Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
125 detections
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The following analytic identifies suspicious command-line parameters associated with the use of Impacket's smbexec.py for lateral movement. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on specific command-line patterns indicative of Impacket tool usage. This activity is significant as both Red Teams and adversaries use Impacket for remote code execution and lateral movement. If confirmed malicious, this activity could allow attackers to execute commands on remote endpoints, potentially leading to unauthorized access, data exfiltration, or further compromise of the network.
The analytic detects `spoolsv.exe` writing a `.dll` file, which is unusual behavior and may indicate exploitation of vulnerabilities like CVE-2021-34527 (PrintNightmare). This detection leverages the Endpoint datamodel, specifically monitoring process and filesystem events to identify `.dll` file creation within the `\spool\drivers\x64\` path. This activity is significant as it may signify an attacker attempting to execute malicious code via the Print Spooler service. If confirmed malicious, this could lead to unauthorized code execution and potential system compromise. Immediate endpoint isolation and further investigation are recommended.
The following analytic detects suspicious database file and directory discovery activities, which may signal an attacker attempt to locate and assess critical database assets on a compromised system. This behavior is often a precursor to data theft, unauthorized access, or privilege escalation, as attackers seek to identify valuable information stored in databases. By monitoring for unusual or unauthorized attempts to locate database files and directories, this analytic aids in early detection of potential reconnaissance or data breach efforts, enabling security teams to respond swiftly and mitigate the risk of further compromise.
The analytic identifies potential lateral movement activities within an organization's Active Directory (AD) environment by correlating multiple analytics from the Active Directory Lateral Movement analytic story within a specified time frame. This is significant for a SOC as lateral movement is a common tactic used by attackers to expand their access within a network, posing a substantial risk. If confirmed malicious, this activity could allow attackers to escalate privileges, access sensitive information, and persist within the environment, leading to severe security breaches.
The following analytic detects the addition of a new custom domain within an Azure Active Directory (AD) tenant. It leverages Azure AD AuditLogs to identify successful "Add unverified domain" operations. This activity is significant as it may indicate an adversary attempting to establish persistence by setting up identity federation backdoors, allowing them to impersonate users and bypass authentication mechanisms. If confirmed malicious, this could enable attackers to gain unauthorized access, escalate privileges, and maintain long-term access to the Azure AD environment, posing a severe security risk.
The analytic identifies AWS console login attempts by users from a new region. It leverages AWS CloudTrail events and compares current login regions against a baseline of previously seen regions for each user. This activity is significant as it may indicate unauthorized access attempts or compromised credentials. If confirmed malicious, an attacker could gain unauthorized access to AWS resources, potentially leading to data breaches, resource manipulation, or further lateral movement within the cloud environment.
This analytic detects file transfers flagged as malware that occurred over non-standard ports (other than 80 and 443) from Cisco Secure Firewall Threat Defense logs. Adversaries may attempt to bypass protocol-based detection or use alternate ports to blend in with other traffic. This analytic identifies these non-conventional flows and surfaces potential evasion techniques. If confirmed malicious, this indicates potential malware delivery or other nefarious activity.
The following analytic detects `spoolsv.exe` writing a `.dll` file, which is unusual behavior and may indicate exploitation of vulnerabilities like CVE-2021-34527 (PrintNightmare). This detection leverages Sysmon EventID 11 to monitor file creation events in the `\spool\drivers\x64\` directory. This activity is significant because `spoolsv.exe` typically does not write DLL files, and such behavior could signify an ongoing attack. If confirmed malicious, this could allow an attacker to execute arbitrary code, escalate privileges, or maintain persistence on the compromised system.
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.
The following analytic detects the creation of a new DWORD value named "EnableAt" in the registry path "HKLM:\SOFTWARE\Microsoft\Windows NT\CurrentVersion\Schedule\Configuration". This modification enables the use of the at.exe or wmi Win32_ScheduledJob commands to add scheduled tasks on a Windows endpoint. The detection leverages registry event data from the Endpoint datamodel. This activity is significant because it may indicate that an attacker is enabling the ability to schedule tasks, potentially to execute malicious code at specific times or intervals. If confirmed malicious, this could allow persistent code execution on the system.
