Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
83 detections
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Detects potential database ransomware activity by correlating the usage of AES_ENCRYPT for record modification, followed by the dropping of tables and the insertion of ransom notes containing specific contact keywords (e.g., bitcoin, proton mail) within database queries or system logs.
KQL Query from file: postgresql-replication-account-escalated-to-superuser-cve-2026-6471.kql
Detects the creation of tar archives within the Coder database during a specific window associated with rogue IP activity, where the file creation was performed by a system-level process.
Detects unauthorized modification of the Nacos database users table, specifically identifying direct INSERT or UPDATE statements used to plant a backdoor administrative account. The rule correlates database modifications with abnormal authentication patterns, such as a password hash update occurring after a failed login attempt to the Nacos application.
Detects potential database-native ransomware activity by identifying the use of MySQL encryption functions (AES_ENCRYPT) to encrypt records followed by the execution of DROP TABLE commands. This pattern is consistent with extortion techniques where attackers encrypt internal database data and remove original tables.
Detects the creation of tar archives within the Coder database during a specific window associated with rogue IP activity, where the file creation was performed by a system-level process.
Detects the creation of tar archives within the Coder database during a specific window associated with rogue IP activity, where the file creation was performed by a system-level process.
Detects the creation of tar archives within the Coder database during a specific window associated with rogue IP activity, where the file creation was performed by a system-level process.
Detects the creation of tar archives within the Coder database during a specific window associated with rogue IP activity, where the file creation was performed by a system-level process.
Detects unauthorized modifications to the 'wp_users' table in a WordPress database. The rule correlates specific WordPress REST API calls (oEmbed or batch processing endpoints) originating from the same source IP address that performs database inserts or updates, while explicitly excluding activity originating from verified Admin UI sessions.
This rule detects potentially unauthorized database manipulation of the 'xadmin' platform, including suspicious administrative role modifications and deletions. It also monitors for suspicious Python script execution involving the 'bcrypt' library, which can be used to generate or verify password hashes in potentially malicious scripts.
Detects command-line execution of database drop operations combined with the disabling of foreign key constraints, which is indicative of malicious database manipulation or automated data destruction attempts.
Detects rapid, suspicious command-line activity indicative of unauthorized user account creation or modification within a database environment, specifically targeting 'xadmin' or 'ROLE_ADMIN' roles accompanied by password hashing commands. The rule monitors for multiple events within a 120-second window to identify potentially automated or batch unauthorized account provisioning.
Detects potentially malicious activity involving MySQL processes, specifically focusing on the creation of files within the /var/lib/mysql-files/ directory, use of SQL 'INTO OUTFILE' for file output, or the use of 'LOAD_FILE()' to read sensitive system files like /etc/hostname or /proc/1/mountinfo, often indicative of SQL injection exploitation or unauthorized data access.
Detects high-frequency execution of MySQL enumeration queries (such as showing databases, tables, or selecting from the information_schema) by the MySQL client process. This behavior is often associated with database reconnaissance or automated scanning during the discovery phase of an attack.
Detects administrative user management (Create/Alter/Drop) and schema deletion within SAP HANA DB.
Detects changes to SAP HANA audit policies (Create, Alter, Drop) which could indicate an attempt to evade logging.
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.
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