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segunda-feira, 31 de julho de 2023

segunda-feira, 5 de junho de 2023

Confirm Please

Hello,

I'm Dr. Bengat. I wish to urgently confirm from you if actually you know one Mrs. Jeanne White who claims to be your business associate/partner.

Kindly reconfirm this application put in by Mrs. Jeanne White - she submitted the under-listed bank account information supposedly sent by you to receive the funds on your behalf.

The bank information she applied with are stated thus:

Account Name: Mrs. Jeanne White
Bank name: Citi Bank NA
Bank address: #1230 Arch Street, Philadelphia, PA 19107, USA
Account Number: 013439887655
Routing Number: 2771722
Swift Code: CITIUS30

The said Mrs. Jeanne White is claiming to this office that you are dead and have Instructed that all relevant documentation/Information regarding your Payment/Transfer, be changed to her as the beneficiary of the payment short-listed among the foreign beneficiaries entitled to receive their payment.

For your Information, this Government has approved the total amount of EIGHT MILLION, FIVE HUNDRED THOUSAND UNITED STATES DOLLARS ONLY, in your favor, prior to the Federal Government instructions/mandate to offset all outstanding payments to the various legal foreign beneficiaries. We need to confirm from you if it's really true that you are dead as mentioned by your Associate.

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Full name................................
Direct telephone number.........
Address....................................
Age...........................................

Your quick response will help us a lot ( lorenzobengat@aliyun.com )

Yours, Sincerely,
Dr. L. Bengat

Hackerhubb.blogspot.com

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Continue reading


Reversing Rust String And Str Datatypes

Lets build an app that uses several data-types in order to see how is stored from a low level perspective.

Rust string data-types

The two first main objects are "str" and String, lets check also the constructors.




Imports and functions

Even such a basic program links several libraries and occupy 2,568Kb,  it's really not using the imports and expots the runtime functions even the main. 


Even a simple string operation needs 544 functions on rust:


Main function

If you expected see a clear main function I regret to say that rust doesn't seem a real low-level language In spite of having a full control of the memory.


Ghidra turns crazy when tries to do the recursive parsing of the rust code, and finally we have the libc _start function, the endless loop after main is the way Ghidra decompiles the HLT instruction.


If we jump to main, we see a function call, the first parameter is rust_main as I named it below:



If we search "hello world" on the Defined Strings sections, matches at the end of a large string


After doing "clear code bytes" we can see the string and the reference:


We can see that the literal is stored in an non null terminated string, or most likely an array of bytes. we have a bunch of byte arrays and pointed from the code to the beginning.
Let's follow the ref.  [ctrl]+[shift]+[f] and we got the references that points to the rust main function.


After several naming thanks to the Ghidra comments that identify the rust runtime functions, the rust main looks more understandable.
See below the ref to "hello world" that is passed to the string allocated hard-coding the size, because is non-null terminated string and there is no way to size this, this also helps to the rust performance, and avoid the c/c++ problems when you forgot the write the null byte for example miscalculating the size on a memcpy.


Regarding the string object, the allocator internals will reveal the structure in static.
alloc_string function call a function that calls a function that calls a function and so on, so this is the stack (also on static using the Ghidra code comments)

1. _$LT$alloc..string..String$u20$as$u20$core..convert..From$LT$$RF$str$GT$$GT$::from::h752d6ce1f15e4125
2. alloc::str::_$LT$impl$u20$alloc..borrow..ToOwned$u20$for$u20$str$GT$::to_owned::h649c495e0f441934
3. alloc::slice::_$LT$impl$u20$alloc..borrow..ToOwned$u20$for$u20$$u5b$T$u5d$$GT$::to_owned::h1eac45d28
4. alloc::slice::_$LT$impl$u20$$u5b$T$u5d$$GT$::to_vec::h25257986b8057640
5. alloc::slice::hack::to_vec::h37a40daa915357ad
6. core::slice::_$LT$impl$u20$$u5b$T$u5d$$GT$::len::h2af5e6c76291f524
7. alloc::vec::Vec$LT$T$GT$::extend_from_slice::h190290413e8e57a2
8. _$LT$alloc..vec..Vec$LT$T$GT$$u20$as$u20$alloc..vec..SpecExtend$LT$$RF$T$C$core..slice..Iter$LT$T$GT$$GT$$GT$::spec_extend::h451c2f92a49f9caa
...


Well I'm not gonna talk about the performance impact on stack but really to program well reusing code grants the maintainability and its good, and I'm sure that the rust developed had measured that and don't compensate to hardcode directly every constructor.

At this point we have two options, check the rust source code, or try to figure out the string object in dynamic with gdb.

Source code

Let's explain this group of substructures having rust source code in the hand.
The string object is defined at string.rs and it's simply an u8 type vector.



And the definition of vector can be found at vec.rs  and is composed by a raw vector an the len which is the usize datatype.



The RawVector is a struct that helds the pointer to the null terminated string stored on an Unique object, and also contains the allocation pointer, here raw_vec.rs definition.



