Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects phishing messages that deliberately mix writing systems/scripts or use homoglyphs in the Subject line as ONE corroborating signal within a broader, multi-signal phishing pattern. This is the only translation that implements true sender-novelty checking via baseline lookups (a sending domain must be new to both the specific recipient and the organization as a whole to add risk). A weighted score (script-mixing=2, homoglyph=3, phishing vocabulary=2, sender-novelty=1, threshold=6) requires multiple corroborating signals before firing. Note: unlike the SPL/S1QL/CQL/CortexQL translations, this version does not strip brand/product tokens before the script check.
Detects phishing messages that deliberately mix writing systems/scripts/homoglyphs in the Subject line as ONE corroborating signal within a broader, multi-signal phishing pattern. Hardened against false positives: brand/product tokens (e.g. Microsoft, PayPal) are stripped before the Latin-script check so a lone brand mention in non-Latin correspondence doesn't count as mixed-script; a weighted score (script-mixing=2, homoglyph=3, high-confidence phishing vocabulary=2, threshold=6) means no two of the three signal categories can reach the threshold alone — all three must co-occur before the rule fires.
Detects phishing messages that deliberately mix writing systems/scripts/homoglyphs in the Subject line as ONE corroborating signal within a broader, multi-signal phishing pattern. Hardened against false positives: brand/product tokens (e.g. Microsoft, PayPal) are stripped before the Latin-script check so a lone brand mention in non-Latin correspondence doesn't count as mixed-script; a weighted score (script-mixing=2, homoglyph=3, high-confidence phishing vocabulary=2, threshold=6) means no two of the three signal categories can reach the threshold alone — all three must co-occur before the rule fires.
Detects phishing messages that deliberately mix writing systems/scripts/homoglyphs in the Subject line as ONE corroborating signal within a broader, multi-signal phishing pattern. Hardened against false positives: brand/product tokens (e.g. Microsoft, PayPal) are stripped before the Latin-script check so a lone brand mention in non-Latin correspondence doesn't count as mixed-script; a weighted score (script-mixing=2, homoglyph=3, high-confidence phishing vocabulary=2, threshold=6) means no two of the three signal categories can reach the threshold alone — all three must co-occur before the rule fires.
Detects the creation of temporary files with names starting with 'MSI' and ending in '.tmp' within the system's temporary directory. Legitimate installations typically perform these actions via the 'msiexec.exe' process; therefore, files created by other processes may indicate suspicious payload staging or activity by malware attempting to masquerade as MSI installer components.
Detects process execution attempts where the binary is located in specific, non-standard macOS system library paths often associated with background tasks or masquerading, specifically when the process is invoked with a '--type=renderer' command-line argument. This is a common pattern for malicious code attempting to mimic browser-based or helper processes.
This rule detects potential persistence via scheduled tasks that masquerade as legitimate network components (e.g., 'System Health Monitor'), as well as abnormal use of WebDav via rundll32.exe or net.exe in the context of the Star Blizzard (COLDRIVER) campaign. It monitors for task creation/updates, schtasks command lines, registry artifacts related to task scheduling, and suspicious WebDav network utility usage, while excluding common security tool processes.
This rule detects potential persistence via scheduled tasks that masquerade as legitimate network components (e.g., 'System Health Monitor'), as well as abnormal use of WebDav via rundll32.exe or net.exe in the context of the Star Blizzard (COLDRIVER) campaign. It monitors for task creation/updates, schtasks command lines, registry artifacts related to task scheduling, and suspicious WebDav network utility usage, while excluding common security tool processes.
This rule detects activities associated with the Star Blizzard (also known as APT28 or Callisto) group's CosmicPulse malware, specifically the downloader and Python-based backdoor. It monitors for suspicious CPL file execution via UNC paths by rundll32.exe or control.exe, unauthorized modifications to the HKCU\Software\Classes\.mollis registry key used for COM hijacking, and anomalous spawning of Python executables from control host processes in user-writable directories.
Detects execution of potentially malicious or dual-use tools (such as credential dumpers or security-bypass utilities) from within ScreenConnect temporary directories via the 'RunFile' command. This is indicative of an attacker leveraging legitimate remote support software to deploy secondary payloads.
Detects execution of potentially malicious or dual-use tools (such as credential dumpers or security-bypass utilities) from within ScreenConnect temporary directories via the 'RunFile' command. This is indicative of an attacker leveraging legitimate remote support software to deploy secondary payloads.
This rule detects malicious activity related to the exploitation of MpDlpService.exe. It looks for the execution or presence of known malicious file hashes associated with this threat, including a P-stage script, payload files, and a malicious mpclient.dll. Additionally, it identifies instances where the legitimate MpDlpService.exe binary is executed from a non-standard, suspicious directory, which is a common indicator of side-loading or persistence techniques.
Detects the execution of a file named 'setup.exe' from suspicious user-writable directories (Temp, Downloads, AppData) when the command line arguments contain keywords associated with archive utilities like 7-Zip or Foobar2000. This pattern is indicative of a 'nested installer' technique where legitimate software utilities are leveraged to extract or execute malicious payloads.
This rule detects the execution of 'rnpkeys.exe' from within the '\ProgramData\keyroll\' directory, specifically when it loads 'rnp.dll' from the same path. This behavior is indicative of potential malicious activity, as the ProgramData directory is a common location for adversaries to stage files, and the use of custom DLL loading from this directory may suggest an attempt to execute unauthorized code or obfuscate tool activity.
This rule detects the execution of processes that use file names mimicking legitimate security or system services, such as 'Credential Guard.exe' or 'Window Security Health Services.exe'. These names are often used by adversaries for masquerading to evade detection by blending in with legitimate system activity.
Detects CARBONATO botnet activity where malicious processes are masquerading as legitimate Linux system components. This includes XMRig mining software running as /usr/sbin/systemd-logind, process arguments attempting to mimic kernel kworker threads, or processes running from directories associated with netd-svc/resolved container images.
Detects the presence of ZIP archive files masquerading as 'Evernote.zip' that contain 'notepad++.exe', which is a pattern frequently observed in VBScript-based downloaders utilizing binary sideloading to execute malicious payloads.
This rule monitors network, file, and process telemetry for matches against a predefined list of malicious IP addresses, domain names, and file hashes. It flags suspicious network connections to known C2 infrastructure and the presence or execution of known malicious files based on SHA256 and SHA1 indicators.
Detects the download and subsequent extraction of the Python embeddable zip package to directories under 'C:\Users\Public'. This activity is consistent with techniques used by threat actors, such as APT37, to establish a covert Python runtime environment on a compromised host for further malicious operations.
Detects potential AI-targeted cloaking, a technique where a web server serves different content to AI crawlers versus human users, identified by comparing HTTP response sizes or hashes across session logs. The rule uses Microsoft Sentinel's ASIM Web Session schema to correlate web requests, identifying discrepancies that suggest malicious or evasive intent. Covers T1036 & T1027
Detects potential AI-targeted cloaking, a technique where a web server serves different content to AI crawlers versus human users, identified by comparing HTTP response sizes or hashes across session logs. The rule uses Microsoft Sentinel's ASIM Web Session schema to correlate web requests, identifying discrepancies that suggest malicious or evasive intent. Covers T1036 & T1027
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