Tool
Hunt pack: Agrius
1,106 vendor-native detections · ready to paste into your SIEM · cross-linked to ATT&CK
Vendor-native detections covering the ATT&CK techniques attributed to Agrius - a ready-to-deploy hunt pack across Splunk, Elastic and Sentinel.
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Detections
50 shown of 1,106Detect Regsvr32 Application Control Bypass
The following analytic identifies the abuse of Regsvr32.exe to proxy execution of malicious code, specifically detecting the loading of "scrobj.dll" by Regsvr32.exe. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process creation events and command-line executions. This activity is significant because Regsvr32.exe is a trusted, signed Microsoft binary, often used in "Squiblydoo" attacks to bypass application control mechanisms. If confirmed malicious, this technique could allow an attacker to execute arbitrary code, potentially leading to system compromise and persistent access.
Show query
| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes
WHERE `process_regsvr32` Processes.process=*scrobj*
BY Processes.action Processes.dest Processes.original_file_name
Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid
Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path
Processes.process Processes.process_exec Processes.process_guid
Processes.process_hash Processes.process_id Processes.process_integrity_level
Processes.process_name Processes.process_path Processes.user
Processes.user_id Processes.vendor_product
| `drop_dm_object_name(Processes)`
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| `detect_regsvr32_application_control_bypass_filter`Detect Remote Access Software Usage DNS
The following analytic detects DNS queries to domains associated with known remote access software such as AnyDesk, GoToMyPC, LogMeIn, and TeamViewer. This detection is crucial as adversaries often use these tools to maintain access and control over compromised environments. Identifying such behavior is vital for a Security Operations Center (SOC) because unauthorized remote access can lead to data breaches, ransomware attacks, and other severe impacts if these threats are not mitigated promptly.
Show query
| tstats `security_content_summariesonly`
count min(_time) as firstTime
max(_time) as lastTime
from datamodel=Network_Resolution where
DNS.query=*
NOT DNS.query IN ("-", "unknown")
by DNS.answer DNS.answer_count DNS.query
DNS.query_count DNS.reply_code_id DNS.src
DNS.vendor_product
| `drop_dm_object_name("DNS")`
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| lookup remote_access_software remote_domain AS query OUTPUT isutility, description as signature,
comment_reference as desc, category
| eval dest = query
| search isutility = True
| `remote_access_software_usage_exceptions`
| `detect_remote_access_software_usage_dns_filter`
Detect Remote Access Software Usage File
The following analytic detects the writing of files from known remote access software to disk within the environment.
It leverages data from Endpoint Detection and Response (EDR) agents, focusing on file path, file name, and user information.
This activity is significant as adversaries often use remote access tools like AnyDesk, GoToMyPC, LogMeIn, and TeamViewer to maintain unauthorized access.
If confirmed malicious, this could allow attackers to persist in the environment, potentially leading to data exfiltration, further compromise, or complete control over affected systems.
It is best to update both the remote_access_software_usage_exception.csv lookup and the remote_access_software lookup with any known or approved remote access software to reduce false positives and increase coverage.
In order to enhance performance, the detection filters for specific file names extensions / names that are used in the remote_access_software lookup.
If add additional entries, consider updating the search filters to include those file names / extensions as well, if not alread covered.
Show query
| tstats `security_content_summariesonly`
count min(_time) as firstTime,
max(_time) as lastTime
values(Filesystem.file_path) as file_path
from datamodel=Endpoint.Filesystem
where Filesystem.file_name IN (
"*.app",
"*.exe",
"*.msi",
"*.pkg",
"*echoware.dll",
"*Idrive.*",
"*rdp2tcp.py"
)
by Filesystem.action Filesystem.dest Filesystem.file_access_time
Filesystem.file_create_time Filesystem.file_hash
Filesystem.file_modify_time Filesystem.file_name
Filesystem.file_path Filesystem.file_acl Filesystem.file_size
Filesystem.process_guid Filesystem.process_id
Filesystem.user Filesystem.vendor_product
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| `drop_dm_object_name(Filesystem)`
| lookup remote_access_software remote_utility AS file_name OUTPUT isutility, description as signature, comment_reference as desc, category
| search isutility = TRUE
| `remote_access_software_usage_exceptions`
| `detect_remote_access_software_usage_file_filter`
Detect Remote Access Software Usage FileInfo
The following analytic detects the execution of processes with file or code signing attributes from known remote access software within the environment. It leverages Sysmon EventCode 1 data and cross-references a lookup table of remote access utilities such as AnyDesk, GoToMyPC, LogMeIn, and TeamViewer. This activity is significant as adversaries often use these tools to maintain unauthorized remote access. If confirmed malicious, this could allow attackers to persist in the environment, potentially leading to data exfiltration or further compromise of the network.
Show query
`sysmon` EventCode=1
| stats count min(_time) as firstTime max(_time) as lastTime, values(Company) as Company values(Product) as Product
BY action dest original_file_name
parent_process parent_process_exec parent_process_guid
parent_process_id parent_process_name parent_process_path
process process_exec process_guid
process_hash process_id process_integrity_level
process_name process_path user
user_id vendor_product
| lookup remote_access_software remote_utility_fileinfo AS Product OUTPUT isutility, description as signature, comment_reference as desc, category
| search isutility = True
| `remote_access_software_usage_exceptions`
| `detect_remote_access_software_usage_fileinfo_filter`Detect Remote Access Software Usage Process
The following analytic detects the execution of known remote access software within the environment. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and parent processes mapped to the Endpoint data model. We then compare with with a list of known remote access software shipped as a lookup file - remote_access_software. This activity is significant as adversaries often use remote access tools like AnyDesk, GoToMyPC, LogMeIn, and TeamViewer to maintain unauthorized access. If confirmed malicious, this could allow attackers to control systems remotely, exfiltrate data, or deploy additional malware, posing a severe threat to the organization's security.
Show query
| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Processes.parent_process) as parent_process from datamodel=Endpoint.Processes where [| inputlookup remote_access_software where isutility=TRUE | rename remote_utility AS Processes.process_name | fields Processes.process_name] AND Processes.dest!="unknown" AND Processes.user!="unknown" by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `drop_dm_object_name(Processes)` | lookup remote_access_software remote_utility AS process_name OUTPUT isutility description AS signature comment_reference AS desc category | search isutility = TRUE | `remote_access_software_usage_exceptions` | `detect_remote_access_software_usage_process_filter`
Detect Remote Access Software Usage Registry
The following analytic detects when a known remote access software is added to common persistence locations on a device within the environment. Adversaries use these utilities to retain remote access capabilities to the environment. Utilities in the lookup include AnyDesk, GoToMyPC, LogMeIn, TeamViewer and much more. Review the lookup for the entire list and add any others.
