sentence stringlengths 1 13.5k | labels listlengths 0 15 | doc_title stringclasses 84
values |
|---|---|---|
The idea of deception originates from the decades old honeypot systems but, unlike those, relies more on forging trust and giving adversaries what they are looking for. | [] | Detecting Credential Stealing Attacks Through Active InNetwork Defense |
With its inbuilt proactiveness it is configured to lure attackers towards deceptive systems. | [] | Detecting Credential Stealing Attacks Through Active InNetwork Defense |
As shown in the figure below, Network Deception consists of authentic looking decoy systems placed within the domain network, specifically in the network where the critical assets are placed. | [] | Detecting Credential Stealing Attacks Through Active InNetwork Defense |
These decoy systems (could be virtual machines) are the full-fledged OS with configured applications or services and could be replicating the crucial services like Domain Controller, Exchange or DB server and other decoy machines that could lead to those systems. | [] | Detecting Credential Stealing Attacks Through Active InNetwork Defense |
The image below highlights the key foundational aspects of the Network Deception Figure 17 – Network Deception Key Aspects of Network Deception As visualized in the figure above, Network Deception comprises the following key basic facts with respect to the deployment in the domain joined environment: As a part of deplo... | [] | Detecting Credential Stealing Attacks Through Active InNetwork Defense |
These decoy systems could be real systems or virtual systems with production grade operating systems with the required setup to make them blend well with real systems. | [] | Detecting Credential Stealing Attacks Through Active InNetwork Defense |
As one of the key aspects, deceptive machines are configured to lure attackers towards the decoy services instead of the production services, thereby deflecting or misleading the attacker’s lateral movement path to the target asset. | [] | Detecting Credential Stealing Attacks Through Active InNetwork Defense |
Many of the decoy machines could replicate critical services like Domain Controller, DB servers, Exchange/SharePoint servers and other critical services or applications within the data center. | [] | Detecting Credential Stealing Attacks Through Active InNetwork Defense |
Any legitimate domain user should not be generating traffic to or communicating with the configured decoy machines unless there are some misconfigurations in the network, which need to be corrected. | [] | Detecting Credential Stealing Attacks Through Active InNetwork Defense |
Basic Decoy Network Setup Since credential theft plays an important role in a successful targeted attack, deception essentially focuses on planting fake credentials on the production and decoy endpoints at multiple places within the OS and monitoring the use of these credentials to pivot to other systems. | [] | Detecting Credential Stealing Attacks Through Active InNetwork Defense |
With respect to the network setup, the following are the key aspects, however this list is not exhaustive, and much more could be added: Replicating critical network assets and services with decoy machines: Replicating critical network services like Active Directory, DB services, etc., will make more sense since these ... | [] | Detecting Credential Stealing Attacks Through Active InNetwork Defense |
The decoy Active Directory should be configured with deceptive AD objects (users, groups, SPNs, etc.). | [] | Detecting Credential Stealing Attacks Through Active InNetwork Defense |
with deceptive contents for other replicated services. | [] | Detecting Credential Stealing Attacks Through Active InNetwork Defense |
Planting authentic looking decoy machines in the production network: As indicated earlier, these decoy machines could be real or virtual machines with the production grade OS placed alongside production systems in the critical infrastructure to blend in well. | [] | Detecting Credential Stealing Attacks Through Active InNetwork Defense |
These decoy machines should be joined to the decoy AD and configured with deceptive user accounts to monitor successful logon attempts to the systems. | [] | Detecting Credential Stealing Attacks Through Active InNetwork Defense |
Injecting deceptive credentials on production endpoints: Production endpoints should be injected with deceptive credentials at multiple places like LSASS process memory, Credential Manager, browser credentials, etc., to increase the possibility of these credentials being picked up and used to pivot to decoy systems in ... | [
