Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
462 detections
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The analytic detects the execution of msiexec.exe with the /y switch parameter, which enables the loading of DLLRegisterServer. This activity can indicate an attempt to register malicious DLLs, potentially leading to code execution or persistence on the system. If confirmed malicious, this could allow an attacker to execute arbitrary code, escalate privileges, or maintain persistence within the environment.
The following analytic detects the execution of the Windows command line to log off a host machine. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on processes involving `shutdown.exe` with specific parameters. This activity is significant as it is often associated with Advanced Persistent Threats (APTs) and Remote Access Trojans (RATs) like dcrat, which use this technique to disrupt operations, aid in system destruction, or inhibit recovery. If confirmed malicious, this could lead to system downtime, data loss, or hindered incident response efforts.
This analytic aggregates and summarizes alerts from Microsoft Defender for Endpoint. It extracts details such as source, file name, severity, process command line, IP address, registry key, signature, description, and unique ID. The detection is not intended to identify new activity from raw data but rather to leverage existing Microsoft Defender for Endpoint alerts for correlation and risk-based alerting. It filters out alerts with a 'clean' verdict and dynamically maps MITRE techniques and assigns risk scores based on the alert's severity.
The following analytic identifies processes referencing plist files that determine which applications are re-opened when a user reboots their MacOS machine. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and parent processes related to "com.apple.loginwindow." This activity is significant because it can indicate attempts to persist across reboots, a common tactic used by attackers to maintain access. If confirmed malicious, this could allow an attacker to execute code or maintain persistence on the affected system, potentially leading to further compromise.
The following analytic detects modifications to the Windows registry aimed at disabling the Registry Editor (regedit). It leverages data from the Endpoint.Registry data model, specifically monitoring changes to the registry path "*\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Policies\\System\\DisableRegistryTools" with a value of "0x00000001". This activity is significant because malware, such as RATs or trojans, often disable registry tools to prevent the removal of their entries, aiding in persistence and defense evasion. If confirmed malicious, this could hinder incident response efforts and allow the attacker to maintain control over the compromised system.
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.
The following analytic identifies when a new device is enrolled on an Okta account. It uses OktaIm2 logs ingested via the Splunk Add-on for Okta Identity Cloud to detect the creation of new device enrollments. This activity is significant as it may indicate a legitimate user settinging up a new device or an adversary adding a device to maintain unauthorized access. If confirmed malicious, this could lead to potential account takeover, unauthorized access, and persistent control over the compromised Okta account. Monitoring this behavior is crucial for detecting and mitigating unauthorized access attempts.
This correlation rule identifies when a user exceeds a predefined risk threshold based on multiple suspicious Okta activities within a 24-hour period. It aggregates risk events from "Suspicious Okta Activity," "Okta Account Takeover," and "Okta MFA Exhaustion" analytic stories. The detection is significant as it highlights potentially compromised user accounts exhibiting multiple tactics, techniques, and procedures (TTPs). If confirmed malicious, this activity could indicate a serious security breach, allowing attackers to gain unauthorized access, escalate privileges, or persist within the environment.
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 the execution of `pkexec` without any command-line arguments. This behavior leverages data from Endpoint Detection and Response (EDR) agents, focusing on process telemetry. The significance lies in the fact that this pattern is associated with the exploitation of CVE-2021-4034 (PwnKit), a critical vulnerability in Polkit's pkexec component. If confirmed malicious, this activity could allow an attacker to gain full root privileges on the affected Linux system, leading to complete system compromise and potential unauthorized access to sensitive information.
The following analytic identifies excessive AccessDenied events within an hour timeframe for IAM users in AWS. It leverages AWS CloudTrail logs to detect multiple failed access attempts from the same source IP and user identity. This activity is significant as it may indicate that an access key has been compromised and is being misused for unauthorized discovery actions. If confirmed malicious, this could allow attackers to gather information about the AWS environment, potentially leading to further exploitation or privilege escalation.
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 following analytic detects the creation of new user accounts on Linux systems using commands like "useradd" or "adduser." It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. This activity is significant as adversaries often create new user accounts to establish persistence on compromised hosts. If confirmed malicious, this could allow attackers to maintain access, escalate privileges, and further compromise the system, 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 analytic detects path traversal command-line execution, often used in malicious documents to execute code via msdt.exe for defense evasion. It leverages Endpoint Detection and Response (EDR) data, focusing on specific patterns in process paths. This activity is significant as it can indicate an attempt to bypass security controls and execute unauthorized code. If confirmed malicious, this behavior could lead to code execution, privilege escalation, or persistence within the environment, potentially allowing attackers to deploy malware or leverage other living-off-the-land binaries (LOLBins).
The analytic detects the creation of specific AzureHound-related files, such as `*-azurecollection.zip` and various `.json` files, on disk. It leverages data from the Endpoint.Filesystem datamodel, focusing on file creation events with specific filenames. This activity is significant because AzureHound is a tool used to gather information about Azure environments, similar to SharpHound for on-premises Active Directory. If confirmed malicious, this activity could indicate an attacker is collecting sensitive Azure environment data, potentially leading to further exploitation or privilege escalation within the cloud infrastructure.
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.
This analytic detects the use of `wget` to download a file from a remote source and pipe its content directly to `bash` for execution. This behavior is commonly associated with malicious activities such as deploying coinminers or exploiting vulnerabilities like CVE-2021-44228 (Log4j). Successful execution of such commands could lead to arbitrary code execution, system compromise, and unauthorized data access.
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.
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