Show query
| tstats `security_content_summariesonly` latest(Registry.process_guid) as process_guid count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where (Registry.registry_path="*\\Microsoft\\Windows\\CurrentVersion\\Run*" OR (Registry.registry_path="*\\SYSTEM\\CurrentControlSet\\Services\\*" AND Registry.registry_value_name="ImagePath")) by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | rex field=registry_value_data "(\")?.+\\\(?<file_name_1>[^\"=]+\.[^\" ]{1,5})(\")?" | rex field=registry_value_data "(?<file_name_2>[^\.]+\.[^\" ]{1,5}$)" | eval file_name = coalesce(file_name_1,file_name_2) | lookup remote_access_software remote_utility AS file_name OUTPUT isutility, description as signature, comment_reference as desc, category | search isutility = TRUE | `remote_access_software_usage_exceptions` | `detect_remote_access_software_usage_registry_filter`Detect Remote Access Software Usage Traffic
The following analytic detects network traffic associated with known remote access software applications, such as AnyDesk, GoToMyPC, LogMeIn, and TeamViewer.
It leverages Palo Alto traffic logs mapped to the Network_Traffic data model in Splunk. This activity is significant because adversaries often use remote access tools to maintain unauthorized access to compromised environments.
If confirmed malicious, this activity could allow attackers to control systems remotely, exfiltrate data, or deploy additional malware, posing a severe threat to the organization's security.
Show query
| tstats `security_content_summariesonly`
count min(_time) as firstTime
max(_time) as lastTime
values(All_Traffic.dest_port) as dest_port
latest(All_Traffic.user) as user
from datamodel=Network_Traffic where
All_Traffic.app=*
NOT All_Traffic.app IN ("-", "unknown")
by All_Traffic.action All_Traffic.app All_Traffic.bytes All_Traffic.bytes_in
All_Traffic.bytes_out All_Traffic.dest All_Traffic.dest_ip
All_Traffic.dest_port All_Traffic.dvc All_Traffic.protocol
All_Traffic.protocol_version All_Traffic.src All_Traffic.src_ip
All_Traffic.src_port All_Traffic.transport All_Traffic.user
All_Traffic.vendor_product
| `drop_dm_object_name("All_Traffic")`
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| lookup remote_access_software remote_appid AS app OUTPUT isutility, description as signature, comment_reference as desc, category
| search isutility = True
| `remote_access_software_usage_exceptions`
| `detect_remote_access_software_usage_traffic_filter`
Detect Remote Access Software Usage URL
The following analytic detects the execution of known remote access software within the environment.
It leverages network logs mapped to the Web data model, identifying specific URLs and user agents associated with remote access tools like AnyDesk, GoToMyPC, LogMeIn, and TeamViewer.
This activity is significant as adversaries often use these utilities to maintain unauthorized remote access. If confirmed malicious, this could allow attackers to control systems remotely, exfiltrate data, or further compromise the network, posing a severe security risk.
Show query
| tstats `security_content_summariesonly`
count min(_time) as firstTime
max(_time) as lastTime
latest(Web.http_method) as http_method
latest(Web.http_user_agent) as http_user_agent
latest(Web.url) as url
latest(Web.user) as user
latest(Web.dest) as dest
from datamodel=Web where
Web.url_domain=*
NOT Web.url_domain IN (
"-",
"unknown"
)
by Web.action Web.src Web.category
Web.url_domain Web.url_length
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| `drop_dm_object_name("Web")`
| lookup remote_access_software remote_domain AS url_domain OUTPUT isutility, description as signature, comment_reference
as desc, category
| search isutility = True
| `remote_access_software_usage_exceptions`
| `detect_remote_access_software_usage_url_filter`
Detect Rundll32 Inline HTA Execution
The following analytic detects the execution of "rundll32.exe" with inline protocol handlers such as "JavaScript", "VBScript", and "About". This behavior is identified using Endpoint Detection and Response (EDR) telemetry, focusing on command-line arguments. This activity is significant as it is often associated with fileless malware or application whitelisting bypass techniques. If confirmed malicious, this could allow an attacker to execute arbitrary code, bypass security controls, and maintain persistence within the environment.
Show query
| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes
WHERE `process_rundll32` (Processes.process=*vbscript*
OR
Processes.process=*javascript*)
BY Processes.action Processes.dest Processes.original_file_name
Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid
Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path
Processes.process Processes.process_exec Processes.process_guid
Processes.process_hash Processes.process_id Processes.process_integrity_level
Processes.process_name Processes.process_path Processes.user
Processes.user_id Processes.vendor_product
| `drop_dm_object_name(Processes)`
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| `detect_rundll32_inline_hta_execution_filter`Detect SNICat SNI Exfiltration
The following analytic identifies the use of SNICat tool commands within the TLS SNI field, indicating potential data exfiltration attempts. It leverages Zeek SSL data to detect specific SNICat commands such as LIST, LS, SIZE, LD, CB, EX, ALIVE, EXIT, WHERE, and finito in the server_name field. This activity is significant as SNICat is a known tool for covert data exfiltration using TLS. If confirmed malicious, this could allow attackers to exfiltrate sensitive data undetected, posing a severe threat to data confidentiality and integrity.
Show query
`zeek_ssl`
| rex field=server_name "(?<snicat>(LIST|LS|SIZE|LD|CB|CD|EX|ALIVE|EXIT|WHERE|finito)-[A-Za-z0-9]{16}\.)"
| stats count BY src_ip dest_ip server_name snicat
| where count>0
| table src_ip dest_ip server_name snicat
| `detect_snicat_sni_exfiltration_filter`Detect SharpHound Command-Line Arguments
The following analytic detects the execution of SharpHound command-line arguments, specifically `-collectionMethod` and `invoke-bloodhound`. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as SharpHound is commonly used for Active Directory enumeration, which can be a precursor to lateral movement or privilege escalation. If confirmed malicious, this activity could allow an attacker to map out the network, identify high-value targets, and plan further attacks, potentially compromising sensitive information and critical systems.
Show query
| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes
WHERE Processes.process IN ("*-collectionMethod*","*invoke-bloodhound*")
BY Processes.action Processes.dest Processes.original_file_name
Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid
Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path
Processes.process Processes.process_exec Processes.process_guid
Processes.process_hash Processes.process_id Processes.process_integrity_level
Processes.process_name Processes.process_path Processes.user
Processes.user_id Processes.vendor_product
| `drop_dm_object_name(Processes)`
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| `detect_sharphound_command_line_arguments_filter`Detect SharpHound File Modifications
The following analytic detects the creation of files typically associated with SharpHound, a reconnaissance tool used for gathering domain and trust data. It leverages file modification events from the Endpoint.Filesystem data model, focusing on default file naming patterns like `*_BloodHound.zip` and various JSON files. This activity is significant as it indicates potential domain enumeration, which is a precursor to more targeted attacks. If confirmed malicious, an attacker could gain detailed insights into the domain structure, facilitating lateral movement and privilege escalation.
