All posts by David

Amazon Lex Introduces an Enhanced Console Experience and New V2 APIs

This post was originally published on this site

Today, the Amazon Lex team has released a new console experience that makes it easier to build, deploy, and manage conversational experiences. Along with the new console, we have also introduced new V2 APIs, including continuous streaming capability. These improvements allow you to reach new audiences, have more natural conversations, and develop and iterate faster.

The new Lex console and V2 APIs make it easier to build and manage bots focusing on three main benefits. First, you can add a new language to a bot at any time and manage all the languages through the lifecycle of design, test, and deployment as a single resource. The new console experience allows you to quickly move between different languages to compare and refine your conversations. I’ll demonstrate later how easy it was to add French to my English bot.

Second, V2 APIs simplify versioning. The new Lex console and V2 APIs provide a simple information architecture where the bot intents and slot types are scoped to a specific language. Versioning is performed at the bot level so that resources such as intents and slot types do not have to be versioned individually. All resources within the bot (language, intents, and slot types) are archived as part of the bot version creation. This new way of working makes it easier to manage bots.

Lastly, you have additional builder productivity tools and capabilities to give you more flexibility and control of your bot design process. You can now save partially completed work as you develop different bot elements as you script, test and tune your configuration. This provides you with more flexibility as you iterate through the bot development. For example, you can save a slot that refers to a deleted slot type. In addition to saving partially completed work, you can quickly navigate across the configuration without getting lost. The new Conversation flow capability allows you to maintain your orientation as you move across the different intents and slot types.

In addition to the enhanced console and APIs, we are providing a new streaming conversation API. Natural conversations are punctuated with pauses and interruptions. For example, a customer may ask to pause the conversation or hold the line while looking up the necessary information before answering a question to retrieve credit card details when providing bill payments. With streaming conversation APIs, you can pause a conversation and handle interruptions directly as you configure the bot. Overall, the design and implementation of the conversation is simplified and easy to manage. The bot builder can quickly enhance the conversational capability of virtual contact center agents or smart assistants.

Let’s create a new bot and explore how some of Lex’s new console and streaming API features provide an improved bot building experience.

Building a bot
I head over to the new V2 Lex console and click on Create bot to start things off.

I select that I want to Start with an example and select the MakeAppointment example.

Over the years, I have spoken at many conferences, so I now offer to review talks that other community members are producing. Since these speakers are often in different time zones, it can be complicated to organize the various appointments for the different types of reviews that I offer. So I have decided to build a bot to streamline the process. I give my bot the name TalkReview and provide a description. I also select Create a role with basic Amazon Lex permissions and use this as my runtime role.

I must add at least one language to my bot, so I start with English (GB). I also select the text-to-speech voice that I want to use should my bot require voice interaction rather than just text.

During the creation, there is a new button that allows me to Add another language. I click on this to add French (FR) to my bot. You can add languages during creation as I am doing here, or you can add additional languages later on as your bot becomes more popular and needs to work with new audiences.

I can now start defining intents for my bot, and I can begin the iterative process of building and testing my bot. I won’t go into all of the details of how to create a bot or show you all of the intents I added, as we have better tutorials that can show you that step-by-step, but I will point out a few new features that make this new enhanced console really compelling.

The new Conversation flow provides you with a visual flow of the conversation, and you can see how the sample utterances you provide and how your conversation might work in the real world. I love this feature because you can click on the various elements, and it will take you to where you can make changes. For example, I can click on the prompt What type of review would you like to schedule and I am taken to the place where I can edit this prompt.

The new console has a very well thought-out approach to versioning a bot. At anytime, on the Bot versions screen, I can click Create version, and it will take a snapshot of the state of the bot’s current configuration. I can then associate that with an alias. For example, in my application, I have an alias called Production. This Production alias is associated with Version 1. Still, at any time, I could switch it to use a different version or even roll back to a previous version if I discover problems.

The testing experience is now very streamlined. Once I have built the bot, I can click the test button on the bottom right hand of the screen and start speaking to the bot and testing the experience. You can also expand the Inspect window, which gives you details about the conversations state, and you can also explore the raw JSON inputs and outputs.

Things to know
Here are a couple of important things to keep in mind when you use the enhanced console

  • Integration with Amazon Connect – Currently, bots built in the new console cannot be integrated with Amazon Connect contact flows. We plan to provide this integration as part of the near-term roadmap. You can use the current console and existing APIs to create and integrate bots with Amazon Connect.
  • Pricing – You only pay for what you use. The charges remain the same for existing audio and text APIs, renamed as RecognizeUtterance and RecognizeText. For the new Streaming capabilities, please refer to the pricing detail here.
  • All existing APIs and bots will continue to be supported. The newly announced features are only available in the new console and V2 APIs.

Go Build
Lex enhanced console is available now, and you can start using it today. The enhanced experience and V2 APIs are available in all existing regions and support all current languages. So, please give this console a try and let us know what you think. To learn more, check out the documentation for the console and the streaming API.

Happy Building!
— Martin

Qakbot activity resumes after holiday break, (Wed, Jan 20th)

This post was originally published on this site

Introduction

Although the botnet infrastructure behind Qakbot was active as we entered this year, we hadn't seen any active campaigns spreading Qakbot.  Qakbot had been quiet since a few days before Christmas.  We saw no new malicious spam (malspam), and we saw no new Excel spreadsheets that we typically find during active campaigns.

It had been relatively quiet for Qakbot until Tuesday 2021-01-19, when we started seeing malicious spam (malspam) pushing Qakbot again.  @BushidoToken tweeted about it here.

Today's diary examines a Qakbot infection from Tuesday 2021-01-19.

Shown above:  Flow chart for recent Qakbot activity.

