Update v8.11.19
editUpdate v8.11.19
editThis section lists all updates associated with version 8.11.19 of the Fleet integration Prebuilt Security Detection Rules.
| Rule | Description | Status | Version |
|---|---|---|---|
This rule is triggered when CVEs collected from the Rapid7 Threat Command Integration have a match against vulnerabilities that were found in the customer environment. |
new |
1 |
|
Identifies the creation of a DNS record that is potentially meant to enable WPAD spoofing. Attackers can disable the Global Query Block List (GQBL) and create a "wpad" record to exploit hosts running WPAD with default settings for privilege escalation and lateral movement. |
new |
1 |
|
Identifies the execution of wbadmin to access the NTDS.dit file in a domain controller. Attackers with privileges from groups like Backup Operators can abuse the utility to perform credential access and compromise the domain. |
new |
1 |
|
Identifies changes to the DNS Global Query Block List (GQBL), a security feature that prevents the resolution of certain DNS names often exploited in attacks like WPAD spoofing. Attackers with certain privileges, such as DNSAdmins, can modify or disable the GQBL, allowing exploitation of hosts running WPAD with default settings for privilege escalation and lateral movement. |
new |
1 |
|
Identifies unusual DLLs loaded by the DNS Server process, potentially indicating the abuse of the ServerLevelPluginDll functionality. This can lead to privilege escalation and remote code execution with SYSTEM privileges. |
new |
1 |
|
Potential Privilege Escalation via Service ImagePath Modification |
Identifies registry modifications to default services that could enable privilege escalation to SYSTEM. Attackers with privileges from groups like Server Operators may change the ImagePath of services to executables under their control or to execute commands. |
new |
1 |
Detects when multiple hosts are using the same agent ID. This could occur in the event of an agent being taken over and used to inject illegitimate documents into an instance as an attempt to spoof events in order to masquerade actual activity to evade detection. |
update |
102 |
|
A machine learning job detected a significant spike in the rate of a particular error in the CloudTrail messages. Spikes in error messages may accompany attempts at privilege escalation, lateral movement, or discovery. |
update |
209 |
|
A machine learning job detected an unusual error in a CloudTrail message. These can be byproducts of attempted or successful persistence, privilege escalation, defense evasion, discovery, lateral movement, or collection. |
update |
209 |
|
A machine learning job detected AWS command activity that, while not inherently suspicious or abnormal, is sourcing from a geolocation (city) that is unusual for the command. This can be the result of compromised credentials or keys being used by a threat actor in a different geography than the authorized user(s). |
update |
209 |
|
A machine learning job detected AWS command activity that, while not inherently suspicious or abnormal, is sourcing from a geolocation (country) that is unusual for the command. This can be the result of compromised credentials or keys being used by a threat actor in a different geography than the authorized user(s). |
update |
209 |
|
A machine learning job detected an AWS API command that, while not inherently suspicious or abnormal, is being made by a user context that does not normally use the command. This can be the result of compromised credentials or keys as someone uses a valid account to persist, move laterally, or exfiltrate data. |
update |
209 |
|
Identifies the use of AssumeRole. AssumeRole returns a set of temporary security credentials that can be used to access AWS resources. An adversary could use those credentials to move laterally and escalate privileges. |
update |
207 |
|
This rule leverages the File Integrity Monitoring (FIM) integration to detect file modifications of files that are commonly used for persistence on Linux systems. The rule detects modifications to files that are commonly used for cron jobs, systemd services, message-of-the-day (MOTD), SSH configurations, shell configurations, runtime control, init daemon, passwd/sudoers/shadow files, Systemd udevd, and XDG/KDE autostart entries. To leverage this rule, the paths specified in the query need to be added to the FIM policy in the Elastic Security app. |
update |
2 |
|
A machine learning job detected unusually large numbers of DNS queries for a single top-level DNS domain, which is often used for DNS tunneling. DNS tunneling can be used for command-and-control, persistence, or data exfiltration activity. For example, dnscat tends to generate many DNS questions for a top-level domain as it uses the DNS protocol to tunnel data. |
update |
104 |
|
A machine learning job detected a rare and unusual DNS query that indicate network activity with unusual DNS domains. This can be due to initial access, persistence, command-and-control, or exfiltration activity. For example, when a user clicks on a link in a phishing email or opens a malicious document, a request may be sent to download and run a payload from an uncommon domain. When malware is already running, it may send requests to an uncommon DNS domain the malware uses for command-and-control communication. |
update |
104 |
|