Show query
| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem
WHERE Filesystem.file_name IN ("*bloodhound.zip", "*_computers.json", "*_gpos.json", "*_domains.json", "*_users.json", "*_groups.json", "*_ous.json", "*_containers.json")
BY Filesystem.action Filesystem.dest Filesystem.file_access_time
Filesystem.file_create_time Filesystem.file_hash Filesystem.file_modify_time
Filesystem.file_name Filesystem.file_path Filesystem.file_acl
Filesystem.file_size Filesystem.process_guid Filesystem.process_id
Filesystem.user Filesystem.vendor_product
| `drop_dm_object_name(Filesystem)`
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| `detect_sharphound_file_modifications_filter`Detect SharpHound Usage
The following analytic detects the usage of the SharpHound binary by identifying its original filename, `SharpHound.exe`, and the process name. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process metadata and command-line executions. SharpHound is a tool used for Active Directory enumeration, often by attackers during the reconnaissance phase. If confirmed malicious, this activity could allow an attacker to map out the network, identify high-value targets, and plan further attacks, potentially leading to privilege escalation and lateral movement within the environment.
Show query
| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes
WHERE (
Processes.process_name=sharphound.exe
OR
Processes.original_file_name=SharpHound.exe
)
BY Processes.action Processes.dest Processes.original_file_name
Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid
Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path
Processes.process Processes.process_exec Processes.process_guid
Processes.process_hash Processes.process_id Processes.process_integrity_level
Processes.process_name Processes.process_path Processes.user
Processes.user_id Processes.vendor_product
| `drop_dm_object_name(Processes)`
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| `detect_sharphound_usage_filter`Detect Use of cmd exe to Launch Script Interpreters
The following detects the execution of cscript.exe or wscript.exe processes spawned by cmd.exe, leveraging Endpoint Detection and Response (EDR) telemetry mapped to the Endpoint data model, with additional contextual filtering to improve fidelity and reduce false positives.
It focuses on executions originating from user-writable directories such as Users, AppData, Temp, and Downloads, which are commonly abused by attackers to stage and execute malicious scripts, while excluding trusted system paths like C:\Windows\System32\ and C:\Program Files\ that are typically associated with legitimate activity.
The detection also filters out service accounts (e.g., accounts ending with $ or known naming conventions) to minimize noise from automated processes and incorporates command-line context to better assess script execution patterns and identify potentially suspicious behavior.
Show query
| tstats `security_content_summariesonly`
count min(_time) as firstTime
max(_time) as lastTime
FROM datamodel=Endpoint.Processes WHERE
Processes.parent_process_name="cmd.exe"
(
Processes.process_name IN ("cscript.exe", "wscript.exe")
OR
Processes.original_file_name IN ("cscript.exe", "wscript.exe")
)
NOT Processes.process IN (
"* \"C:\\Program Files (x86)\\*",
"* \"C:\\Program Files\\*",
"* \"C:\\Windows\\System32\\*",
"* \"C:\\Windows\\SysWOW64\\*",
"* C:\\Program Files (x86)\\*",
"* C:\\Program Files\\*",
"* C:\\Windows\\System32\\*",
"* C:\\Windows\\SysWOW64\\*"
)
NOT Processes.user="*$"
BY Processes.action Processes.dest Processes.original_file_name
Processes.parent_process Processes.parent_process_exec
Processes.parent_process_guid Processes.parent_process_id
Processes.parent_process_name Processes.parent_process_path
Processes.process Processes.process_exec Processes.process_guid
Processes.process_hash Processes.process_id Processes.process_integrity_level
Processes.process_name Processes.process_path Processes.user
Processes.user_id Processes.vendor_product
| `drop_dm_object_name("Processes")`
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| `detect_use_of_cmd_exe_to_launch_script_interpreters_filter`Detect Web Access to Decommissioned S3 Bucket
This detection identifies web requests to domains that match previously decommissioned S3 buckets through web proxy logs. This activity is significant because attackers may attempt to access or recreate deleted S3 buckets that were previously public to hijack them for malicious purposes. If successful, this could allow attackers to host malicious content or exfiltrate data through compromised bucket names that may still be referenced by legitimate applications.
Show query
| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(Web.http_method) as http_method values(Web.http_user_agent) as http_user_agent values(Web.url) as url values(Web.user) as user FROM datamodel=Web
WHERE Web.url_domain!=""
BY Web.src Web.url_domain
| `drop_dm_object_name("Web")`
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| eval bucket_domain = lower(url_domain)
| lookup decommissioned_buckets bucketName as bucket_domain OUTPUT bucketName as match
| where isnotnull(match)
| `detect_web_access_to_decommissioned_s3_bucket_filter`Detect Zerologon via Zeek
The following analytic detects attempts to exploit the Zerologon CVE-2020-1472 vulnerability via Zeek RPC. It leverages Zeek DCE-RPC data to identify specific operations: NetrServerPasswordSet2, NetrServerReqChallenge, and NetrServerAuthenticate3. This activity is significant because it indicates an attempt to gain unauthorized access to a domain controller, potentially leading to a complete takeover of an organization's IT infrastructure. If confirmed malicious, the impact could be severe, including data theft, ransomware deployment, or other devastating outcomes. Immediate investigation of the identified IP addresses and RPC operations is crucial.
Show query
`zeek_rpc` operation IN (NetrServerPasswordSet2,NetrServerReqChallenge,NetrServerAuthenticate3)
| bin span=5m _time
| stats values(operation) dc(operation) as opscount count(eval(operation=="NetrServerReqChallenge")) as challenge count(eval(operation=="NetrServerAuthenticate3")) as authcount count(eval(operation=="NetrServerPasswordSet2")) as passcount count as totalcount
BY _time,src_ip,dest_ip
| search opscount=3 authcount>4 passcount>0
| search `detect_zerologon_via_zeek_filter`Detect mshta inline hta execution
The following analytic detects the execution of "mshta.exe" with inline protocol handlers such as "JavaScript", "VBScript", and "About". It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line arguments and process details. This activity is significant because mshta.exe can be exploited to execute malicious scripts, potentially leading to unauthorized code execution. If confirmed malicious, this could allow an attacker to execute arbitrary code, escalate privileges, or establish persistence within the environment, posing a severe security risk.