"T1055",
"T1078"
] | Detecting Credential Stealing Attacks Through Active InNetwork Defense |
These endpoints could be public facing machines or their replicas as well. | [] | Detecting Credential Stealing Attacks Through Active InNetwork Defense |
Decoy Machine runs client applications pointing to decoy services: Decoy machines may run the client with deceptive credentials and configured to point to the decoy services. | [] | Detecting Credential Stealing Attacks Through Active InNetwork Defense |
These could be DB/FTP/Email clients and any other replicated decoy services. | [] | Detecting Credential Stealing Attacks Through Active InNetwork Defense |
Mark decoy systems as “NO LANDING ZONE”: One of the key deployment aspects of deception is to mark all the decoy systems and services as “NO LANDING ZONE”, essentially meaning no legitimate domain users should be accessing decoys and any attempts to access these systems should be closely monitored. | [] | Detecting Credential Stealing Attacks Through Active InNetwork Defense |
Some of the other setup required for effective deployment of deception is as summarized below: Figure 18 – Deceptive network setup – Basic requirements Basic Decoy Systems Setup To detect the use of deceptive credentials, setting up decoy machines is an essential part of the solution as well. | [] | Detecting Credential Stealing Attacks Through Active InNetwork Defense |
Primarily, decoy machines should enable the access attackers are looking to have during the lateral movement phase. | [] | Detecting Credential Stealing Attacks Through Active InNetwork Defense |
Decoys should also be configured to enable relevant auditing services to be able to generate events. | [] | Detecting Credential Stealing Attacks Through Active InNetwork Defense |
For instance, the following enables the account logon events to be audited: Decoy machines must be setup to run the log collector agent that can collect the access logs generated and forward them to the correlation server. | [] | Detecting Credential Stealing Attacks Through Active InNetwork Defense |
However, in the domain joined environment, it is also essential to tune the decoy machines to forward only the relevant logs to the correlation server to minimize false positives. | [] | Detecting Credential Stealing Attacks Through Active InNetwork Defense |
The below highlights some of the auditing required to be enabled on the decoy systems for effective correlation. | [] | Detecting Credential Stealing Attacks Through Active InNetwork Defense |
Figure 19 – Basic decoy setup Illustrating and Achieving Network Deception | [] | Detecting Credential Stealing Attacks Through Active InNetwork Defense |
The following sections describe some examples of how deception can be achieved in the domain network, along with a visualization of how credential theft can be detected. | [] | Detecting Credential Stealing Attacks Through Active InNetwork Defense |
Network Deception – Example 1: Injecting NETONLY credentials into LSASS process memory LSASS process memory is one of the prime targets for attackers, as well as malware armed with lateral movement capabilities since it caches a variety of credentials. | [
"T1003.001",
"T1055"
] | Detecting Credential Stealing Attacks Through Active InNetwork Defense |
Credential extraction from the LSASS process requires opening a read handle to the process itself which is closely monitored by EDR products but there are stealthier ways around it. | [
"T1003.001"
] | Detecting Credential Stealing Attacks Through Active InNetwork Defense |
One of the primary tasks towards achieving credential-based deception is to stage the deceptive credentials in LSASS process memory. | [
"T1003.001"
] | Detecting Credential Stealing Attacks Through Active InNetwork Defense |
This can be accomplished on the production and decoy systems by executing a trivial credential injection code which uses the CreateProcessWithLogonW Windows API with the specified crafted credentials. | [
"T1106"
] | Detecting Credential Stealing Attacks Through Active InNetwork Defense |
CreateProcessWithLogonW creates the new logon session using the caller process access token and spawns the process specified as a parameter in the security context of the specified deceptive credentials and it will be staged in the LSASS memory until the process runs in the background. | [] | Detecting Credential Stealing Attacks Through Active InNetwork Defense |
