Three executables are marked as risky because intrinsic risk is enabled and only ‘ntoskrnl.exe’ contains a valid digital certificate.

Conclusions

I’d like to remind you that this utility hasn’t been tested on disks other than on the one I’ve created for the post and, as already mentioned, it doesn’t even implement the best method to recover files from a FAT32, which is to use a signature based approach. It’s possible that in the future we’ll improve the script and include it in an update.

The purpose of this post was to show some of the many things which can be done with Profiler. I used only Profiler for the entire job: from analysis to code development (I even wrote the entire Python code with it). And finally to demonstrate how a utility with commercial value like the one presented could be written in under 300 lines of Python code (counting comments and new-lines).

The advantages of using the Profiler SDK are many. Among them:

  • It hugely simplifies the analysis of files. In fact, I used only two external Python functions: one to check the existence of a directory and one to normalize the path string.
  • It helps building a fast robust product.
  • It offers a graphical analysis experience to the user with none or little effort.
  • It gives the user the benefit of all the other features and extension offered by Profiler.

To better explain what is meant by the last point, let’s take the current example. Thanks to the huge amount of formats supported by Profiler, it will be easy for the user to validate the recovered files.

Validate recovered files

In the case of Portable Executables it’s extremely easy because of the presence of digital certificates, checksums and data structures. But even with other files it’s easy, because Profiler may detect errors in the format or unused ranges.

I hope you enjoyed this post!

P.S. You can download the complete source code and related files from here.

References

  1. File System Forensic Analysis – Brian Carrier
  2. Understanding FAT32 Filesystems – Paul Stoffregen
  3. Official documentation – Microsoft
  4. File Allocation Table – Wikipedia
  5. Master boot record – Wikipedia

SQLite3 support and inspection of free pages

The upcoming 1.0.0 version of the Profiler introduces support for SQLite3 databases.

SQLite table

You’ll see that even viewing large tables is pleasantly fast. The SQL table control is available to the Python SDK as well: it can either be created via createView or inside a custom view with the tag sqltable.

Once the sql table view is created, it offers the following methods:

So it’s possible to display a particular table in it or offer the possibility to the user to choose the table via setSQLTableSelectVisible.

The database can be accessed as well. The Profiler exposes its internal SQLite code in the homonymous module. It differs from the standard Python implementation and it matches the C API. For instance, to enumerate the table names in a database we can use this code:

The handle returned by GetHandle grants only read access. In fact, to maximize speed and avoiding copy operations, the Profiler replaces the virtual file-system of the SQLite database in order for it to read directly from the CFFObject.

The exposed C API can be used to open external databases as well and will be used to access the main report database file in order to give plugins the capability to store and retrieve their own data.

Free pages inspection

When the database file contains free pages, it will be reported in the summary. Free pages usually contain deleted data and can therefore be of interest for forensic purposes.

Free pages

The image above shows a test database I’ve created. In it I created a few tables, and inserted some records containing repeated values (but keeping each record different). Then I deleted a specific record containing ‘1’s. The result is that the database now contains free pages and when inspecting them with the Profiler we can see a big part of the original data.

Keep in mind that data contained in free pages can be incomplete and is scattered. The free pages data can be retrieved programmatically as well through the method GetFreePages.

Stay tuned as there’s much more coming soon!