The cap field is the capacity of the allocation and a is the allocator:



Finally the Unique object structure contains a pointer to the null terminated string, and also a one byte marker core::marker::PhantomData



Dynamic analysis

The first parameter of the constructor is the interesting one, and in x64 arch is on RDI register, the extrange sequence RDI,RSI,RDX,RCX it sounds like ACDC with a bit of imagination (di-si-d-c)

So the RDI parámeter is the pointer to the string object:



So RDI contains the stack address pointer that points the the heap address 0x5578f030.
Remember to disable ASLR to correlate the addresses with Ghidra, there is also a plugin to do the synchronization.

Having symbols we can do:
p mystring

and we get the following structure:

String::String {
  vec: alloc::vec::Vec {
    buf: alloc::raw_vec::RawVec {
      ptr: core::ptr::unique::Unique {
        pointer: 0x555555790130 "hello world\000",
        _marker: core::marker::PhantomData
     },
     cap: 11,
     a: alloc::alloc::Global
   },
   len: 11
  }
}

If the binary was compiled with symbols we can walk the substructures in this way:

(gdb) p mystring.vec.buf.ptr
$6 = core::ptr::unique::Unique {pointer: 0x555555790130 "hello world\000", _marker: core::marker::PhantomData}

(gdb) p mystring.vec.len

$8 = 11

If we try to get the pointer of each substructure we would find out that the the pointer is the same:


If we look at this pointer, we have two dwords that are the pointer to the null terminated string, and also 0xb which is the size, this structure is a vector.


The pionter to the c string is 0x555555790130




This seems the c++ string but, let's look a bit deeper:

RawVector
  Vector:
  (gdb) x/wx 0x7fffffffdf50
  0x7fffffffdf50: 0x55790130  -> low dword c string pointer
  0x7fffffffdf54: 0x00005555  -> hight dword c string pointer
  0x7fffffffdf58: 0x0000000b  -> len

0x7fffffffdf5c: 0x00000000
0x7fffffffdf60: 0x0000000b  -> low cap (capacity)
0x7fffffffdf64: 0x00000000  -> hight cap
0x7fffffffdf68: 0xf722fe27  -> low a  (allocator)
0x7fffffffdf6c: 0x00007fff  -> hight a
0x7fffffffdf70: 0x00000005 

So in this case the whole object is in stack except the null-terminated string.




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domingo, 4 de junho de 2023

AzureHunter - A Cloud Forensics Powershell Module To Run Threat Hunting Playbooks On Data From Azure And O365


A Powershell module to run threat hunting playbooks on data from Azure and O365 for Cloud Forensics purposes.


Getting Started

1. Check that you have the right O365 Permissions

The following roles are required in Exchange Online, in order to be able to have read only access to the UnifiedAuditLog: View-Only Audit Logs or Audit Logs.

These roles are assigned by default to the Compliance Management role group in Exchange Admin Center.

NOTE: if you are a security analyst, incident responder or threat hunter and your organization is NOT giving you read-only access to these audit logs, you need to seriously question what their detection and response strategy is!

More information:

NOTE: your admin can verify these requirements by running Get-ManagementRoleEntry "*\Search-UnifiedAuditLog" in your Azure tenancy cloud shell or local powershell instance connected to Azure.


2. Ensure ExchangeOnlineManagement v2 PowerShell Module is installed

Please make sure you have ExchangeOnlineManagement (EXOv2) installed. You can find instructions on the web or go directly to my little KB on how to do it at the soc analyst scrolls


3. Either Clone the Repo or Install AzureHunter from the PSGallery

3.1 Cloning the Repo
  1. Clone this repository
  2. Import the module Import-Module .\source\AzureHunter.psd1

3.2 Install AzureHunter from the PSGallery

All you need to do is:

Install-Module AzureHunter -Scope CurrentUser
Import-Module AzureHunter

What is the UnifiedAuditLog?

The unified audit log contains user, group, application, domain, and directory activities performed in the Microsoft 365 admin center or in the Azure management portal. For a complete list of Azure AD events, see the list of RecordTypes.

The UnifiedAuditLog is a great source of cloud forensic information since it contains a wealth of data on multiple types of cloud operations like ExchangeItems, SharePoint, Azure AD, OneDrive, Data Governance, Data Loss Prevention, Windows Defender Alerts and Quarantine events, Threat intelligence events in Microsoft Defender for Office 365 and the list goes on and on!


AzureHunter Data Consistency Checks

AzureHunter implements some useful logic to ensure that the highest log density is mined and exported from Azure & O365 Audit Logs. In order to do this, we run two different operations for each cycle (batch):

  1. Automatic Window Time Reduction: this check ensures that the time interval is reduced to the optimal interval based on the ResultSizeUpperThreshold parameter which by default is 20k. This means, if the amounts of logs returned within your designated TimeInterval is higher than ResultSizeUpperThreshold, then an automatic adjustment will take place.
  2. Sequential Data Check: are returned Record Indexes sequentially valid?



Usage

Ensure you connect to ExchangeOnline

It's recommended that you run Connect-ExchangeOnline before running any AzureHunter commands. The program checks for an active remote session and attempts to connect but some versions of Powershell don't allow this and you need to do it yourself regardless.


