Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
4,543 detections
Filters
Last updated
All Time
Detection languages
2,478
532
344
301
286
Contributors
1,303
815
737
215
193
Categories
20,456
11,839
5,877
5,156
4,865
4,543
Platforms
3,841
1,049
473
39
29
Products / Services
1,835
1,138
211
183
171
MITRE Techniques
1,604
1,423
1,300
749
473
CVEs
19
17
16
11
11
IDS Classtypes
23
1
IDS Protocols
16
7
1
This rule detects a suspicious sequence of events where a process with a name resembling an updater (AutoUpdate.exe or au.exe) accesses a specific remote configuration file from a herokuapp.com URL, followed by the creation or modification of specific application binaries (e.g., InstaTime.exe, ffmpegsumo.dll). This pattern is indicative of a supply chain compromise or an automated software update hijacking where malicious binaries are staged to replace legitimate application components.
Detects unauthorized attempts to dump the process memory of the Local Security Authority Subsystem Service (LSASS), a common technique used by attackers to harvest credentials from memory. This includes the use of legitimate diagnostic tools like procdump and comsvcs.dll, as well as the identification of resulting dump files in directory paths associated with LSASS.
Detects modifications to the 'crond' daemon binary followed closely (within 5 minutes) by a restart or start command via common system management utilities. This behavior is highly indicative of potential persistence mechanisms where an adversary replaces a system binary with a malicious version.
Detects the creation or modification of specific files within the /tmp directory, specifically those matching naming patterns associated with 'jasper-log' files or temporary pipe files. These patterns are often associated with temporary storage or data staging activities during post-exploitation or malware execution.
This rule detects modifications or creation of files named 'crond' in standard system paths ('/usr/bin' or '/usr/sbin') that occur within a 4-second window of an 'ssh' binary file event. This pattern is indicative of potential persistence establishment where an adversary replaces or modifies a system daemon while simultaneously interacting with SSH services.
Detects the creation or modification of a specific file ('libvirtlog.so.0') located in '/usr/lib/' which acts as a gating mechanism for the curlRAT malware. The detection correlates this file event with processes commonly associated with system management that are executing with command line arguments indicative of malicious activity (e.g., 'reverse', 'pty').
Detects modifications to the 'crond' daemon binary followed closely (within 5 minutes) by a restart or start command via common system management utilities. This behavior is highly indicative of potential persistence mechanisms where an adversary replaces a system binary with a malicious version.
Detects the creation or modification of files within the /tmp directory that follow a specific naming convention (t[number]_w.pipe) by an HAProxy process. This pattern is often associated with inter-process communication or potential post-exploitation activity where HAProxy is leveraged to handle named pipes for malicious command execution or data redirection.
This rule detects potential persistence mechanisms in a PostgreSQL database by identifying the creation of a new shared library file (e.g., .so or .dll) within PostgreSQL plugin directories, followed by a modification to the PostgreSQL configuration files (postgresql.conf or postgresql.auto.conf) within a short 30-minute window. This behavior is indicative of an attacker registering a malicious shared library to be loaded upon database startup, a technique associated with PostGREShell-style post-exploitation.
Detects anomalous activity originating from PostgreSQL processes, including the spawning of shells (cmd, powershell, sh, bash), access to sensitive files (e.g., /etc/shadow, SSH keys), and file creation outside the standard PostgreSQL data directories, which is consistent with the abuse of SQL functionality like pg_read_file() or lo_export().
Detects anomalous activity originating from PostgreSQL processes, including the spawning of shells (cmd, powershell, sh, bash), access to sensitive files (e.g., /etc/shadow, SSH keys), and file creation outside the standard PostgreSQL data directories, which is consistent with the abuse of SQL functionality like pg_read_file() or lo_export().
Detects anomalous activity originating from PostgreSQL processes, including the spawning of shells (cmd, powershell, sh, bash), access to sensitive files (e.g., /etc/shadow, SSH keys), and file creation outside the standard PostgreSQL data directories, which is consistent with the abuse of SQL functionality like pg_read_file() or lo_export().
Detects package manager processes (npm, pip, python) executing install-related commands followed by the creation or modification of sensitive files (SSH keys, cloud credentials, shell configurations) within a short timeframe. This behavior is indicative of potential supply chain attacks where a malicious package attempts to steal credentials or secrets upon installation.
Detects package manager processes (npm, pip, python) executing install-related commands followed by the creation or modification of sensitive files (SSH keys, cloud credentials, shell configurations) within a short timeframe. This behavior is indicative of potential supply chain attacks where a malicious package attempts to steal credentials or secrets upon installation.
Detects package manager processes (npm, pip, python) executing install-related commands followed by the creation or modification of sensitive files (SSH keys, cloud credentials, shell configurations) within a short timeframe. This behavior is indicative of potential supply chain attacks where a malicious package attempts to steal credentials or secrets upon installation.
Detects package manager processes (npm, pip, python) executing install-related commands followed by the creation or modification of sensitive files (SSH keys, cloud credentials, shell configurations) within a short timeframe. This behavior is indicative of potential supply chain attacks where a malicious package attempts to steal credentials or secrets upon installation.
The following analytic detects .cab files being written to disk.
It leverages data from Endpoint Detection and Response (EDR) agents, focusing on events where the file name is '*.cab' and the action is 'write'.
This activity can be significant as .cab files can be used to deliver malicious payloads, including embedded .url files that execute harmful code.
If confirmed malicious, this behavior could lead to unauthorized code execution and potential system compromise.
Analysts should review the file path and associated artifacts for further investigation.
It leverages data from Endpoint Detection and Response (EDR) agents, focusing on events where the file name is '*.cab' and the action is 'write'.
This activity can be significant as .cab files can be used to deliver malicious payloads, including embedded .url files that execute harmful code.
If confirmed malicious, this behavior could lead to unauthorized code execution and potential system compromise.
Analysts should review the file path and associated artifacts for further investigation.
Detects file creation, modification, or renaming in System32 by TieringEngineService.exe or MsMpEng.exe within a short time frame.
Detects persistence activities initiated by Postgres-related processes or users involving scheduled tasks and system services.
Detects persistence activities initiated by Postgres-related processes or users involving scheduled tasks and system services.
Detects persistence activities initiated by Postgres-related processes or users involving scheduled tasks and system services.
Page 74 of 217