The below shows the example code calling the API with the specified credentials which is also visible when credentials are extracted with Mimikatz. | [
"T1003.001",
"T1106"
] | Detecting Credential Stealing Attacks Through Active InNetwork Defense |
Figure 20 – Injecting credentials into LSASS memory One of the parameters to CreateProcessWithLogonW is “dwLogonFlags” which should be specified as LOGON_NETCREDENTIALS_ONLY as shown in the code above. | [
"T1055"
] | Detecting Credential Stealing Attacks Through Active InNetwork Defense |
This ensures the specified credentials are used only on the network and not for local logons. | [] | Detecting Credential Stealing Attacks Through Active InNetwork Defense |
Additionally, NETONLY credentials used to create a logon session are not validated by the system. | [] | Detecting Credential Stealing Attacks Through Active InNetwork Defense |
Below is a code snapshot from credential extraction tool Mimikatz, using a similar approach to forge a logon session and replacing the credentials with the supplied ones while executing Pass-the-Hash attacks. | [
"T1003.001"
] | Detecting Credential Stealing Attacks Through Active InNetwork Defense |
Figure 21 – Mimikatz code for PTH attack Network Deception – Example 2: Configure deceptive hostnames for decoy VMs Attackers or malware moving laterally inside the network might do a recon for interesting hostnames via nbtstat/nbtscan. | [
"T1082"
] | Detecting Credential Stealing Attacks Through Active InNetwork Defense |
To deflect the lateral movement path, decoy systems can be configured with real looking hostnames that match the production systems. | [] | Detecting Credential Stealing Attacks Through Active InNetwork Defense |
These hostnames will then be visible on NetBIOS scans as shown below. | [] | Detecting Credential Stealing Attacks Through Active InNetwork Defense |
Figure 22 – Deceptive host names pointing to decoy machines | [] | Detecting Credential Stealing Attacks Through Active InNetwork Defense |
These decoy systems can also run the relevant client applications pointing to the decoy services, with authentication directed to the decoy Domain Controller in the network. | [] | Detecting Credential Stealing Attacks Through Active InNetwork Defense |
Detection of this attack path happens much earlier, however the decoy network setup keeps the adversaries engaged, helping admins to study their Tools and Techniques. Figure 23 – Decoy machines running clients pointing to decoy services A similar deception setup can also be done for the browsers where saved credentials... | [] | Detecting Credential Stealing Attacks Through Active InNetwork Defense |
For instance, Chrome saves the credentials in the SQLite format on the disk which can be decrypted using DPAPI as discussed earlier sections. | [
"T1140"
] | Detecting Credential Stealing Attacks Through Active InNetwork Defense |
The below examples demonstrate deceptive browser credentials which can lure adversaries towards the decoy services. | [] | Detecting Credential Stealing Attacks Through Active InNetwork Defense |
Figure 24 – Inserting deceptive browser credentials | [] | Detecting Credential Stealing Attacks Through Active InNetwork Defense |
In addition to some of the techniques discussed above, and many others highlighted in the previous sections, setting up deception involves much more advanced configuration of decoy systems to minimize false positives and needs to be tuned to the environment to accurately identify malicious activities. | [] | Detecting Credential Stealing Attacks Through Active InNetwork Defense |
Deception can also be configured to address multiple other phases of lateral movement activity including reconnaissance and target discovery, essentially redirecting the adversaries and giving them a path to the target. | [] | Detecting Credential Stealing Attacks Through Active InNetwork Defense |
Below is a high-level visualization of how the decoy network can look like the domain environment. | [] | Detecting Credential Stealing Attacks Through Active InNetwork Defense |
Figure 25 – Deception network setup On the occasion where one of the domain-joined or public facing systems is compromised, authentication would be attempted to other domain joined systems in the network. | [
"T1190"
] | Detecting Credential Stealing Attacks Through Active InNetwork Defense |
If an authentication is attempted and any of the decoy systems are accessed and logged on, the use of these planted deceptive credentials should be a red flag and something which must be investigated. | [] | Detecting Credential Stealing Attacks Through Active InNetwork Defense |
