Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects modifications to the 'SSLKEYLOGFILE' environment variable in the Windows Registry. This variable is often used by applications like web browsers to log SSL/TLS session keys to a file, which can then be used to decrypt encrypted network traffic. Malicious actors might enable this to capture sensitive data.
This rule detects suspicious device enrollment attempts via Microsoft Intune Company Portal from a country not previously observed in a user's 7-day sign-in history. It identifies two primary patterns: sustained successful sign-ins from a new country, indicating a potential active session and successful device enrollment (High Severity), or Conditional Access Policy (CAP) blocked attempts from a new country, suggesting an attacker probing with stolen credentials (Medium Severity). The detection aims to identify identity theft leading to unauthorized device enrollment.
Detects when a web browser (Chrome, Edge, Firefox, Brave, Opera) initiates a process that is not another instance of itself. This could indicate a drive-by download, execution of malicious code, or other suspicious activity originating from a browser.
This rule identifies an anomalous surge in network connection attempts to a particular remote IP address. It establishes a baseline of average daily connection counts to each remote IP over the past week. It then compares the current hourly connection count to this baseline, triggering an alert if the current count is more than five times the baseline and surpasses a total of 500 connections within that hour. This could indicate various malicious activities such as data exfiltration, command and control communication, or a denial-of-service attack originating from the monitored environment.
This rule detects suspicious device enrollment attempts via Microsoft Intune Company Portal from a country not previously observed in a user's 7-day sign-in history. It identifies two primary patterns: sustained successful sign-ins from a new country, indicating a potential active session and successful device enrollment (High Severity), or Conditional Access Policy (CAP) blocked attempts from a new country, suggesting an attacker probing with stolen credentials (Medium Severity). The detection aims to identify identity theft leading to unauthorized device enrollment.
This rule detects the presence of known Chaos ransomware samples by matching their SHA256 hashes against process execution events and file events. If a process with a matching hash is executed or a file with a matching hash is observed, an alert will be triggered.
This rule detects suspicious device enrollment attempts via Microsoft Intune Company Portal from a country not previously observed in a user's 7-day sign-in history. It identifies two primary patterns: sustained successful sign-ins from a new country, indicating a potential active session and successful device enrollment (High Severity), or Conditional Access Policy (CAP) blocked attempts from a new country, suggesting an attacker probing with stolen credentials (Medium Severity). The detection aims to identify identity theft leading to unauthorized device enrollment.
This rule detects network communication with known AI model providers by checking `RemoteUrl` against a list of AI API URLs from an external CSV file. It aims to identify potential unauthorized use of AI services, data exfiltration to AI models, or to monitor AI usage within an environment.
Detects the addition of a specific threat actor SSH key to an authorized_keys file for persistence. This key was used in a campaign involving a malicious Go crypto module that delivered the Rekoobe backdoor. The rule specifically looks for command-line activity that modifies the authorized_keys file and contains the hardcoded malicious SSH key, often initiated by shell processes.
Detects processes on Linux systems that exhibit fileless execution characteristics while masquerading as common shells or interpreters. This behavior is a key TTP of the ShadowHS post-exploitation framework, which uses argv spoofing and in-memory execution to evade detection.
Detects the creation of configuration files (.cfg) in sensitive or temporary Linux directories. This behavior is associated with the GRIDTIDE backdoor, which stages a configuration file containing a key to decrypt its C2 settings.
The following analytic detects the use of default or publicly known named pipes associated with Cobalt Strike. It leverages Sysmon EventID 17 and 18 to identify specific named pipes commonly used by Cobalt Strike's Artifact Kit and Malleable C2 Profiles. This activity is significant because Cobalt Strike is a popular tool for adversaries to conduct post-exploitation tasks, and identifying its named pipes can reveal potential malicious activity. If confirmed malicious, this could indicate an active Cobalt Strike beacon, leading to unauthorized access, data exfiltration, or further lateral movement within the network.
This analytic detects suspicious data transfer activities involving the use of the `split` syscall with the `-b` option, which specifies the size of each output file. This technique can be used by attackers to evade detection by breaking large files into smaller parts, bypassing size-based security controls, and facilitating covert exfiltration of sensitive data. By monitoring for unusual or unauthorized use of the `split` syscall with a specified block size, this analytic helps identify potential data exfiltration attempts.
This analytic identifies the bulk creation of ransomware notes (e.g., .txt, .html, .hta files) on an infected machine. It leverages Sysmon EventCode 11 to detect multiple instances of these file types being created within a short time frame (10 seconds). A detection is triggered if 15 or more unique readme paths are observed. This activity is a strong indicator of an active ransomware attack, where the attacker is notifying the victim of the encryption. If confirmed malicious, this behavior could lead to widespread data encryption, rendering critical files inaccessible and potentially causing significant operational disruption.
The following analytic detects suspicious attempts to find credentials stored in password stores, indicating a potential attacker's effort to access sensitive login information. Password stores are critical repositories that contain valuable credentials, and unauthorized access to them can lead to significant security breaches. By monitoring for unusual or unauthorized activities related to password store access, this analytic helps identify potential credential theft attempts, allowing security teams to respond promptly and prevent unauthorized access to critical systems and data.
KQL Query from file: Shuyal Stealer Detection
This rule detects potential device code phishing attempts by correlating URL click events with sign-in logs using the 'deviceCode' authentication protocol. It specifically looks for successful or failed sign-ins (result types 0, 50074, 50076, 500121) that occur after a user clicks on a URL.
Detects potential Multi-Factor Authentication (MFA) fatigue attacks by identifying a high number of MFA denial events for a single user within a short timeframe. This rule specifically looks for sign-in logs where the user explicitly declined the authentication prompt, indicating a possible attempt by an attacker to bombard the user with MFA requests until they approve one.
Detects M365 Copilot impersonation and roleplay jailbreak attempts where users try to manipulate the AI into adopting alternate personas, behaving as unrestricted entities, or impersonating malicious AI systems to bypass safety controls. The detection searches exported eDiscovery prompt logs for roleplay keywords like "pretend you are," "act as," "you are now," "amoral," and "roleplay as" in the Subject_Title field. Prompts are categorized into specific impersonation types (AI_Impersonation, Malicious_AI_Persona, Unrestricted_AI_Persona, etc.) to identify attempts to override the AI's safety guardrails through persona injection attacks.
The following analytic identifies the execution of RAR utilities to archive files on a system. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names, GUIDs, and command-line arguments. This activity is significant as threat actors, including red-teamers and malware like DarkGate, use RAR archiving to compress and exfiltrate collected data from compromised hosts. If confirmed malicious, this behavior could lead to the unauthorized transfer of sensitive information to command and control servers, posing a severe risk to data confidentiality and integrity.
This rule detects potential persistence mechanisms related to browser extensions. It looks for processes launching Chrome or Edge with the '--load-extension' command-line argument, which can be used to load unpacked extensions. Additionally, it monitors registry modifications to 'ExtensionInstallForcelist' that contain 'http' or 'crx', indicating a forced installation of an extension, potentially from a remote source or a local file.
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