Show query
| tstats `security_content_summariesonly` count values(Processes.process)
as process values(Processes.parent_process) as parent_process min(_time) as firstTime
max(_time) as lastTime from datamodel=Endpoint.Processes where
`process_mshta`
Processes.process IN ("*vbscript*", "*javascript*", "*about*")
by Processes.action Processes.dest Processes.original_file_name Processes.parent_process
Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id
Processes.parent_process_name Processes.parent_process_path Processes.process
Processes.process_exec Processes.process_guid Processes.process_hash
Processes.process_id Processes.process_integrity_level Processes.process_name
Processes.process_path Processes.user Processes.user_id Processes.vendor_product
| `drop_dm_object_name(Processes)`
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| `detect_mshta_inline_hta_execution_filter`
Detect mshta renamed
The following analytic identifies instances where mshta.exe has been renamed and executed. It leverages Endpoint Detection and Response (EDR) data, specifically focusing on the original file name field to detect discrepancies. This activity is significant because renaming mshta.exe is a common tactic used by attackers to evade detection and execute malicious scripts. If confirmed malicious, this could allow an attacker to execute arbitrary code, potentially leading to system compromise, data exfiltration, or further lateral movement within the network.
Show query
| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes
WHERE Processes.process_name!=mshta.exe
AND
Processes.original_file_name=MSHTA.EXE
BY Processes.action Processes.dest Processes.original_file_name
Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid
Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path
Processes.process Processes.process_exec Processes.process_guid
Processes.process_hash Processes.process_id Processes.process_integrity_level
Processes.process_name Processes.process_path Processes.user
Processes.user_id Processes.vendor_product
| `drop_dm_object_name(Processes)`
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| `detect_mshta_renamed_filter`Disable Security Logs Using MiniNt Registry
The following analytic detects a suspicious registry modification aimed at disabling security audit logs by adding a specific registry entry. It leverages data from the Endpoint.Registry data model, focusing on changes to the "Control\\MiniNt" registry path. This activity is significant because it can prevent Windows from logging any events to the Security Log, effectively blinding security monitoring efforts. If confirmed malicious, this technique could allow an attacker to operate undetected, making it difficult to trace their actions and compromising the integrity of security audits.
Show query
| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path="*\\Control\\MiniNt\\*") by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `disable_security_logs_using_minint_registry_filter`
Domain Account Discovery with Dsquery
The following analytic identifies the execution of `dsquery.exe` with command-line arguments used to discover domain users. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as it indicates potential reconnaissance efforts by adversaries to map out domain users, which is a common precursor to further attacks. If confirmed malicious, this behavior could allow attackers to gain insights into user accounts, facilitating subsequent actions like privilege escalation or lateral movement within the network.
Show query
| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes
WHERE Processes.process_name="dsquery.exe"
AND
Processes.process = "*user*"
BY Processes.action Processes.dest Processes.original_file_name
Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid
Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path
Processes.process Processes.process_exec Processes.process_guid
Processes.process_hash Processes.process_id Processes.process_integrity_level
Processes.process_name Processes.process_path Processes.user
Processes.user_id Processes.vendor_product
| `drop_dm_object_name(Processes)`
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| `domain_account_discovery_with_dsquery_filter`Domain Account Discovery with Wmic
The following analytic detects the execution of `wmic.exe` with command-line arguments used to query for domain users. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on specific command-line patterns indicative of domain account discovery. This activity is significant as it often precedes lateral movement or privilege escalation attempts by adversaries. If confirmed malicious, this behavior could allow attackers to map out user accounts within the domain, facilitating further attacks and potentially compromising sensitive information.
Show query
| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name="wmic.exe" AND Processes.process = "*/NAMESPACE:\\\\root\\directory\\ldap*" AND Processes.process = "*ds_user*" AND Processes.process = "*GET*" AND Processes.process = "*ds_samaccountname*" by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `domain_account_discovery_with_wmic_filter`
Domain Controller Discovery with Nltest
The following analytic detects the execution of `nltest.exe` with command-line arguments `/dclist:` or `/dsgetdc:` to discover domain controllers. It leverages Endpoint Detection and Response (EDR) data, focusing on process names and command-line arguments. This activity is significant because both Red Teams and adversaries use `nltest.exe` for situational awareness and Active Directory discovery. If confirmed malicious, this behavior could allow attackers to map out domain controllers, facilitating further attacks such as privilege escalation or lateral movement within the network.
Show query
| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes
WHERE (
Processes.process_name=nltest.exe
OR
Processes.original_file_name=nltestrk.exe
)
(Processes.process="*/dclist:*" OR Processes.process="*/dsgetdc:*")
BY Processes.action Processes.dest Processes.original_file_name
Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid
Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path
Processes.process Processes.process_exec Processes.process_guid
Processes.process_hash Processes.process_id Processes.process_integrity_level
Processes.process_name Processes.process_path Processes.user
Processes.user_id Processes.vendor_product
| `drop_dm_object_name(Processes)`
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| `domain_controller_discovery_with_nltest_filter`Domain Controller Discovery with Wmic
The following analytic identifies the execution of `wmic.exe` with command-line arguments used to discover domain controllers in a Windows domain. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments. This activity is significant because it is commonly used by adversaries and Red Teams for situational awareness and Active Directory discovery. If confirmed malicious, this behavior could allow attackers to map out the network, identify key systems, and plan further attacks, potentially leading to unauthorized access and data exfiltration.
Show query
| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes
WHERE (
Processes.process_name="wmic.exe"
)
(Processes.process="" OR Processes.process="*DomainControllerAddress*")
BY Processes.action Processes.dest Processes.original_file_name
Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid
Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path
Processes.process Processes.process_exec Processes.process_guid
Processes.process_hash Processes.process_id Processes.process_integrity_level
Processes.process_name Processes.process_path Processes.user
Processes.user_id Processes.vendor_product
| `drop_dm_object_name(Processes)`
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| `domain_controller_discovery_with_wmic_filter`Domain Group Discovery With Dsquery
The following analytic identifies the execution of `dsquery.exe` with command-line arguments used to query for domain groups. It leverages Endpoint Detection and Response (EDR) data, focusing on process names and command-line arguments. This activity is significant because both Red Teams and adversaries use `dsquery.exe` to enumerate domain groups, gaining situational awareness and facilitating further Active Directory discovery. If confirmed malicious, this behavior could allow attackers to map out the domain structure, identify high-value targets, and plan subsequent attacks, potentially leading to privilege escalation or data exfiltration.
Show query
| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes
WHERE (
Processes.process_name="dsquery.exe"
)
(Processes.process="*group*")
BY Processes.action Processes.dest Processes.original_file_name
Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid
Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path
Processes.process Processes.process_exec Processes.process_guid
Processes.process_hash Processes.process_id Processes.process_integrity_level
Processes.process_name Processes.process_path Processes.user
Processes.user_id Processes.vendor_product
| `drop_dm_object_name(Processes)`
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| `domain_group_discovery_with_dsquery_filter`Domain Group Discovery With Wmic
The following analytic identifies the execution of `wmic.exe` with command-line arguments used to query for domain groups. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as it indicates potential reconnaissance efforts by adversaries to gain situational awareness and map out Active Directory structures. If confirmed malicious, this behavior could allow attackers to identify and target specific domain groups, potentially leading to privilege escalation or lateral movement within the network.