A machine learning job detected a rare and unusual URL that indicates unusual web browsing activity. This can be due to initial access, persistence, command-and-control, or exfiltration activity. For example, in a strategic web compromise or watering hole attack, when a trusted website is compromised to target a particular sector or organization, targeted users may receive emails with uncommon URLs for trusted websites. These URLs can be used to download and run a payload. When malware is already running, it may send requests to uncommon URLs on trusted websites the malware uses for command-and-control communication. When rare URLs are observed being requested for a local web server by a remote source, these can be due to web scanning, enumeration or attack traffic, or they can be due to bots and web scrapers which are part of common Internet background traffic. |
update |
104 |
|
A machine learning job detected a rare and unusual user agent indicating web browsing activity by an unusual process other than a web browser. This can be due to persistence, command-and-control, or exfiltration activity. Uncommon user agents coming from remote sources to local destinations are often the result of scanners, bots, and web scrapers, which are part of common Internet background traffic. Much of this is noise, but more targeted attacks on websites using tools like Burp or SQLmap can sometimes be discovered by spotting uncommon user agents. Uncommon user agents in traffic from local sources to remote destinations can be any number of things, including harmless programs like weather monitoring or stock-trading programs. However, uncommon user agents from local sources can also be due to malware or scanning activity. |
update |
104 |
|
A machine learning job found an unusually large spike in authentication failure events. This can be due to password spraying, user enumeration or brute force activity and may be a precursor to account takeover or credentialed access. |
update |
105 |
|
A machine learning job found an unusually large spike in successful authentication events. This can be due to password spraying, user enumeration or brute force activity. |
update |
104 |
|
A machine learning job found an unusually large spike in successful authentication events from a particular source IP address. This can be due to password spraying, user enumeration or brute force activity. |
update |
105 |
|
Looks for anomalous access to the metadata service by an unusual process. The metadata service may be targeted in order to harvest credentials or user data scripts containing secrets. |
update |
104 |
|
Looks for anomalous access to the cloud platform metadata service by an unusual user. The metadata service may be targeted in order to harvest credentials or user data scripts containing secrets. |
update |
104 |
|
Identifies an unusually high number of authentication attempts. |
update |
104 |
|
Looks for anomalous access to the metadata service by an unusual process. The metadata service may be targeted in order to harvest credentials or user data scripts containing secrets. |
update |
104 |
|
Looks for anomalous access to the cloud platform metadata service by an unusual user. The metadata service may be targeted in order to harvest credentials or user data scripts containing secrets. |
update |
104 |
|
Looks for commands related to system information discovery from an unusual user context. This can be due to uncommon troubleshooting activity or due to a compromised account. A compromised account may be used to engage in system information discovery in order to gather detailed information about system configuration and software versions. This may be a precursor to selection of a persistence mechanism or a method of privilege elevation. |
update |
104 |
|
Looks for commands related to system network configuration discovery from an unusual user context. This can be due to uncommon troubleshooting activity or due to a compromised account. A compromised account may be used by a threat actor to engage in system network configuration discovery in order to increase their understanding of connected networks and hosts. This information may be used to shape follow-up behaviors such as lateral movement or additional discovery. |
update |
105 |
|
Looks for commands related to system network connection discovery from an unusual user context. This can be due to uncommon troubleshooting activity or due to a compromised account. A compromised account may be used by a threat actor to engage in system network connection discovery in order to increase their understanding of connected services and systems. This information may be used to shape follow-up behaviors such as lateral movement or additional discovery. |
update |
104 |
|
Looks for commands related to system process discovery from an unusual user context. This can be due to uncommon troubleshooting activity or due to a compromised account. A compromised account may be used by a threat actor to engage in system process discovery in order to increase their understanding of software applications running on a target host or network. This may be a precursor to selection of a persistence mechanism or a method of privilege elevation. |
update |
104 |
|
Looks for commands related to system user or owner discovery from an unusual user context. This can be due to uncommon troubleshooting activity or due to a compromised account. A compromised account may be used to engage in system owner or user discovery in order to identify currently active or primary users of a system. This may be a precursor to additional discovery, credential dumping or privilege elevation activity. |
update |
105 |
|
A machine learning job detected a PowerShell script with unusual data characteristics, such as obfuscation, that may be a characteristic of malicious PowerShell script text blocks. |
update |
105 |
|
A machine learning job detected a user logging in at a time of day that is unusual for the user. This can be due to credentialed access via a compromised account when the user and the threat actor are in different time zones. In addition, unauthorized user activity often takes place during non-business hours. |
update |
105 |
|