The malspam

No changes here.  Qakbot malspam typically spoofs stolen email chains from previously-infected Windows hosts, and it feeds the data to currently-infected Windows hosts that send new malspam pushing updated files for Qakbot.  See the image below for an example from Tuesday 2021-01-19.


Shown above:  An example of Qakbot malspam from Tuesday 2021-01-19.


Shown above:  Screenshot from one of the spreadsheets I used to infected a Windows host with Qakbot.

Infection traffic

See the images below for my analysis of network traffic from the Qakbot infection.


Shown above:  Traffic from the Qakbot infection filtered in Wireshark.


Shown above:  Excel macro retrieving the initial DLL file for Qakbot.


Shown above:  More post-infection activity from the Qakbot-infected Windows host.


Shown above:  Traffic over TCP port 65400 caused by Trickbot.


Shown above:  Certificate issuer data for HTTPS traffic caused by Qakbot (example 1 of 3).

Shown above:  Certificate issuer data for HTTPS traffic caused by Qakbot (example 2 of 3).


Shown above:  Certificate issuer data for HTTPS traffic caused by Qakbot (example 3 of 3).

Forensics on infected Windows host

See the images below for my forensic investigation on the infected Windows host.


Shown above:  Initial Qakbot DLL saved to the infected Windows host.


Shown above:  Other artifacts from the infected Windows host.


Shown above:  Windows registry updates caused by Qakbot on the infected host.

Indicators of Compromise (IOCs)

SHA256 hash: 8ebba35fa60f107aa4e19fa39ae831feab4ffb1718bdca016670d3898b2fe4fc

  • File size: 25,543 bytes
  • File name: Complaint_Copy_1206700885_01192021.xlsm
  • File description: Spreadsheet with macro for Qakbot

SHA256 hash: f9560829534803161c87666795f0feab028ff484fac5170b515390b50e8050fd

  • File size: 1,545,688 bytes
  • File location: hxxp://senzo-conseil-expat[.]fr/bqkckb/5555555555.jpg
  • File location: C:Users[username]AppDataRoamingKKEEDTT.DEEREDTTDVD
  • File description: Initial DLL for Qakbot
  • Run method: rundll32.exe [filename],DllRegisterServer

HTTP request caused by Excel macro to retrieve DLL for Qakbot:

  • 51.210.14[.]58 port 80 – senzo-conseil-expat[.]fr – GET /bqkckb/5555555555.jpg

HTTPS traffic from the infected host:

  • 95.76.27[.]6 port 443
  • 185.14.30[.]127 port 443
  • 172.115.177[.]204 port 2222

Web traffic connectivity checks from the infected host (HTTPS traffic):

  • port 443 – www.openssl.org
  • port 443 – api.ipify.org

TCP traffic from the infected host:

  • 54.36.108[.]120 port 65400

Connectivity checks to mail servers from the infected host:

  • 172.217.195.109 port 993 – imap.gmail.com
  • 108.177.104.28 port 25 – smtp-relay.gmail.com
  • 108.177.104.28 port 465 – smtp-relay.gmail.com
  • 108.177.104.28 port 587 – smtp-relay.gmail.com
  • 64.29.151.102 port 110 – mail.myfairpoint.net
  • 64.29.151.102 port 143 – mail.myfairpoint.net
  • 74.6.106.29 port 995 – inbound.att.net

Certificate issuer data for HTTPS traffic to 95.76.27[.]6 over TCP port 443:

  • id-at-countryName=NL
  • id-at-stateOrProvinceName=ED
  • id-at-localityName=Dadoe
  • id-at-organizationName=Letx Uqe Dzcmtewzs Kctonlfg Inc.
  • id-at-commonName=epeivate.biz

Certificate issuer data for HTTPS traffic to 185.14.30[.]127 over TCP port 443:

  • id-at-countryName=US
  • id-at-stateOrProvinceName=NY
  • id-at-localityName=New York
  • id-at-organizationName=cloudservers03.com
  • id-at-commonName=cloudservers03.com

Certificate issuer data for HTTPS traffic to 172.115.117[.]204 over TCP port 2222:

  • id-at-countryName=DE
  • id-at-stateOrProvinceName=IQ
  • id-at-localityName=Aeur
  • id-at-organizationName=Cepasduq Nqo Ooifzetkp Mqen
  • id-at-commonName=ltxkvijevns.com

Final words

A pcap of the infection traffic along with malware (Excel file and DLL) from an infected host can be found here.


Brad Duncan
brad [at] malware-traffic-analysis.net

(c) SANS Internet Storm Center. https://isc.sans.edu Creative Commons Attribution-Noncommercial 3.0 United States License.

Gordon for fast cyber reputation checks, (Tue, Jan 19th)

This post was originally published on this site

Gordon quickly provides threat & risk information about observables

Gordon is a great website for security analysis and threat intelligence practitioners courtesy of Marc-Henry Geay of France.
It’s a fine offering that quickly provides threat and risk information about observables such as IPv4 addresses, URLs, Domains/FQDNs, MD5, SHA-1, SHA-256 hashes, or email addresses.

All aspirations and architecture for Gordon are available in Marc-Henry’s Medium post, as well as his About content.
You really need only know the following in any detail:

  • Gordon submits your observables (IOCs) to multiple sources (30+ engines) to ensure good coverage.
  • Observables are only searched in open security databases’ existing records (passive).
  • Results can be viewed and shared for up to 3 days, thereafter they are deleted, Marc-Henry has EU privacy regulations to contend with.
  • Results are available as Summary Reports with risk-based coloration for some engines, and can be exported as PDF, CSV, and XLSX.