Show query
| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where `process_wmic` (Processes.process=*/NAMESPACE:\\\\root\\directory\\ldap* AND Processes.process=*ds_group* AND Processes.process="*GET ds_samaccountname*") by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `domain_group_discovery_with_wmic_filter`
Domain Group Discovery with Adsisearcher
The following analytic detects the use of the `[Adsisearcher]` type accelerator in PowerShell to query Active Directory for domain groups. It leverages PowerShell Script Block Logging (EventCode=4104) to identify specific script blocks containing `[adsisearcher]` and group-related queries. This activity is significant as it may indicate an attempt by adversaries or Red Teams to enumerate domain groups for situational awareness and Active Directory discovery. If confirmed malicious, this behavior could lead to further reconnaissance, privilege escalation, or lateral movement within the network.
Show query
`powershell` (ScriptBlockText = "*[adsisearcher]*" AND ScriptBlockText = "*(objectcategory=group)*" AND ScriptBlockText = "*findAll()*")
| fillnull
| stats count min(_time) as firstTime max(_time) as lastTime
BY dest signature signature_id
user_id vendor_product EventID
Guid Opcode Name
Path ProcessID ScriptBlockId
ScriptBlockText
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| `domain_group_discovery_with_adsisearcher_filter`Dump LSASS via comsvcs DLL
The following analytic detects the behavior of dumping credentials from memory by exploiting the Local Security Authority Subsystem Service (LSASS) using the comsvcs.dll and MiniDump via rundll32. This detection leverages process information from Endpoint Detection and Response (EDR) logs, focusing on specific command-line executions. This activity is significant because it indicates potential credential theft, which can lead to broader system compromise, persistence, lateral movement, and privilege escalation. If confirmed malicious, attackers could gain unauthorized access to sensitive information, leading to data theft, ransomware attacks, or other damaging outcomes.
Show query
| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes
WHERE `process_rundll32` Processes.process=*comsvcs.dll* Processes.process IN ("*MiniDump*", "*#24*")
BY Processes.action Processes.dest Processes.original_file_name
Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid
Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path
Processes.process Processes.process_exec Processes.process_guid
Processes.process_hash Processes.process_id Processes.process_integrity_level
Processes.process_name Processes.process_path Processes.user
Processes.user_id Processes.vendor_product
| `drop_dm_object_name(Processes)`
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| `dump_lsass_via_comsvcs_dll_filter`Dump LSASS via procdump
The following analytic detects the use of procdump.exe to dump the LSASS
process, specifically looking for the -mm and -ma command-line arguments. It leverages
data from Endpoint Detection and Response (EDR) agents, focusing on process names,
command-line executions, and parent processes. This activity is significant because
dumping LSASS can expose sensitive credentials, posing a severe security risk. If
confirmed malicious, an attacker could obtain credentials, escalate privileges,
and move laterally within the network, leading to potential data breaches and further
compromise of the environment.
Show query
| tstats `security_content_summariesonly`
count min(_time) as firstTime
max(_time) as lastTime
from datamodel=Endpoint.Processes where
(
Processes.process_name IN (
"procdump.exe",
"procdump64.exe",
"procdump64a.exe"
)
OR
Processes.original_file_name=procdump
)
Processes.process IN (
"*-ma*",
"*-mm*",
"*-mp*",
"*/ma*",
"*/mm*",
"*/mp*"
)
Processes.process IN (
"* keyiso*",
"* ls*",
"* samss*"
)
by Processes.action Processes.dest Processes.original_file_name Processes.parent_process
Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id
Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec
Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level
Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product
| `drop_dm_object_name(Processes)`
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| `dump_lsass_via_procdump_filter`
ESXi Account Modified
This detection identifies the creation, deletion, or modification of a local user account on an ESXi host. This activity may indicate unauthorized access, indicator removal, or persistence attempts by an attacker seeking to establish or maintain control of the host.
Show query
`esxi_syslog` Message="*esxcli system account*" Message IN ("*-i *","*--id*") NOT Message="*[shell*" | rex field=_raw "Z (?<dest>[\w\.]+)\s.*: \[(?<initial_user>\w+)]:\s.+-i[d]*\s(?<modified_user>[\w_\-0-9]+)" | stats min(_time) as firstTime max(_time) as lastTime count by dest initial_user modified_user | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `esxi_account_modified_filter`ESXi External Root Login Activity
This detection identifies instances where the ESXi UI is accessed using the root account instead of a delegated administrative user. Direct root access to the UI bypasses role-based access controls and auditing practices, and may indicate risky behavior, misconfiguration, or unauthorized activity by a malicious actor using compromised credentials.
Show query
`esxi_syslog` Message="*root*" AND Message="*logged in*" | rex field=_raw "root@(?<SrcIpAddr>\d{1,3}(?:\.\d{1,3}){3})" | rex field=_raw "Z (?<dest>[\w\.]+)\s" | search SrcIpAddr != "127.0.0.1" AND SrcIpAddr != 192.168.0.0/16 AND SrcIpAddr != 172.16.0.0/12 AND SrcIpAddr != 10.0.0.0/8 | stats min(_time) as firstTime max(_time) as lastTime count by dest SrcIpAddr | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `esxi_external_root_login_activity_filter`ESXi Reverse Shell Patterns
This detection looks for reverse shell string patterns on an ESXi host, which may indicate that a threat actor is attempting to establish remote control over the system.
Show query
`esxi_syslog` Message IN ("*bash -i >&*","*/dev/tcp/*","*/dev/udp/*", "*/socat exec:*","*socket(S,PF_INET*") OR (Message="*python -c*" AND Message="*import socket*") | rex field=_raw "Z (?<dest>[\w\.]+)\s" | stats min(_time) as firstTime max(_time) as lastTime count by dest Message | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `esxi_reverse_shell_patterns_filter`ESXi SSH Enabled
This detection identifies SSH being enabled on ESXi hosts, which can be an early indicator of malicious activity. Threat actors often use SSH to gain persistent remote access after compromising credentials or exploiting vulnerabilities.
Show query
`esxi_syslog` Message="*SSH access has been enabled" | rex field=_raw "Z (?<dest>[\w\.]+)\s" | stats min(_time) as firstTime max(_time) as lastTime count by dest Message | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `esxi_ssh_enabled_filter`
ESXi Sensitive Files Accessed
This detection identifies access to sensitive system and configuration files on an ESXi host, including authentication data, service configurations, and VMware-specific management settings. Interaction with these files may indicate adversary reconnaissance, credential harvesting, or preparation for privilege escalation, lateral movement, or persistence.