A machine learning job detected a user logging in from an IP address that is unusual for the user. This can be due to credentialed access via a compromised account when the user and the threat actor are in different locations. An unusual source IP address for a username could also be due to lateral movement when a compromised account is used to pivot between hosts. |
update |
104 |
|
A machine learning job found an unusual user name in the authentication logs. An unusual user name is one way of detecting credentialed access by means of a new or dormant user account. An inactive user account (because the user has left the organization) that becomes active may be due to credentialed access using a compromised account password. Threat actors will sometimes also create new users as a means of persisting in a compromised web application. |
update |
105 |
|
A machine learning job detected activity for a username that is not normally active, which can indicate unauthorized changes, activity by unauthorized users, lateral movement, or compromised credentials. In many organizations, new usernames are not often created apart from specific types of system activities, such as creating new accounts for new employees. These user accounts quickly become active and routine. Events from rarely used usernames can point to suspicious activity. Additionally, automated Linux fleets tend to see activity from rarely used usernames only when personnel log in to make authorized or unauthorized changes, or threat actors have acquired credentials and log in for malicious purposes. Unusual usernames can also indicate pivoting, where compromised credentials are used to try and move laterally from one host to another. |
update |
104 |
|
A machine learning job detected activity for a username that is not normally active, which can indicate unauthorized changes, activity by unauthorized users, lateral movement, or compromised credentials. In many organizations, new usernames are not often created apart from specific types of system activities, such as creating new accounts for new employees. These user accounts quickly become active and routine. Events from rarely used usernames can point to suspicious activity. Additionally, automated Linux fleets tend to see activity from rarely used usernames only when personnel log in to make authorized or unauthorized changes, or threat actors have acquired credentials and log in for malicious purposes. Unusual usernames can also indicate pivoting, where compromised credentials are used to try and move laterally from one host to another. |
update |
105 |
|
A machine learning job detected an unusual remote desktop protocol (RDP) username, which can indicate account takeover or credentialed persistence using compromised accounts. RDP attacks, such as BlueKeep, also tend to use unusual usernames. |
update |
104 |
|
A machine learning job detected an unusually large spike in network traffic that was denied by network access control lists (ACLs) or firewall rules. Such a burst of denied traffic is usually caused by either 1) a mis-configured application or firewall or 2) suspicious or malicious activity. Unsuccessful attempts at network transit, in order to connect to command-and-control (C2), or engage in data exfiltration, may produce a burst of failed connections. This could also be due to unusually large amounts of reconnaissance or enumeration traffic. Denial-of-service attacks or traffic floods may also produce such a surge in traffic. |
update |
104 |
|
A machine learning job detected an unusually large spike in network traffic. Such a burst of traffic, if not caused by a surge in business activity, can be due to suspicious or malicious activity. Large-scale data exfiltration may produce a burst of network traffic; this could also be due to unusually large amounts of reconnaissance or enumeration traffic. Denial-of-service attacks or traffic floods may also produce such a surge in traffic. |
update |
104 |
|
Identifies Linux processes that do not usually use the network but have unexpected network activity, which can indicate command-and-control, lateral movement, persistence, or data exfiltration activity. A process with unusual network activity can denote process exploitation or injection, where the process is used to run persistence mechanisms that allow a malicious actor remote access or control of the host, data exfiltration, and execution of unauthorized network applications. |
update |
104 |
|
Identifies unusual destination port activity that can indicate command-and-control, persistence mechanism, or data exfiltration activity. Rarely used destination port activity is generally unusual in Linux fleets, and can indicate unauthorized access or threat actor activity. |
update |
104 |
|
A machine learning job detected an unusual network destination domain name. This can be due to initial access, persistence, command-and-control, or exfiltration activity. For example, when a user clicks on a link in a phishing email or opens a malicious document, a request may be sent to download and run a payload from an uncommon web server name. When malware is already running, it may send requests to an uncommon DNS domain the malware uses for command-and-control communication. |
update |
104 |
|
A machine learning job detected a rare destination country name in the network logs. This can be due to initial access, persistence, command-and-control, or exfiltration activity. For example, when a user clicks on a link in a phishing email or opens a malicious document, a request may be sent to download and run a payload from a server in a country which does not normally appear in network traffic or business work-flows. Malware instances and persistence mechanisms may communicate with command-and-control (C2) infrastructure in their country of origin, which may be an unusual destination country for the source network. |