The visualization below shows the flow and an event being sent to an administrator on accessing one of the decoy systems. | [] | Detecting Credential Stealing Attacks Through Active InNetwork Defense |
Figure 26 – Deceptive credentials usage for authentication in the domain One such example event of successfully logging on to the decoy system is as shown below: Figure 27 – Alert send to administrator on using deceptive credentials MITRE ATT&CK Techniques: Credential theft attacks discussed here are mapped by MITRE as... | [
"T1003.001"
] | Detecting Credential Stealing Attacks Through Active InNetwork Defense |
Administrative privileges are required to access the process memory. | [] | Detecting Credential Stealing Attacks Through Active InNetwork Defense |
T1003.002 SAM Database Accessing credentials from SAM database requires SYSTEM level privileges. | [] | Detecting Credential Stealing Attacks Through Active InNetwork Defense |
Stores credentials for all the local user accounts on the machine. | [] | Detecting Credential Stealing Attacks Through Active InNetwork Defense |
T1003.003 NTDS.dit file Contains credentials for all the domain users. | [] | Detecting Credential Stealing Attacks Through Active InNetwork Defense |
File is present on the DC and domain admin privileges are required to access this file. | [] | Detecting Credential Stealing Attacks Through Active InNetwork Defense |
T1003.006 DCSync Attacker can extract the credentials from the DC by impersonating the domain controller and use DRSUAPI protocol to replicate credentials from DC. | [] | Detecting Credential Stealing Attacks Through Active InNetwork Defense |
T1558.001 Golden Ticket Attackers acquiring credentials for KRBTGT account can forge the Kerberos ticket called Golden Ticket, allowing them to get unrestricted access to any system in the domain T1558.002 Silver Ticket Allows attacker to get admin level access to the service accounts by abusing Kerberos authentication... | [] | Detecting Credential Stealing Attacks Through Active InNetwork Defense |
With attackers’ lateral movement tactics evolving and getting more stealthier, defenders will have to adapt to innovative ways of defending the critical network assets. | [] | Detecting Credential Stealing Attacks Through Active InNetwork Defense |
In–network defense strategies like Deception could prove to be a promising and forward-looking approach towards detecting and mitigating data theft attacks. | [] | Detecting Credential Stealing Attacks Through Active InNetwork Defense |
Strategic planting of decoy systems within the production network, inserting decoy credentials and decoy contents on calculative selection of endpoints and decoy systems and accurately setting up the logging and correlation via SIEMs for monitoring the use of decoy contents, could certainly detect and mitigate the atta... | [] | Detecting Credential Stealing Attacks Through Active InNetwork Defense |
Endpoint solutions like User Entity Behavior Analytics (UEBA) and Endpoint Detection and Response (EDR) could also play a significant role in building the deception infrastructure. | [] | Detecting Credential Stealing Attacks Through Active InNetwork Defense |
For instance, one of the ways UEBA solutions could prove useful is to baseline user behavior and monitor access to credential stores on the system. | [] | Detecting Credential Stealing Attacks Through Active InNetwork Defense |
UEBA/EDR could raise the red flag on injection of forged Kerberos tickets in the memory. | [] | Detecting Credential Stealing Attacks Through Active InNetwork Defense |
This can provide user level visibility to a greater extent when integrated with SIEM, playing a crucial role in mitigating credential theft attacks. | [] | Detecting Credential Stealing Attacks Through Active InNetwork Defense |
The post Detecting Credential Stealing Attacks Through Active In-Network Defense appeared first on McAfee Blog. | [] | Detecting Credential Stealing Attacks Through Active InNetwork Defense |
Affected platforms: WindowsImpacted parties: Any organizationImpact: Controls victim’s device and collects sensitive informationSeverity level: Critical EvilExtractor (sometimes spelled Evil Extractor) is an attack tool designed to target Windows operating systems and extract data and files from endpoint devices. | [
"T1005"
] | EvilExtractor AllinOne Stealer |