Show query
`esxi_syslog` Message="*shell[*" Message IN ("*/etc/shadow*","*/etc/vmware/hostd/hostd.xml*", "*/etc/vmware/vpxa/vpxa.cfg*","*/etc/sfcb/sfcb.cfg*","*/etc/security/*", "*/etc/likewise/krb5-affinity.conf*","*/etc/vmware-vpx/vcdb.properties*") | rex field=_raw "\]: \[(?<user>\w+)\]:(?<command>.+)" | rex field=_raw "Z (?<dest>[\w\.]+)\s" | stats min(_time) as firstTime max(_time) as lastTime count by dest user command | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `esxi_sensitive_files_accessed_filter`ESXi Shared or Stolen Root Account
This detection monitors for signs of a shared or potentially compromised root account on ESXi hosts by tracking the number of unique IP addresses logging in as root within a short time window. Multiple logins from different IPs in a brief period may indicate credential misuse, lateral movement, or account compromise.
Show query
`esxi_syslog` Message="*root*" Message="*logged in*" NOT Message="*[email protected]*" | rex field=_raw "root@(?<SrcIpAddr>\d{1,3}(?:\.\d{1,3}){3})" | rex field=_raw "Z (?<dest>[\w\.]+)\s" | bin _time span=15m | stats min(_time) as firstTime max(_time) as lastTime dc(SrcIpAddr) AS distinct_ip_count values(SrcIpAddr) AS SrcIps by dest | where distinct_ip_count > 1 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `esxi_shared_or_stolen_root_account_filter`
ESXi Shell Access Enabled
This detection identifies when the ESXi Shell is enabled on a host, which may indicate that a malicious actor is preparing to execute commands locally or establish persistent access. Enabling the shell outside of approved maintenance windows can be a sign of compromise or unauthorized administrative activity.
Show query
`esxi_syslog` Message="*ESXi Shell*" Message="*has been enabled*" | rex field=_raw "'(?<user>\w+)@" | rex field=_raw "Z (?<dest>[\w\.]+)\s" | stats min(_time) as firstTime max(_time) as lastTime count by dest user Message | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `esxi_shell_access_enabled_filter`
ESXi System Clock Manipulation
This detection identifies a significant change to the system clock on an ESXi host, which may indicate an attempt to manipulate timestamps and evade detection or forensic analysis
Show query
`esxi_syslog` Message="*NTPClock*" AND Message="*system clock stepped*" | rex field=_raw "stepped to (?<epoch_time>\d+\.\d+),.+delta\s(?<delta>\d+)\s" | rex field=_raw "Z (?<dest>[\w\.]+)\s" | eval epoch_time=tonumber(epoch_time) | eval delta=tonumber(delta) | eval event_time=round(_time, 0) | eval direction=if(epoch_time < event_time, "backward", "forward") | eval original_time=if(direction=="backward", epoch_time + delta, epoch_time - delta) | eval stepped_to_str=strftime(epoch_time, "%Y-%m-%d %H:%M:%S") | eval original_time_str=strftime(original_time, "%Y-%m-%d %H:%M:%S") | stats min(_time) as firstTime max(_time) as lastTime count by dest direction original_time_str stepped_to_str epoch_time delta | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `esxi_system_clock_manipulation_filter`
ESXi System Information Discovery
This detection identifies the use of ESXCLI system-level commands that retrieve configuration details. While used for legitimate administration, this behavior may also indicate adversary reconnaissance aimed at profiling the ESXi host's capabilities, build information, or system role in preparation for further compromise.
Show query
`esxi_syslog` Message="*system*" AND Message="*esxcli*" AND Message IN ("*get*","*list*") AND Message="*user=*" NOT Message="*filesystem*" | rex field=_raw "user=(?<user>\w+)\]\s+Dispatch\s+(?<command>[^\s]+)" | rex field=_raw "Z (?<dest>[\w\.]+)\s" | stats min(_time) as firstTime max(_time) as lastTime count by dest user command | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `esxi_system_information_discovery_filter`ESXi VM Exported via Remote Tool
This detection identifies the use of a remote tool to download virtual machine disk files from a datastore. The NFC protocol is used by management tools to transfer files to and from ESXi hosts, but it can also be abused by attackers or insiders to exfiltrate full virtual disk images
Show query
`esxi_syslog` Message="*File download from path*" Message="*was initiated from*" | rex field=_raw "from path '\[(?<Datastore>[^\]]+)\](?<VMPath>[^']+)'" | rex field=_raw "initiated from '(?<InitiatorTool>[^/]+)/(?<ToolVersion>[^@]+)@(?<InitiatorIP>\d{1,3}(?:\.\d{1,3}){3})'" | rex field=_raw "Z (?<dest>[\w\.]+)\s" | stats min(_time) as firstTime max(_time) as lastTime count by Datastore VMPath InitiatorTool ToolVersion InitiatorIP dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `esxi_vm_exported_via_remote_tool_filter`Elevated Group Discovery With Wmic
The following analytic detects the execution of `wmic.exe` with command-line arguments querying specific elevated domain groups. It leverages Endpoint Detection and Response (EDR) telemetry to identify processes that access the LDAP namespace and search for groups like "Domain Admins" or "Enterprise Admins." This activity is significant as it indicates potential reconnaissance efforts by adversaries to identify high-privilege accounts within Active Directory. If confirmed malicious, this behavior could lead to privilege escalation, allowing attackers to gain elevated access and control over critical network resources.
Show query
| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where (Processes.process_name="wmic.exe") (Processes.process=*/NAMESPACE:\\\\root\\directory\\ldap*) (Processes.process="*Domain Admins*" OR Processes.process="*Enterprise Admins*" OR Processes.process="*Schema Admins*" OR Processes.process="*Account Operators*" OR Processes.process="*Server Operators*" OR Processes.process="*Protected Users*" OR Processes.process="*Dns Admins*") by Processes.action Processes.dest Processes.original_file_name Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path Processes.process Processes.process_exec Processes.process_guid Processes.process_hash Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path Processes.user Processes.user_id Processes.vendor_product | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `elevated_group_discovery_with_wmic_filter`
Elevated Group Discovery with PowerView
The following analytic detects the execution of the `Get-DomainGroupMember` cmdlet from PowerView, identified through PowerShell Script Block Logging (EventCode=4104). This cmdlet is used to enumerate members of elevated domain groups such as Domain Admins and Enterprise Admins. Monitoring this activity is crucial as it indicates potential reconnaissance efforts by adversaries to identify high-privileged users within the domain. If confirmed malicious, this activity could lead to targeted attacks on privileged accounts, facilitating further compromise and lateral movement within the network.