I gave Gordon a quick test using IPv4 IOCs from the Cisco Talos Threat Advisory: SolarWinds supply chain attack. Gordon limits you to 15 observables at most, and note that it favors non-Microsoft browsers, so I experimented via Firefox. Using ten IP IOCs, separated one per line, I received swift results as seen in Figure 1.

Gordon

Figure 1: Gordon IPv4 SUNBURST results

As noted, Figure 1: shows IPvs SUNBURST IOC results that are precise and color coded by risk.
Using ten SHA-256 hashes from the Talos report for my next query I opted to export the results as an Excel document, then sorted by malicious results only.

Gordon

Figure 2: Gordon SHA-256 query results

Again, the SUNBURST SHA-256 IOC results are robust and detailed. I’ve certainly added Gordon to my favorites list and suggest you consider doing the same.

Cheers…until next time.

(c) SANS Internet Storm Center. https://isc.sans.edu Creative Commons Attribution-Noncommercial 3.0 United States License.

Doc & RTF Malicious Document, (Mon, Jan 18th)

This post was originally published on this site

A reader pointed us to a malicious Word document.

First, I run my strings.py command on it, with option -a to get statistics (see my diary entry "Strings 2021").

There aren't any long strings in this file (the longest is 33 characters). So there isn't a payload here that we can extract directly, like we did in diary entry "Maldoc Strings Analysis".

Let's check if there are URLs in this file, by grepping for http:

Unfortunately, none.

Let's take a look at the longest strings (-n 20: strings at least 20 characters long):

If you are a bit familiar with the internals of Word documents, you might recognize this as the name of XML files found inside OOXML files (.docx, .docm, .xlsx, …).

Let's try oledump.py:

This means that there are no OLE files inside this OOXML file, hence no VBA macros.

Since an OOXML is an OPC file, e.g. a ZIP container, let's take a look with my tool zipdump.py:

It looks like this OOXML file only contains XML files (extensions .xml and .rels). Let's verify by getting statistics of the content of the contained files, by using option -e:

Here is a close look on the statisctics:

All contained files starts with <?xm and have only printable ASCII characters (except one file with 90 bytes >= 127).

So we have no binary files in here, just text files. One possible scenario, is that this .docx file contains a reference (URL) to a malicious payload.

Next step, is to extract all files and search for URLs in them. Now, in Office OOXML files, you will find a lot of legitimate URLs. To get an idea of what type of URLs we have in this document, we use my re-search.py tool to extract URLs, and display a unique list of hostnames found in these URLS, like this:

The following hostnames are legitimate, and found in Office OOXML files:

schemas.openxmlformats.org
schemas.microsoft.com
purl.org
www.w3.org

But the IP address is not. So let's extract the full URLs now, and grep for 104:

I downloaded this document. Let's start again with strings:

4555 characters long: this might be a payload. Let's take a look:

This looks like a lot of hexadecimal data. That's interesting. And notice the 3 curly braces at the end. Hexadecimal data and curly braces: this might be a malicious RTF document. Let's check with the file command (I use my tool file-magic.py on Windows):

This is indeed an RTF file. RTF files can not contain VBA code. If they are malicious, they often contain an exploit, stored as (obfuscated) hexadecimal characters inside the RTF file. Hence the strings command will not be of much use.

I recently updated my tool rtfdump.py to make analysis of embedded objects (like malicious payloads) easier. We use the new option -O to get an overview of all objects found inside this RTF file:

There's one object with name equation… . It's very likely that this is an exploit for the equation editor, and that we have to extract and analyze shellcode.

Let's extract this payload and write it to a file:

Let's see if there are some intesting strings:

Nothing interesting.

The equation editor that is targeted here, only exists as a 32-bit executable. Hence the shellcode must also be 32-bit, and we can use the shellcode emulator scdbg to help us.

We use option -f findsc to let scdbg search for entrypoints, option -r to produce a report, and -f shellcode to pass the shellcode file for analysis:

The shellcode emulator found 4 entry points (numbered 0 to 3). I select entry point 0. This results in the emulation of shellcode, that calls the Win32 API (GetProcAddress, …). This is clearly 32-bit Windows shellcode. And it decodes itself into memory. We can use option -d to dump the decoded shellcode:

This creates a file: shellcode.unpack. Let's use strings again on this file:

This looks more promising. What are the longest strings:

And finally, we have our URL.

 

 

 

Didier Stevens
Senior handler
Microsoft MVP
blog.DidierStevens.com DidierStevensLabs.com

(c) SANS Internet Storm Center. https://isc.sans.edu Creative Commons Attribution-Noncommercial 3.0 United States License.

Obfuscated DNS Queries, (Fri, Jan 15th)

This post was originally published on this site

This week I started seeing some URL with /dns-query?dns in my honeypot[1][2]. The queries obviously did not look like a standard DNS queries, this got me curious and then proceeded to investigate to determine what these DNS query were trying to resolve.

But before proceeding, I have logs going back to May 2018 and reviewed the logs to see when this activity was first captured. The first time the honeypot logged something similar was in February 2020 with one long query that was different to all other queries. All the logs are targeting TCP/443 and are unencrypted.

Using base64 URL safe option in CyberChef, I was able to decode the DNS information for the 3 different queries. The first query captured in February 2020 appears to be a test (see decoded information below). The other two resolve to a URL: one as a test (www.example[.]com) and the other to Baidu search engine (www.baidu[.]com).