It includes several modules that all work via an FTP service. | [] | EvilExtractor AllinOne Stealer |
It was developed by a company named Kodex, which claims it is an educational tool. | [] | EvilExtractor AllinOne Stealer |
However, research conducted by FortiGuard Labs shows cybercriminals are actively using it as an info stealer. | [] | EvilExtractor AllinOne Stealer |
Based on our traffic source data to the host, evilextractor[.]com, malicious activity increased significantly in March 2023. | [] | EvilExtractor AllinOne Stealer |
FortiGuard Labs observed this malware in a phishing email campaign on 30 March, which we traced back to the samples included in this blog. | [] | EvilExtractor AllinOne Stealer |
It usually pretends to be a legitimate file, such as an Adobe PDF or Dropbox file, but once loaded, it begins to leverage PowerShell malicious activities. | [
"T1036.005"
] | EvilExtractor AllinOne Stealer |
It also contains environment checking and Anti-VM functions. | [] | EvilExtractor AllinOne Stealer |
Its primary purpose seems to be to steal browser data and information from compromised endpoints and then upload it to the attacker’s FTP server. | [] | EvilExtractor AllinOne Stealer |
We recently reviewed a version of the malware that was injected into a victim’s system and, as part of that analysis, identified that most of its victims are located in Europe and America. | [
"T1055"
] | EvilExtractor AllinOne Stealer |
The developer released its project in October 2022 (Figure 1) and has kept updating it to increase its stability and strengthen its module. | [] | EvilExtractor AllinOne Stealer |
This article will examine the initial attack method used to deliver EvilExtractor and its functions. Figure 1. | [] | EvilExtractor AllinOne Stealer |
EvilExtractor for sale on the web Initial Access The phishing email with the malicious attachment is shown in Figure 2. | [
"T1566.001"
] | EvilExtractor AllinOne Stealer |
It is disguised as an account confirmation request. | [] | EvilExtractor AllinOne Stealer |
The attacker also tricks the victim by using an Adobe PDF icon for the decompressed file. | [] | EvilExtractor AllinOne Stealer |
The PE header is shown in Figure 3. | [] | EvilExtractor AllinOne Stealer |
The phishing email Figure 3. | [] | EvilExtractor AllinOne Stealer |
File header of "Account_Info.exe" The execution file is a Python program packaged by PyInstaller. | [
"T1027"
] | EvilExtractor AllinOne Stealer |
We extracted it with pyinstxtractor and found that the “PYARMOR” string in its main code file “contain.pyc”, shown in Figure 4, is an obfuscating tool for Python script that makes the malware harder to be analyzed and detected. | [
"T1027"
] | EvilExtractor AllinOne Stealer |
We extracted the key and iv from _pytransform.dll and decrypted the “contain.pyc” using AES-GCM. Figure 4. | [] | EvilExtractor AllinOne Stealer |
Code in "contain.pyc" | [] | EvilExtractor AllinOne Stealer |
In addition to the Python program, we observed a .NET loader that can extract EvilExtractor. | [] | EvilExtractor AllinOne Stealer |
Figure 5 is part of the code. | [] | EvilExtractor AllinOne Stealer |
It contains Base64-encoded data, which is a PowerShell script. | [
"T1027"
] | EvilExtractor AllinOne Stealer |
This execution file is generated from the tool “PS2EXE-GUI”, which can convert PowerShell scripts to EXE Files. | [] | EvilExtractor AllinOne Stealer |
Figure 5. .Net | [] | EvilExtractor AllinOne Stealer |
Code for EvilExtractor EvilExtractor After decrypting the pyc file, we get the primary code of EvilExtractor. | [
"T1140"
] | EvilExtractor AllinOne Stealer |
It is a PowerShell script that contains the following modules: | [] | EvilExtractor AllinOne Stealer |
Date time checking Anti-Sandbox Anti-VM Anti-Scanner FTP server setting Steal data Upload Stolen data Clear log It first checks whether the system’s date is between 2022-11-09 and 2023-04-12. | [] | EvilExtractor AllinOne Stealer |
If not, it uses the following command to delete the data in PSReadline and terminate: DEL \"$env:APPDATA\Microsoft\Windows\PowerShell\PSReadline\*\" -Force –Recurse It then compares the product model to see if it matches any of the following: VirtualBox, VMWare, Hyper-V, Parallels, Oracle VM VirtualBox, Citrix Hypervis... | [
"T1070.004"
] | EvilExtractor AllinOne Stealer |
It also checks the victim’s hostname against 187 names from VirusTotal machines or other scanner/virtual machines, as shown in Figure 7. Figure 6. | [
"T1082"
] | EvilExtractor AllinOne Stealer |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.