Show query
`powershell` EventCode=4104 (ScriptBlockText = "*Get-DomainGroupMember*") AND ScriptBlockText IN ("*Domain Admins*","*Enterprise Admins*", "*Schema Admins*", "*Account Operators*" , "*Server Operators*", "*Protected Users*", "*Dns Admins*")
| fillnull
| stats count min(_time) as firstTime max(_time) as lastTime
BY dest signature signature_id
user_id vendor_product EventID
Guid Opcode Name
Path ProcessID ScriptBlockId
ScriptBlockText
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| `elevated_group_discovery_with_powerview_filter`Enable RDP In Other Port Number
The following analytic detects modifications to the registry that enable RDP on a machine using a non-default port number. It leverages data from the Endpoint.Registry data model, specifically monitoring changes to the registry path "HKLM\SYSTEM\CurrentControlSet\Control\Terminal Server\WinStations\RDP-Tcp" and the "PortNumber" value. This activity is significant as attackers often modify RDP settings to facilitate lateral movement and maintain remote access to compromised systems. If confirmed malicious, this could allow attackers to bypass network defenses, gain persistent access, and potentially control the compromised machine.
Show query
| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry WHERE (Registry.registry_path="*\\CurrentControlSet\\Control\\Terminal Server\\WinStations\\RDP-Tcp*" Registry.registry_value_name = "PortNumber") by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | where isnotnull(registry_value_data) | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `enable_rdp_in_other_port_number_filter`
Enumerate Users Local Group Using Telegram
The following analytic detects a Telegram process enumerating all network users in a local group. It leverages EventCode 4798, which is generated when a process enumerates a user's security-enabled local groups on a computer or device. This activity is significant as it may indicate an attempt to gather information on user accounts, a common precursor to further malicious actions. If confirmed malicious, this behavior could allow an attacker to map out user accounts, potentially leading to privilege escalation or lateral movement within the network.
Show query
`wineventlog_security` EventCode=4798 CallerProcessName = "*\\telegram.exe" | stats count min(_time) as firstTime max(_time) as lastTime by user Computer EventCode CallerProcessName ProcessID SubjectUserSid SubjectDomainName SubjectLogonId | rename Computer as dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `enumerate_users_local_group_using_telegram_filter`
Esentutl SAM Copy
The following analytic detects the use of `esentutl.exe` to access credentials stored in the ntds.dit or SAM file. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs that include command-line details. This activity is significant because it may indicate an attempt to extract sensitive credential information, which is a common tactic in lateral movement and privilege escalation. If confirmed malicious, this could allow an attacker to gain unauthorized access to user credentials, potentially compromising the entire network.
Show query
| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time)
as lastTime from datamodel=Endpoint.Processes where
(Processes.process_name=esentutl.exe OR Processes.original_file_name=esentutl.exe)
Processes.process IN ("*ntds*", "*SAM*")
by Processes.action Processes.dest Processes.original_file_name
Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid
Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path
Processes.process Processes.process_exec Processes.process_guid Processes.process_hash
Processes.process_id Processes.process_integrity_level Processes.process_name Processes.process_path
Processes.user Processes.user_id Processes.vendor_product
| `drop_dm_object_name(Processes)`
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| `esentutl_sam_copy_filter`
Excessive DNS Failures
The following analytic identifies excessive DNS query failures by counting DNS responses that do not indicate success, triggering when there are more than 50 occurrences. It leverages the Network_Resolution data model, focusing on DNS reply codes that signify errors. This activity is significant because a high number of DNS failures can indicate potential network misconfigurations, DNS poisoning attempts, or malware communication issues. If confirmed malicious, this activity could lead to disrupted network services, hindered communication, or data exfiltration attempts by attackers.
Show query
| tstats `security_content_summariesonly` count FROM datamodel=Network_Resolution
WHERE nodename=DNS "DNS.reply_code"!="No Error" "DNS.reply_code"!="NoError" DNS.reply_code!="unknown" NOT "DNS.query"="*.arpa" "DNS.query"="*.*"
BY "DNS.src" "DNS.query" "DNS.reply_code"
| `drop_dm_object_name("DNS")`
| lookup cim_corporate_web_domain_lookup domain as query OUTPUT domain
| where isnull(domain)
| lookup update=true alexa_lookup_by_str domain as query OUTPUT rank
| where isnull(rank)
| eventstats max(count) as mc
BY src reply_code
| eval mode_query=if(count=mc, query, null())
| stats sum(count) as count values(mode_query) as query values(mc) as max_query_count
BY src reply_code
| where count>50
| `get_asset(src)`
| `excessive_dns_failures_filter`Excessive File Deletion In WinDefender Folder
The following analytic detects excessive file deletion events in the Windows Defender folder. It leverages Sysmon EventCodes 23 and 26 to identify processes deleting multiple files within this directory. This behavior is significant as it may indicate an attempt to corrupt or disable Windows Defender, a key security component. If confirmed malicious, this activity could allow an attacker to disable endpoint protection, facilitating further malicious actions without detection.
Show query
`sysmon` EventCode IN ("23","26") TargetFilename = "*\\ProgramData\\Microsoft\\Windows Defender\\*" | stats count min(_time) as firstTime, max(_time) as lastTime values(file_path) as file_path values(file_hash) as file_hash values(file_name) as file_name values(file_modify_time) as file_modify_time values(process_name) as process_name values(process_path) as process_path values(process_guid) as process_guid values(process_id) as process_id values(process_exec) as process_exec by action dest dvc signature signature_id user user_id vendor_product | where count >=50 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `excessive_file_deletion_in_windefender_folder_filter`Excessive distinct processes from Windows Temp
The following analytic identifies an excessive number of distinct processes executing from the Windows\Temp directory. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process paths and counts within a 20-minute window. This behavior is significant as it often indicates the presence of post-exploit frameworks like Koadic and Meterpreter, which use this technique to execute malicious actions. If confirmed malicious, this activity could allow attackers to execute arbitrary code, escalate privileges, and maintain persistence within the environment, posing a severe threat to system integrity and security.
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| tstats `security_content_summariesonly` distinct_count(Processes.process) as distinct_process_count min(_time) as firstTime max(_time) as lastTime values(Processes.action) as action values(Processes.original_file_name) as original_file_name values(Processes.parent_process) as parent_process values(Processes.parent_process_exec) as parent_process_exec values(Processes.parent_process_guid) as parent_process_guid values(Processes.parent_process_id) as parent_process_id values(Processes.parent_process_name) as parent_process_name values(Processes.parent_process_path) as parent_process_path values(Processes.process) as process values(Processes.process_exec) as process_exec values(Processes.process_guid) as process_guid values(Processes.process_hash) as process_hash values(Processes.process_id) as process_id values(Processes.process_integrity_level) as process_integrity_level values(Processes.process_name) as process_name values(Processes.process_path) as process_path values(Processes.user_id) as user_id values(Processes.vendor_product) as vendor_product from datamodel=Endpoint.Processes where Processes.process_path = "*\\Windows\\Temp\\*" by Processes.dest Processes.user _time span=20m | where distinct_process_count > 37 | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `excessive_distinct_processes_from_windows_temp_filter`
Excessive number of taskhost processes
The following analytic identifies an excessive number of taskhost.exe and taskhostex.exe processes running within a short time frame. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and their counts. This behavior is significant as it is commonly associated with post-exploitation tools like Meterpreter and Koadic, which use multiple instances of these processes for actions such as discovery and lateral movement. If confirmed malicious, this activity could indicate an ongoing attack, allowing attackers to execute code, escalate privileges, or move laterally within the network.