update |
104 |
|
A machine learning job detected an unusually large spike in network activity to one destination country in the network logs. This could be due to unusually large amounts of reconnaissance or enumeration traffic. Data exfiltration activity may also produce such a surge in traffic to a destination country that does not normally appear in network traffic or business workflows. Malware instances and persistence mechanisms may communicate with command-and-control (C2) infrastructure in their country of origin, which may be an unusual destination country for the source network. |
update |
105 |
|
Identifies Windows processes that do not usually use the network but have unexpected network activity, which can indicate command-and-control, lateral movement, persistence, or data exfiltration activity. A process with unusual network activity can denote process exploitation or injection, where the process is used to run persistence mechanisms that allow a malicious actor remote access or control of the host, data exfiltration, and execution of unauthorized network applications. |
update |
104 |
|
Searches for rare processes running on multiple Linux hosts in an entire fleet or network. This reduces the detection of false positives since automated maintenance processes usually only run occasionally on a single machine but are common to all or many hosts in a fleet. |
update |
105 |
|
Identifies rare processes that do not usually run on individual hosts, which can indicate execution of unauthorized services, malware, or persistence mechanisms. Processes are considered rare when they only run occasionally as compared with other processes running on the host. |
update |
105 |
|
Identifies rare processes that do not usually run on individual hosts, which can indicate execution of unauthorized services, malware, or persistence mechanisms. Processes are considered rare when they only run occasionally as compared with other processes running on the host. |
update |
109 |
|
Identifies processes started from atypical folders in the file system, which might indicate malware execution or persistence mechanisms. In corporate Windows environments, software installation is centrally managed and it is unusual for programs to be executed from user or temporary directories. Processes executed from these locations can denote that a user downloaded software directly from the Internet or a malicious script or macro executed malware. |
update |
105 |
|
Searches for rare processes running on multiple hosts in an entire fleet or network. This reduces the detection of false positives since automated maintenance processes usually only run occasionally on a single machine but are common to all or many hosts in a fleet. |
update |
106 |
|
Identifies unusual parent-child process relationships that can indicate malware execution or persistence mechanisms. Malicious scripts often call on other applications and processes as part of their exploit payload. For example, when a malicious Office document runs scripts as part of an exploit payload, Excel or Word may start a script interpreter process, which, in turn, runs a script that downloads and executes malware. Another common scenario is Outlook running an unusual process when malware is downloaded in an email. Monitoring and identifying anomalous process relationships is a method of detecting new and emerging malware that is not yet recognized by anti-virus scanners. |
update |
106 |
|
A machine learning job detected an unusual Windows service, This can indicate execution of unauthorized services, malware, or persistence mechanisms. In corporate Windows environments, hosts do not generally run many rare or unique services. This job helps detect malware and persistence mechanisms that have been installed and run as a service. |
update |
104 |
|
Looks for sudo activity from an unusual user context. An unusual sudo user could be due to troubleshooting activity or it could be a sign of credentialed access via compromised accounts. |
update |
104 |
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A machine learning job detected an unusual user context switch, using the runas command or similar techniques, which can indicate account takeover or privilege escalation using compromised accounts. Privilege elevation using tools like runas are more commonly used by domain and network administrators than by regular Windows users. |
update |
104 |
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Looks for compiler activity by a user context which does not normally run compilers. This can be the result of ad-hoc software changes or unauthorized software deployment. This can also be due to local privilege elevation via locally run exploits or malware activity. |
update |
104 |
|
This rule is triggered when an IP address indicator from the Threat Intel Filebeat module or integrations has a match against a network event. |
update |
7 |
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This rule is triggered when a hash indicator from the Threat Intel Filebeat module or integrations has a match against an event that contains file hashes, such as antivirus alerts, process creation, library load, and file operation events. |
update |
8 |
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This rule is triggered when a Windows registry indicator from the Threat Intel Filebeat module or integrations has a match against an event that contains registry data. |
update |
7 |
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This rule is triggered when a URL indicator from the Threat Intel Filebeat module or integrations has a match against an event that contains URL data, like DNS events, network logs, etc. |
update |
7 |