Sample Logs

  • tcp-honeypot-20200212-195552.log:20200226-230039: 192.168.25.9:443-54.153.67.242:59822 data 'GET /dns-query?dns=AAABAAABAAAAAAAAAWE-NjJjaGFyYWN0ZXJsYWJlbC1tYWtlcy1iYXNlNjR1cmwtZGlzdGluY3QtZnJvbS1zdGFuZGFyZC1iYXNlNjQHZXhhbXBsZQNjb20AAAEAAQ HTTP/1.1rnHost: XX.30.102.198:443rnConnection: closernAccept-Encoding: gziprnUser-Agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.102 Safari/537.36rnrn'
  • tcp-honeypot-20200413-081332.log:20200413-171212: 192.168.25.9:443-195.37.190.77:40634 data 'GET /dns-query?dns=AAABAAABAAAAAAAAA3d3dwdleGFtcGxlA2NvbQAAAQAB HTTP/1.1rnHost: XX.30.102.198rnUser-Agent: Go-http-client/1.1rnAccept-Encoding: gziprnConnection: closernrn'

[…]

  • 20210112-110540: 192.168.25.9:443-39.96.138.251:60736 data 'GET /dns-query?dns=AAABAAABAAAAAAAAA3d3dwViYWlkdQNjb20AAAEAAQ HTTP/1.1rnHost: XX.49.33.78rnUser-Agent: Go-http-client/1.1rnAccept: application/dns-messagernAccept-Encoding: gziprnConnection: closernrn'
  • 20210113-040125: 192.168.25.9:443-161.117.239.46:49778 data 'GET /dns-query?dns=AAABAAABAAAAAAAAA3d3dwViYWlkdQNjb20AAAEAAQ HTTP/1.1rnHost: XX.49.33.78rnUser-Agent: Go-http-client/1.1rnAccept: application/dns-messagernAccept-Encoding: gziprnConnection: closernrn'

Base64 Decoded Queries

  • AAABAAABAAAAAAAAAWE-NjJjaGFyYWN0ZXJsYWJlbC1tYWtlcy1iYXNlNjR1cmwtZGlzdGluY3QtZnJvbS1zdGFuZGFyZC1iYXNlNjQHZXhhbXBsZQNjb20AAAEAAQ ………….a>62characterlabel-makes-base64url-distinct-from-standard-base64.example.com…..
  • AAABAAABAAAAAAAAA3d3dwViYWlkdQNjb20AAAEAAQ   ………….www.baidu.com…..
  • AAABAAABAAAAAAAAA3d3dwdleGFtcGxlA2NvbQAAAQAB ………….www.example.com…..

DNS Queries by Base64 String

  • IP Activity resolving to www.example[.]com has been active since April 2020 with 2 packets per month.
  • User-Agent → Mozilla/5.0 (compatible; DNSResearchBot/2.1; +http://195.37.190.77)

195.37.190[.]77

====================

  • IP Activity resolving to www.baidu[.]com only started in December 2020 and has been active since then.
  • User-Agent → Go-http-client/1.1

39.96.138[.]251
39.96.139[.]173
39.96.139[.]223
39.96.140[.]32
47.74.84[.]52
47.241.66[.]187
54.153.67[.]242

====================

  • IP Activity resolving to 62characterlabel-makes-base64url-distinct-from-standard-base64.example.com only seen once in February 2020 which appears to be only a test.
  • Something interesting, 62characterlabel-makes-base64url-distinct-from-standard-base64 is equal to 62 characters
  • User-Agent → Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.102 Safari/537.36

161.117.239[.]46

====================

Do you have similar obfuscated DNS queries in your logs? Please use our comment form to share them.

[1] https://github.com/DidierStevens/Beta/blob/master/tcp-honeypot.py
[2] https://www.inetsim.org/documentation.html
[3] https://gchq.github.io/CyberChef/

———–
Guy Bruneau IPSS Inc.
My Handler Page
Twitter: GuyBruneau
gbruneau at isc dot sans dot edu

(c) SANS Internet Storm Center. https://isc.sans.edu Creative Commons Attribution-Noncommercial 3.0 United States License.

Throwback Friday: An Example of Rig Exploit Kit, (Fri, Jan 15th)

This post was originally published on this site

Introduction

As this week winds down, I wanted to highlight a threat that's significantly diminished in recent years.  For today's #ThrowbackFriday, I'm reviewing an example of Rig exploit kit (EK) generated yesterday on Thursday 2021-01-14.

History of Rig EK

EKs are a malware distribution method.  They're channels to send malware to vulnerable Windows hosts.  An EK's payload is Windows-based malware.

Rig EK was discovered in 2014, back when EKs were much more common than today.  Like other EKs in 2014, Rig exploited Internet Explorer (IE) and browser-based applications that worked with IE like Java, Flash, and Silverlight.  Since then, people have increasingly moved to other browsers like FireFox and Chrome.  Because of this, EK activity began to decline.

Windows 10 was introduced in 2015 with Microsoft Edge as its default browser.  As more people switched to Windows 10, some EKs disappeared.  Rig EK continued to decline, with a substantial drop in 2017.  By 2018, Rig EK was one of only a few remaining EKs.  Today, people still discover examples of Rig EK, but it's only effective against out-of-date hosts running Windows 7 and using IE.

To prepare for throwback Friday, I fired up a vulnerable Windows 7 host, opened IE 11, and entered a URL that led to Rig EK.

Gate to Rig EK

An HTTPS gate that leads to Rig EK has been active since December 2020:

  • hxxps://anklexit[.]online/twDGMjtfsacfa3e

On 2020-12-24, @nao_sec tweeted an example of Rig EK pushing SmokeLoader that contained the above URL.  Earlier this month, Rig EK from the same gate pushed Dridex.

URLs like this act as a gate to an EK.  This gate wouldn't direct me to Rig EK when I tried it through a VPN.  However, tethering through my phone worked.  These gates are somewhat picky.  Use the gate once, and it might work.  But try it again from the same IP address, and it prevents you from reaching the EK again.  You generally have to wait 12 to 24 hours before the gate will work again, if you're coming from the same IP address.