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| tstats `security_content_summariesonly` values(Processes.action) as action values(Processes.original_file_name) as original_file_name values(Processes.parent_process) as parent_process values(Processes.parent_process_exec) as parent_process_exec values(Processes.parent_process_guid) as parent_process_guid values(Processes.parent_process_id) as parent_process_id values(Processes.parent_process_name) as parent_process_name values(Processes.parent_process_path) as parent_process_path values(Processes.process) as process values(Processes.process_exec) as process_exec values(Processes.process_guid) as process_guid values(Processes.process_hash) as process_hash values(Processes.process_id) as process_id values(Processes.process_integrity_level) as process_integrity_level values(Processes.user) as user values(Processes.process_path) as process_path values(Processes.user_id) as user_id values(Processes.vendor_product) as vendor_product min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes
WHERE Processes.process_name = "taskhost.exe"
OR
Processes.process_name = "taskhostex.exe"
BY Processes.dest Processes.process_name _time
span=1h
| `drop_dm_object_name(Processes)`
| eval pid_count=mvcount(process_id)
| eval taskhost_count_=if(process_name == "taskhost.exe", pid_count, 0)
| eval taskhostex_count_=if(process_name == "taskhostex.exe", pid_count, 0)
| stats sum(taskhost_count_) as taskhost_count, sum(taskhostex_count_) as taskhostex_count values(action) as action values(original_file_name) as original_file_name values(parent_process) as parent_process values(parent_process_exec) as parent_process_exec values(parent_process_guid) as parent_process_guid values(parent_process_id) as parent_process_id values(parent_process_name) as parent_process_name values(parent_process_path) as parent_process_path values(process) as process values(process_exec) as process_exec values(process_guid) as process_guid values(process_hash) as process_hash values(process_id) as process_id values(process_integrity_level) as process_integrity_level values(user) as user values(process_path) as process_path values(user_id) as user_id values(vendor_product) as vendor_product values(process_name) as process_name
BY _time, dest, firstTime,
lastTime
| where taskhost_count > 10 or taskhostex_count > 10
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| `excessive_number_of_taskhost_processes_filter`Exchange PowerShell Module Usage
The following analytic detects the usage of specific Exchange PowerShell modules, such as New-MailboxExportRequest, New-ManagementRoleAssignment, New-MailboxSearch, and Get-Recipient. It leverages PowerShell Script Block Logging (EventCode 4104) to identify these commands. This activity is significant because these modules can be exploited by adversaries who have gained access via ProxyShell or ProxyNotShell vulnerabilities. If confirmed malicious, attackers could export mailbox contents, assign management roles, conduct mailbox searches, or view recipient objects, potentially leading to data exfiltration, privilege escalation, or unauthorized access to sensitive information.
Show query
`powershell` EventCode=4104 ScriptBlockText IN ("*New-MailboxExportRequest*", "*New-ManagementRoleAssignment*", "*New-MailboxSearch*", "*Get-Recipient*", "Search-Mailbox")
| fillnull
| stats count min(_time) as firstTime max(_time) as lastTime
BY dest signature signature_id
user_id vendor_product EventID
Guid Opcode Name
Path ProcessID ScriptBlockId
ScriptBlockText
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| `exchange_powershell_module_usage_filter`Executable File Written in Administrative SMB Share
The following analytic detects executable files (.exe or .dll) being written to Windows administrative SMB shares (Admin$, IPC$, C$). It leverages Windows Security Event Logs with EventCode 5145 to identify this activity. This behavior is significant as it is commonly used by tools like PsExec/PaExec for staging binaries before creating and starting services on remote endpoints, a technique often employed for lateral movement and remote code execution. If confirmed malicious, this activity could allow an attacker to execute arbitrary code remotely, potentially compromising additional systems within the network.
Show query
`wineventlog_security` EventCode=5145 RelativeTargetName IN ("*.exe","*.dll") ObjectType=File ShareName IN ("\\\\*\\C$","\\\\*\\IPC$","\\\\*\\admin$") AccessMask= "0x2" | stats min(_time) as firstTime max(_time) as lastTime count by EventCode ShareName RelativeTargetName ObjectType AccessMask src_user src_port IpAddress dest | `security_content_ctime(firstTime)` | `executable_file_written_in_administrative_smb_share_filter`Executables Or Script Creation In Suspicious Path
The following analytic identifies the creation of executables or scripts in suspicious file paths on Windows systems. It leverages the Endpoint.Filesystem dataset to detect files with specific extensions (e.g., .exe, .dll, .ps1) created in uncommon directories (e.g., \windows\fonts\, \users\public\). This activity can be significant as adversaries often use these paths to evade detection and maintain persistence. If confirmed malicious, this behavior could allow attackers to execute unauthorized code, escalate privileges, or persist within the environment, posing a significant security threat.
Show query
| tstats `security_content_summariesonly`
count min(_time) as firstTime
max(_time) as lastTime
from datamodel=Endpoint.Filesystem where
Filesystem.file_name IN (
"*.bat",
"*.cmd",
"*.com",
"*.dll",
"*.exe",
"*.js",
"*.msc",
"*.pif",
"*.ps1",
"*.sys",
"*.vbe",
"*.vbs"
)
Filesystem.file_path IN (
"*:\\PerfLogs\\*",
"*:\\Users\\Administrator\\Music\\*",
"*:\\Users\\Default\\*",
"*:\\Users\\Public\\*",
"*:\\Windows\\debug\\*",
"*:\\Windows\\fonts\\*",
"*:\\Windows\\Media\\*",
"*:\\Windows\\repair\\*",
"*:\\Windows\\servicing\\*",
"*\\inetpub\\*",
"*\\Microsoft\\Windows\\Libraries\\*",
"*Recycle.bin*"
)
by Filesystem.action Filesystem.dest Filesystem.file_access_time
Filesystem.file_create_time Filesystem.file_hash
Filesystem.file_modify_time Filesystem.file_name
Filesystem.file_path Filesystem.file_acl Filesystem.file_size
Filesystem.process_guid Filesystem.process_id Filesystem.user
Filesystem.vendor_product
| `drop_dm_object_name(Filesystem)`
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| `executables_or_script_creation_in_suspicious_path_filter`Showing 451-500 of 1,106