The analytic detects a suspicious registry modification that enables the plain text credential feature in Windows by setting the "UseLogonCredential" value to 1 in the WDigest registry path. This activity is commonly used by malware and tools like Mimikatz to dump plain text credentials, indicating a potential credential dumping attempt. If confirmed malicious, this could allow an attacker to obtain sensitive credentials, leading to further compromise and lateral movement within the network.
The following analytic detects modifications to undocumented registry keys that allow a DLL to load into lsass.exe, potentially capturing credentials. It leverages the Endpoint.Registry data model to identify changes to \CurrentControlSet\Services\NTDS\DirectoryServiceExtPt or \CurrentControlSet\Services\NTDS\LsaDbExtPt. This activity is significant as it indicates a possible attempt to inject malicious code into the Local Security Authority Subsystem Service (LSASS), which can lead to credential theft. If confirmed malicious, this could allow attackers to gain unauthorized access to sensitive information and escalate privileges within the environment.
The analytic identifies spikes in the number of Server Message Block (SMB) connections using the Machine Learning Toolkit (MLTK). It leverages the Network_Traffic data model to monitor SMB traffic on ports 139 and 445, applying a machine learning model to detect anomalies. Sudden increases in SMB traffic can indicate lateral movement or data exfiltration attempts by attackers. If confirmed malicious, this behavior could lead to unauthorized access, data theft, or further compromise of the network.
The analytic detects the suspicious loading of DLLs by spoolsv.exe, potentially indicating PrintNightmare exploitation. It leverages Sysmon EventCode 7 to identify instances where spoolsv.exe loads multiple DLLs from the Windows System32 spool drivers x64 directory. This activity is significant as it may signify an attacker exploiting the PrintNightmare vulnerability to execute arbitrary code. If confirmed malicious, this could lead to unauthorized code execution, privilege escalation, and persistent access within the environment, posing a severe security risk.
The following analytic identifies a suspicious modification of the Windows registry setting for max connections per server. It detects changes to specific registry paths using data from the Endpoint.Registry datamodel. This activity is significant because altering this setting can be exploited by attackers to increase the number of concurrent connections to a remote server, potentially facilitating DDoS attacks or enabling more effective lateral movement within a compromised network. If confirmed malicious, this could lead to network disruption or further compromise of additional systems.
This analytic detects the execution of the 'shred' command on a Linux machine, which is used to overwrite files to make them unrecoverable. It leverages data from Linux Auditd, focusing on process names and command-line arguments. This activity is significant because the 'shred' command can be used in destructive attacks, such as those seen in the Industroyer2 malware targeting energy facilities. If confirmed malicious, this activity could lead to the permanent destruction of critical files, severely impacting system integrity and data availability.
The following analytic detects modifications in the Windows registry to disable Windows Error Reporting (WER) settings. It leverages data from the Endpoint.Registry datamodel, specifically monitoring changes to registry paths related to WER with a value set to "0x00000001". This activity is significant as adversaries may disable WER to suppress error notifications, hiding the presence of malicious activities. If confirmed malicious, this could allow attackers to operate undetected, potentially leading to prolonged persistence and further exploitation within the environment.
The following analytic detects a modification to the Windows registry setting "LongPathsEnabled," which allows file paths longer than 260 characters. This detection leverages data from the Endpoint.Registry datamodel, focusing on changes to the specific registry path and value. This activity is significant because adversaries, including malware like BlackByte, exploit this setting to bypass file path limitations, potentially aiding in evasion techniques. If confirmed malicious, this modification could facilitate the execution of long-path payloads, aiding in persistence and further system compromise.
The following analytic detects suspicious modifications to the Windows registry keys related to file compression color and information tips. It leverages data from the Endpoint.Registry data model, specifically monitoring changes to the "ShowCompColor" and "ShowInfoTip" values under the "Microsoft\\Windows\\CurrentVersion\\Explorer\\Advanced" path. This activity is significant as it was observed in the Hermetic Wiper malware, indicating potential malicious intent to alter file attributes and user interface elements. If confirmed malicious, this could signify an attempt to manipulate file visibility and deceive users, potentially aiding in further malicious activities.
Detects suspicious rejected SMB guest logon attempts from an IP address, which can be an indicator of an attempted PrintNightmare (CVE-2021-1675) remote code execution exploit or other brute-force/password guessing attacks against SMB. The rule specifically looks for EventID 31017 (SMB client security event for rejected guest logon) where the UserName is empty and the ServerName starts with '\1', which is often seen in exploit attempts.
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