Traffic from an infection

See the below images for traffic from the infection.


Shown above:  Traffic from the infection filtered in Wireshark.


Shown above:  Rig EK landing page shown in an HTTP stream.


Shown above:  Dridex installer EXE sent by Rig EK as an encrypted binary.


Shown above:  Certificate issuer data for HTTPS traffic generated by Dridex installer.

To get a better understanding of Dridex infection traffic, see this Wireshark tutorial I wrote about it last year.

Forensics on an infected Windows host

While the Rig EK payload (an EXE to install Dridex) generated HTTPS command and control (C2) traffic, it wasn't able to install Dridex on the victim host.  So I only saw the Dridex installer EXE.  I also captured a small file (approx 1 kB) of JavaScript text used by Rig EK before it was deleted during the infection process.


Shown above:  Artifacts from the infection caused by Rig EK.

Indicators of Compromise (IOCs)

The following are indicators from this infection.

Traffic from an infected Windows host:

  • 188.225.75[.]54 port 443 – anklexit[.]online – HTTPS URL – gate to Rig EK
  • 188.227.106[.]164 port 80 – 188.227.106[.]164 – Rig EK
  • 162.241.44[.]26 port 9443 – HTTPS traffic caused by Dridex installer
  • 185.184.25[.]234 port 4664 – attempted TCP connection caused by Dridex installer
  • 138.201.138[.]91 port 3389 – attempted TCP connection caused by Dridex installer

Certificate issuer data from Dridex HTTPS traffic to 162.241.44[.]26 over TCP port 9443:

  • id-at-countryName=DS
  • id-at-stateOrProvinceName=Tholend finck4
  • id-at-localityName=Khartoum
  • id-at-organizationName=Antymasu PEEC
  • id-at-commonName=anompatof.rip

Malware/artifacts from the infected Windows 7 host:

SHA256 hash: 209093c71d0e87df00a290c588a5147e1e14023402f317d7903c6402d52a87ee

  • File size: 98,819 bytes
  • File location: hxxp://188.227.106[.]164/?MzA1NTIz&pwDDc&AcAZl=pinny.866&shghfg[long string]
  • File description: HTML for Rig EK landing page

SHA256 hash: f14c7ba0240be3456164dd63f53dd4bc7eb34bcdb1ac26e98a623edc0390b56b

  • File size: 1,152 bytes
  • File location: C:Users[username]AppDataLocalTemp3.tMp
  • File description: JavaScript text file dropped by Rig EK

SHA256 hash: 0376f97c21d2f00bc9c0919ce108ef14a2b3b1b356b2caa502a6cae81c7798f2

  • File size: 1,198,592 bytes
  • File location: C:Users[username]AppDataLocalTempjv9qx.exe
  • File description: Rig EK payload, Windows EXE to install Dridex malware

Final Words

Pcap and malware/artifacts for this diary can be found here.

I wonder how it long this method of malware distribution will remain profitable.  Apparently, enough people currently use out-of-date vulnerable Windows hosts.  I guess this presents a big enough target base for the people behind Rig EK.

Every time I find Rig EK, I think back to all the entries I posted on my blog from 2013 through 2016 featuring Rig and other EK infections.  That's why I consider today's diary a #ThrowbackFriday.


Brad Duncan
brad [at] malware-traffic-analysis.net

(c) SANS Internet Storm Center. https://isc.sans.edu Creative Commons Attribution-Noncommercial 3.0 United States License.

Dynamically analyzing a heavily obfuscated Excel 4 macro malicious file, (Thu, Jan 14th)

This post was originally published on this site

Recently I had to analyze an Excel malicious file that was caught in the wild, in a real attack. The file was used in a spear phishing attack where a victim was enticed into opening the file with Excel and, of course, enabling macros.

The image below shows what the file looks like when opened in Excel:

Malicious document

As you can see, it is a very common malicious Excel file where the victim is supposed to click on the Enable content button to see the document. Of course, once the victim does that it is game over and the machine gets infected. My goal was to analyze the malicious Excel file to identify what exactly it is doing.

Typically, the first step to analyze such a document would be verify macros with Didier’s oledump.py tool, which is the de-facto standard malicious document analysis tool (the FOR610 instructors are joking that Day 3 of FOR610 is Didier’s day, since he wrote so many useful tools).

However, as you can see below, I was a bit disappointed to see that there are no macros – this normally means that the attacker is using Excel 4 macros – an old way of creating active content:

$ python3 oledump.py ~/source.xls 
  1:      4096 'x05DocumentSummaryInformation'
  2:      4096 'x05SummaryInformation'
  3:    162264 'Workbook'

Both Didier and Xavier wrote a number of diaries about analyzing Excel 4 macros (available here and here), so the next step was to use the BIFF plugin Didier wrote, which allows output of BIFF records – these hold Excel 4 macros (formulas really), so let’s do that:

BIFF plugin outputOk, we’re getting somewhere, however there were thousands of lines like this in the output:

ErrorsNot nice. This means that Didier’s BIFF plugin does not know how to parse these bytes, which define a currently unknown formula. Didier also wrote about another tool that can be used to deobfuscate malicious Excel files, XLMMacroDeobfuscator (read the diary here) so I thought about trying that as well:

XLMDeobfuscatorHmm, a bit better but we still do not know what exactly this file is doing. There are two important things we can see in XLMMacroDeobfuscator’s output though:

  1. First, we can see that there is a cell with the name of Auto_Open. The contents of this cell will be executed (if it is a formula) automatically when this Excel file is open, after the user has clicked on the Enable content button, of course.
  2. Second, we can see that the last two functions that are called are WORKBOOK.UNHIDE and WORKBOOK.HIDE. These do exactly as their names say – they will hide one workbook and unhide another – this will result in the final, decoy content to be shown to the victim (no screenshot, sorry, as it contains sensitive information about the target).