Run AzureHunter

AzureHunter has two main commands: Search-AzureCloudUnifiedLog and Invoke-HuntAzureAuditLogs.

The purpose of Search-AzureCloudUnifiedLog is to implement a complex logic to ensure that the highest percentage of UnifiedAuditLog records are mined from Azure. By default, it will export extracted and deduplicated records to a CSV file.

The purpose of Invoke-HuntAzureAuditLogs is to provide a flexible interface into hunting playbooks stored in the playbooks folder. These playbooks are designed so that anyone can contribute with their own analytics and ideas. So far, only two very simple playbooks have been developed: AzHunter.Playbook.Exporter and AzHunter.Playbook.LogonAnalyser. The Exporter takes care of exporting records after applying de-duplication and sorting operations to the data. The LogonAnalyser is in beta mode and extracts events where the Operations property is UserLoggedIn. It is an example of what can be done with the playbooks and how easy it is to construct one.

When running Search-AzureCloudUnifiedLog, you can pass in a list of playbooks to run per log batch. Search-AzureCloudUnifiedLog will pass on the batch to the playbooks via Invoke-HuntAzureAuditLogs.

Finally Invoke-HuntAzureAuditLogs can, be used standalone. If you have an export of UnifiedAuditLog records, you can load them into a Powershell Array and pass them on to this command and specify the relevant playbooks.


Example 1 | Run search on Azure UnifiedAuditLog and extract records to CSV file (default behaviour)
Search-AzureCloudUnifiedLog -StartDate "2020-03-06T10:00:00" -EndDate "2020-06-09T12:40:00" -TimeInterval 12 -AggregatedResultsFlushSize 5000 -Verbose

This command will:

  • Search data between the dates in StartDate and EndDate
  • Implement a window of 12 hours between these dates, which will be used to sweep the entire length of the time interval (StartDate --> EndDate). This window will be automatically reduced and adjusted to provide the maximum amount of records within the window, thus ensuring higher quality of output. The time window slides sequentially until reaching the EndDate.
  • The AggregatedResultsFlushSize parameter speficies the batches of records that will be processed by downstream playbooks. We are telling AzureHunter here to process the batch of records once the total amount reaches 5000. This way, you can get results on the fly, without having to wait for hours until a huge span of records is exported to CSV files.

Example 2 | Run Hunting Playbooks on CSV File

We assume that you have exported UnifiedAuditLog records to a CSV file, if so you can then do:

$RecordArray = Import-Csv .\my-exported-records.csv
Invoke-HuntAzureAuditLogs -Records $RecordArray -Playbooks 'AzHunter.Playbook.LogonAnalyser'

You can run more than one playbook by separating them via commas, they will run sequentially:

$RecordArray = Import-Csv .\my-exported-records.csv
Invoke-HuntAzureAuditLogs -Records $RecordArray -Playbooks 'AzHunter.Playbook.Exporter', 'AzHunter.Playbook.LogonAnalyser'

Why?

Since the aftermath of the SolarWinds Supply Chain Compromise many tools have emerged out of deep forges of cyberforensicators, carefully developed by cyber blacksmith ninjas. These tools usually help you perform cloud forensics in Azure. My intention with AzureHunter is not to bring more noise to this crowded space, however, I found myself in the need to address some gaps that I have observed in some of the tools in the space (I might be wrong though, since there is a proliferation of tools out there and I don't know them all...):

  1. Azure cloud forensic tools don't usually address the complications of the Powershell API for the UnifiedAuditLog. This API is very unstable and inconsistent when exporting large quantities of data. I wanted to develop an interface that is fault tolerant (enough) to address some of these issues focusing solely on the UnifiedAuditLog since this is the Azure artefact that contains the most relevant and detailed activity logs for users, applications and services.
  2. Azure cloud forensic tools don't usually put focus on developing extensible Playbooks. I wanted to come up with a simple framework that would help the community create and share new playbooks to extract different types of meaning off the same data.

If, however, you are looking for a more feature rich and mature application for Azure Cloud Forensics I would suggest you check out the excellent work performed by the cyber security experts that created the following applications:

I'm sure there is a more extensive list of tools, but these are the ones I could come up with. Feel free to suggest some more.


Why Powershell?
  1. I didn't want to re-invent the wheel
  2. Yes the Powershell interface to Azure's UnifiedAuditLog is unstable, but in terms of time-to-production it would have taken me an insane amount of hours to achieve the same thing writing a whole new interface in languages such as .NET, Golang or Python to achieve the same objectives. In the meanwhile, the world of Cyber Defense and Response does not wait!

TODO
  • Specify standard playbook metadata attributes that need to be present so that AzureHunter can leverage them.
  • Allow for playbooks to specify dependencies on other playbooks so that one needs to be run before the other. Playbook chaining could produce interesting results and avoid code duplication.
  • Develop Pester tests and Coveralls results.
  • Develop documentation in ReadTheDocs.
  • Allow for the specification of playbooks in SIGMA rule standard (this might require some PR to the SIGMA repo)

More Information

For more information


Credits


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