Armed with this knowledge I wanted to dig further into the file. While most researchers might prefer static analysis since it is safer, in this case such analysis might be very difficult or time consuming. The main reason is that the tools we have on our disposal failed to completely parse the document (as shown above) and, besides this, the file is heavily obfuscated with a number of formulas and calculations that are performed automatically by Excel.

So, I decided to go with dynamic analysis of the file – cool thing is that, at least for this case, we do not need any other tools but Excel. Of course, by running malicious code we will be putting ourselves to risk, so if you ever need to perform a similar analysis, make sure that you do it in a VM, without Internet access (or you will be really living on the edge).

Since this malicious document does not have a VBA macro, it relies on executing formulas. Excel will generally execute formulas top to bottom in a column, then move to the next column and so on. This does not necessarily has to be in this order, but in all cases I have checked it was. Our first step will be to find the cell (formula) that gets executed first – XLMMacroDeobfuscator already told us that it is a cell in the “Sheet_vrg” tab of this document, and that the cell is $HV$19420 (column HV, row 19420). We can also see this by opening the malicious file in Excel (notice I am not enabling content yet!) and then going to Formulas -> Name Manager. We will see this very same cell displayed, as shown in the figure below:

Excel Name Manager

Let’s scroll now to this cell to see what its contents look like:

Cell

Aha – this is actually what XLMMacroDeobfuscator showed to us, but it partially evaluated the contents so we still do not know what this code actually does. So let’s see how we can dynamically analyze this document. Excel actually allows us to manually evaluate any formula shown in the document. All we have to do is right click on a cell, but the catch-22 here is that we have to click on Enable content in order to do that, and by doing this we will execute the malicious macro.

The solution is, luckily, relatively simple. A function called HALT() exists that does exactly what the name says, so we can manually insert this function in a free cell and then change the Auto_Open name to point to our cell. What’s even better – in the image above you can see that there is already a cell with the =HALT() function (it’s the last one), so let’s just change Auto_Open to that cell:

Halted

Now we can safely click on the Enable content button and nothing will happen! We will stop at the =HALT() function but we can now inspect other cells and contents around this file.

Since the document is heavily obfuscated, we will want to somehow debug it – single step through it. In this particular case, this was not all that difficult, but keep in mind that with a very complex and obfuscated document, the following activities might be more difficult to perform (but still easier than performing static analysis).

What we will want to do here is (ab)use the =HALT() function to execute a formula in a single cell and then stop the execution. This will allow us to examine what happened, evaluate the formula and continue. In the example below, you can see that I copied contents of all cells under the first one (the original Auto_Open cell) and put =HALT() in the cell immediately after the first one. This will cause Excel to stop processing formulas:

We can now use Excel’s built-in evaluation. In order to do that we will right click on the first cell, select Run and then Step Into. This is what we will get:

Pretty cool! Notice that nice “Evaluate” button? Let’s see what it does:

So this is what the first cell does! Depending on how complex this is, we might need to click on “Step Into”, which will take us further down the rabbit hole (Hi Neo!) and we will start evaluating whatever is under this particular function. Since I was impatient I clicked on “Continue” – remember that I put our “breakpoint” with the =HALT() function and this will be kind of similar to pressing F9 in your debugger.

In this document there was a bunch of functions called by the top one – most of them actually deobfuscated various content which is now populated in “new” cells in this worksheet. Keep this in mind – a nasty document could actually change contents of our =HALT() cell, which would lead to the payload fully executing.

Since it was not the case here, we continue by shifting the =HALT() breakpoint to the next row, like this:

You can probably guess what we’ll do next – right click on the =REGISTER() cell, click on Run then Step Into, and this is what we get:

Let’s Evaluate this again – it should work because the cell that got executed before populated what this (currently executing) cell needs:

Interesting! The REGISTER() function allows us to create a defined name which will point to a function in an external DLL. Sounds fantastic for the attacker – what they are doing here is create a name called “bBpmgyvS” which will point to the CreateDirectoryA function in the Kernel32.dll library. It is quite clear that the attacker will want to create a directory on the local machine.

And now it is rinse and repeat – we use the same method to evaluate all other cells until we figure out what the document is doing. The one I was analyzing uses the same mechanism to create another name that points to URLDownloadToFileA from the URLMON.dll library, which is used to download the second stage binary.
The same mechanism is again used to create a name that points to ShellExecuteA function from the Shell32.dll library which executes the downloaded binary.

At the end, the attacker hides this sheet and shows a decoy one.

And this leads us to the end of this diary/tutorial. I hope you found it interesting and useful, and that it will help someone analyze such heavily obfuscated Excel 4 macro malicious files which, this time with help of Microsoft’s own Excel can be relatively easily dynamically analyzed.

Finally, I have to stress out one more time that you should be ultra careful when performing such an analysis since we will be executing malicious code.
 


Bojan
@bojanz
INFIGO IS

(c) SANS Internet Storm Center. https://isc.sans.edu Creative Commons Attribution-Noncommercial 3.0 United States License.

Using the NVD Database and API to Keep Up with Vulnerabilities and Patches – Tool Drop: CVEScan (Part 3 of 3), (Mon, Jan 11th)

This post was originally published on this site

Now with a firm approach to or putting an inventory and using the NVD API (https://isc.sans.edu/forums/diary/Using+the+NIST+Database+and+API+to+Keep+Up+with+Vulnerabilities+and+Patches+Part+1+of+3/26958/ and https://isc.sans.edu/forums/diary/Using+the+NIST+Database+and+API+to+Keep+Up+with+Vulnerabilities+and+Patches+Playing+with+Code+Part+2+of+3/26964/), for any client I typically create 4 inventories:

  • Devices/appliances and applications that are exposed at the perimeter (public internet or other firewalled trust boundary)
  • Server applications and devices/appliances
  • Workstation  applications
  • IOT devices (in workstation or dedicated VLANs/subnets)

As we've noted, you can use nmap as a short-cut for a first draft of any products that have listening ports.  To just get your the CPE list for a subnet or range of IPs, this does the trick nicely:

nmap -p <ports> -sV –open <subnet> | grep -i cpe | awk -F" "  "{print $NF}"

(-F gives the delimiter, the print command prints the $NF field.  Since $NF is the number of fields, it prints the last one, which happens to be the CPE).

Let's focus on the first one of these target list – perimeter services for an actual customer.
Starting with Cisco FTD (Firepower Threat Defense), we see that even the titles vary from version to version, and the versions are very granular for this product
>type official-cpe-dictionary_v2.3.xml | grep -i title | grep -i cisco | grep -i firepower | grep -i -v management | grep -i "threat defense"
( excerpt only)

    <title xml:lang="en-US">Cisco Firepower Threat Defense (FTD) 6.5.0.3</title>
    <title xml:lang="en-US">Cisco Firepower Threat Defense 6.5.0.5</title>
    <title xml:lang="en-US">Cisco Firepower Threat Defense 6.6.0</title>
    <title xml:lang="en-US">Cisco Firepower Threat Defense (FTD) 6.6.1</title>

Since this is such a lengthy (and version-specific) list, let's try to consolidate.  From cisco's download site, the latest and recommended version (as of today) is 6.6.1.  Knowing that this client will be "close to current" on this, a quick look for FTD 6.6:

"cpe:2.3:a:cisco:firepower_threat_defense:6.6.*:*:*:*:*:*:*:*"

gives us these hits:

cpe:2.3:a:cisco:firepower_threat_defense:6.6.0:*:*:*:*:*:*:*
cpe:2.3:a:cisco:firepower_threat_defense:6.6.1:*:*:*:*:*:*:*

So our final input data file has the following (hostname followed by the cpe "blanket" query):

"dc01-fw01","cpe:2.3:a:cisco:firepower_threat_defense:6.6.*"

Let's add in the Citrix Netscaler Gateway (now called ADC).  The ADC is a pretty versatile appliance, it can be a load balancer, a firewall, a front-end for a Citrix farm, or (just like everyone else these days) and SD-WAN solution.  In our case it's a front-end for a Citrix XenServer farm.
The current version is 13.x, so let's search for all of 13.*:

cpe:2.3:o:citrix:application_delivery_controller_firmware:13.*

Finally, this client also has an application that uses Apache Struts, which they have been very particular about monitoring since the Equifax breach:
The current stable version is 2.5.26, let's hunt for cpe:

cpe:2.3:a:apache:struts:2.5.*

So our perimeter input file will look like this (again, the fields are hostname,cpe):

"dc01-fw01","cpe:2.3:a:cisco:firepower_threat_defense:6.6.*"
"dc01-adc01","cpe:2.3:o:citrix:application_delivery_controller_firmware:13.*"
"dc01-appsrv01","cpe:2.3:a:apache:struts:2.5.*"

We'll call our code with (note the input filename):

cvescan.ps1 -i Customername.Perimeter.in -d 90

This will give us the CVEs for the indicated platforms, for the last 90 days, sorted from high severity to low.

And our code will look like the listing below (maintained at https://github.com/robvandenbrink/CVEScan ):

##########################################################################

# CVESCAN

# Version 1.iscisc0

# Assess an inventoried infrastructure from pre-inventoried CPEs and published CVEs

#

# Hosted at https://github.com/robvandenbrink/CVEScan

#

# Further documentation at:

#         https://isc.sans.edu

#         https://isc.sans.edu

#         https://isc.sans.edu

#

# Syntax:

#         CVEScan.ps1  -i <input file> -d <how many days back to look>

##########################################################################

 

param (

[alias("i")]

$infile,

[alias("d")]

$daterange

)

 

function helpsyntax {

write-host "CVESCAN: Assess a known inventory against current CVEs"

write-host "Parameters:"

write-host "    -i          <input file name>"

write-host "Optional Parameters:"

write-host "    -d          <CVEs for last "n" days>"

write-host "cvescan -i perimeterdevices.in -d 60"

exit

}

 

if ($daterange -eq 0) { write-host "ERROR: Must specify input filename and date range`n" ; helpsyntax }

 

# setup

$allCVEs = @()

$CVEDetails = @()

 

$apps = Import-Csv -path $infile

$now = get-date

$outfile = $infile.replace(".in",$now.tostring("yyyy-MM-dd_hh-mm")+"_"+$daterange+"-days.html")

$StartDate = $now.adddays(-$daterange).tostring("yyyy-MM-dd")+ "T00:00:00:000%20UTC-00:00"

 

# Collect host to CVEs table

foreach ($app in $apps) {

    $request = "https://services.nvd.nist.gov/rest/json/cves/1.0?modStartDate=" + $StartDate + "&cpeMatchString=" + $app.cpe

    $CVEs = (invoke-webrequest $request | ConvertFrom-Json).result.CVE_items.cve.CVE_data_meta.id

    foreach ($CVE in $CVEs) {

        $tempobj = [pscustomobject]@{

            Hostname = $app.hostname

            CVE = $CVE

           }

        $allCVEs += $tempobj

        }

    }

 

$Header = @"

<style>

TABLE {border-width: 1px; border-style: solid; border-color: black; border-collapse: collapse;}

TH {border-width: 1px; padding: 3px; border-style: solid; border-color: black; background-color: #6495ED;}

TD {border-width: 1px; padding: 3px; border-style: solid; border-color: black;VERTICAL-ALIGN: TOP; font-size: 15px}

</style>

"@

 

$filepath = gci $infile

 

$Title = @()

$Title += [pscustomobject]@{ Organization="Scope";bbb=$filepath.basename.split(".")[1] }

$Title += [pscustomobject]@{ Organization="From Date:"; bbb=($now.adddays(-$daterange).tostring("yyyy-MM-dd")) }

$Title += [pscustomobject]@{ Organization="To Date:";bbb=$now.tostring("yyyy-MM-dd") }

 

(($Title | convertto-HTML -title "CVE Summary" -Head $header) + "<br><br><br>").replace("bbb",$filepath.basename.split(".")[0]) | out-file  $outfile

 

(($allCVEs | Convertto-HTML -Head $header) + "<br><br>") | out-file -append $outfile

 

#parse out just the CVEs

$justCVEs = $allCVEs | select CVE | Sort-Object | Get-Unique -AsString

 

# collect CVE info

foreach ($CVE in $justCVEs) {

    $h = ""

    $request = "https://services.nvd.nist.gov/rest/json/cve/1.0/" + $CVE.CVE

    $cvemetadata = (invoke-webrequest $request) | convertfrom-json

    $CVEURLs = $cvemetadata.result.cve_items.cve.references.reference_data.url

    $affectedApps = ($cvemetadata.result.CVE_items.configurations.nodes.children.cpe_match) | where {$_.vulnerable -eq "true" } | select cpe23Uri,versionendincluding

 

    # add the affected hosts back into the detailed listing

    # write-host $CVE.CVE

    foreach ($ac in $allCVEs) {

        if ($ac.CVE -eq $CVE.CVE) {

            $h += ($ac.Hostname + "<br>")

            }

        }

 

    $tempobj = [pscustomobject]@{

        CVE = $CVE.CVE

        Hosts = $h

        # Just the datestamp, remove the clock time

        "Published Date" = ($cvemetadata.result.cve_items.publishedDate).split("T")[0]

        "CVE Description" = $cvemetadata.result.cve_items.cve.description.description_data.value

        Vector = $cvemetadata.result.CVE_items.impact.basemetricv3.cvssv3.attackVector

        "Attack Complexity" = $cvemetadata.result.CVE_items.impact.basemetricv3.cvssv3.attackComplexity

        "User Interaction" = $cvemetadata.result.CVE_items.impact.basemetricv3.cvssv3.userInteraction

        "Base Score" = $cvemetadata.result.CVE_items.impact.basemetricv3.cvssv3.baseScore

        "Severity" = $cvemetadata.result.CVE_items.impact.basemetricv3.cvssv3.baseSeverity

        "Reference URLs" = ($CVEURLs | ft -hidetableheaders | out-string).replace("`n","`n<br>")

        "Affected Apps" = ($affectedapps | ft -HideTableHeaders | out-string).replace("`n","`n<br>")

        }

    $CVEDetails += $tempobj

    }

 

# to just view the detailed output

# $CVEDetails | out-gridview

 

# to output to HTML

$Header = @"

<style>

TABLE {border-width: 1px; border-style: solid; border-color: black; border-collapse: collapse;}

TH {border-width: 1px; padding: 3px; border-style: solid; border-color: black; background-color: #6495ED;}

TD {border-width: 1px; padding: 3px; border-style: solid; border-color: black;VERTICAL-ALIGN: TOP; font-size: 15px}

</style>

"@

 

# Note that the <br> tags get escaped, these are un-escaped below

# this is a horrible hack, but I can't find a decent "elegant" way to do this

# … in less than 5x the time it took me to do it the ugly way  🙂

 

 

(($CVEDetails | sort -descending -property "Base Score" )| Convertto-HTML -Head $header) -replace '&lt;br&gt;', '<br>' | out-file  -append $outfile

 

Our output is dumped into: Customername.Perimeter-dateandtime-days.html, so for this example and today's date: Customername.Perimeter2021-01-11_09-50_90-days.html (note that the output filename mirrors the input filename – change that if you need)

Note also in the output that I had to un-escape all of the line breaks that were in the output (sometimes the quick and dirty methods win over perfect code)

Looking at that file, our output (truncated) looks as below.  The lead in is the customer and date range info, followed by the CVE's found on which host.  The final table contains all the CVE details, in descending / unique order of "Base Score" of Severity:

 

 As mentioned, the code is on my github – use it or modify it to suit your needs.  For the most part it's a short list of API requests, with parsing, formatting and I/O bolted on – so if you'd prefer this to be in a different language of course feel free!

If you were able to head off a "situation" in your environment, or if that nmap trick finds something unexpected in your environment, please do post to our comment form (subject to NDA's of course)

===============
Rob VandenBrink
rob@coherentsecurity.com

(c) SANS Internet Storm Center. https://isc.sans.edu Creative Commons Attribution-Noncommercial 3.0 United States License.

Maldoc Analysis With CyberChef, (Sun, Jan 10th)

This post was originally published on this site

In diary entry "Maldoc Strings Analysis" I show how to analyze a malicious document, by extracting and dedocing strings with command-line tools.

In this video, I analyze the same malicious Word document, using CyberChef only. This is possible, because this particular maldoc contains a very long string with the payload, and this string can be extracted without parsing the structure of this .doc file.

I pasted the recipe on pastebin here.

Didier Stevens
Senior handler
Microsoft MVP
blog.DidierStevens.com DidierStevensLabs.com

(c) SANS Internet Storm Center. https://isc.sans.edu Creative Commons Attribution-Noncommercial 3.0 United States License.