Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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This rule helps you find files that may be part of the EmEditor watering hole incident by looking for a small set of very specific clues that were described in public reporting. It searches for lookalike website names and a repeated campaign marker that appeared in the attacker’s network paths. A match does not automatically prove malware, but it is a strong hint that the file should be reviewed, especially if it is a script, configuration, or installer related content. If you get a hit, confirm where the file came from and whether it is expected for that machine. If it is unexpected, save the file for analysis, check for other related downloads, and look for signs of credential theft or surveillance activity. Also search for the same marker across endpoints, because campaigns often reuse the same identifier on multiple victims. The intent is to give defenders an easy, reliable starting point for triage using human readable clues.
Detects network and endpoint activity associated with the XWorm LATAM campaign, including C2 communication, payload downloads, CasPol.exe making outbound connections, WMI-spawned PowerShell with Cloudinary fetches and Assembly.Load, and a specific file hash.
This rule detects two related activities that could indicate the use of AI webchat services as a covert C2 proxy via WebView2. The first part identifies network connections to specific AI webchat domains (grok.com, copilot.microsoft.com) via proxy or web logs. The second part identifies the creation of the 'msedgewebview2.exe' process by unusual parent processes, excluding common browsers and Microsoft Office applications. This combination aims to catch scenarios where WebView2, often used by AI webchat applications, is leveraged for malicious command and control.
This rule detects network connections to known cryptocurrency mining pools or processes associated with cryptocurrency mining. It identifies suspicious activity based on remote URLs, process names, and remote ports commonly used by miners.
This rule detects activities associated with the SSHStalker botnet. It identifies outbound IRC-like network traffic on common IRC ports (6667, 6697, 7000) over TCP. It also looks for persistence mechanisms by monitoring writes to common cron job directories and files on Linux systems. Additionally, the rule detects log tampering attempts by monitoring writes to critical system log files such as /var/run/utmp, /var/log/wtmp, and /var/log/lastlog. Finally, it identifies on-host compilation activities, specifically the execution of compilers (gcc, cc, clang) or build tools (make) within suspicious temporary directories like /tmp, /var/tmp, or /dev/shm, which can indicate malware compilation.
This rule detects suspicious processes (e.g., powershell.exe, cmd.exe, mshta.exe) making outbound network connections to public IP addresses on non-standard ports. This behavior can indicate command and control (C2) communication or data exfiltration attempts, as adversaries often use non-standard ports to evade detection.
This rule detects activity associated with the DYNOWIPER malware. It identifies the malware through specific SHA256 file hashes, known filenames (dynacom_update.exe, source.exe, schtask.exe, dynacon_update.ps1, exp.ps1), and process names (dynacom_update.exe, source.exe, schtask.exe, powershell.exe). Additionally, it flags network connections to or from a set of known malicious IP addresses (185.200.177.10, 31.172.71.5, 193.200.17.163, 185.82.127.20, 72.62.35.76) which are associated with VPN logins or exfiltration reverse proxies used by DYNOWIPER.
This rule detects activity associated with the RenEngine campaign, which utilizes HijackLoader to deliver Lumma Stealer and ACR Stealer. It identifies malicious activity through three main methods: 1) Endpoint detection of known MD5 hashes associated with the malware, 2) Network traffic (proxy/DNS/netflow) to distribution sites used by the campaign, and 3) Network traffic (proxy/DNS/netflow) to known Lumma Stealer C2 domains.
Detects processes associated with Dohdoor malware making network connections to Cloudflare's DNS-over-HTTPS service for C2 domain resolution.
Matches published GRIMBOLT/SLAYSTYLE/BRICKSTORM hashes from RecoverPoint VM intrusions.
This rule detects potential port scanning activity from a single host. It identifies scenarios where a device attempts to connect to a large number of unique remote ports (20 or more) or a large number of unique remote IP addresses (25 or more) within a 10-minute window. This behavior is indicative of an adversary attempting to discover open ports or active hosts on a network.
This rule detects activities associated with UNC6201 exploitation of Dell RecoverPoint VM, specifically looking for three distinct phases: Tomcat Manager WAR file deployment, modification of a persistence script on Linux, and communication with a GRIMBOLT C2 endpoint. The first part identifies WAR file deployments via Tomcat Manager. The second part looks for modifications to a specific persistence script file. The third part identifies network connections to a known GRIMBOLT C2 IP address or URL pattern.
This rule detects Microsoft Teams chat or call activity initiated by external users whose domains are not present in a pre-defined whitelist. It specifically looks for 'ChatCreated' or 'CallParticipantDetail' actions from external users and filters out activity from known, whitelisted external domains. This can help identify potentially unauthorized or suspicious communication with external entities.
This rule detects network connections or email URLs that match a list of known Indicators of Compromise (IOCs) associated with Signal-targeted phishing infrastructure and explicit phishing destination domains. These IOCs were identified in Unit 42's research on QR code attack vectors. The rule queries email URL information, URL click events, device network events, and common security logs (proxy/firewall/WAF) to identify activity related to these malicious domains.
Detects web navigation to Amazon and AliExpress that contains specific hardcoded affiliate tags used by the 10xprofit extension network. The presence of these tags indicates active affiliate hijacking, likely due to a malicious browser extension.
Matches published CastleLoader stage hashes from ANY.RUN analysis.
This rule detects activities related to the DKnife gateway, which is associated with Adversary-in-the-Middle (AitM) attacks. It identifies the creation of specific DKnife process names (dknife.bin, sslmm.bin, postapi.bin, rules.bin, sshapi.bin, inotify.bin, curlapi.bin), file events involving DKnife configuration artifacts (rules.conf, sslmm.cfg, url.cfg), and the presence of specific marker strings ("dkimsi=", "460110672021628", "PASSWORD") in logs or captured payloads. The rule aims to identify the presence and activity of DKnife components on Linux systems.
This rule detects the presence of hardcoded tokens associated with the dYdX supply chain attack. These tokens were used for C2 authentication and as an API key for data exfiltration. The rule searches across process command lines, network URLs, network headers, and common security logs for these specific tokens.
Detects known file hashes and C2 domains associated with the Dohdoor malware campaign. This rule combines file and network indicators; it may need to be split into two separate rules (e.g., for file events and network events) depending on the SIEM implementation.
Detects legitimate Windows processes, known to be targeted by Dohdoor for process hollowing, making outbound network connections. This is highly indicative of the injected Dohdoor payload communicating with its C2 server.
This rule detects network connections or DNS queries to a list of domains identified as phishing campaign infrastructure. These domains are associated with a 'Signal Phishing Campaign' and are likely used for malicious purposes such as credential harvesting or malware distribution. The rule checks for these domains in `RemoteUrl` or `DestinationHost` fields across various network-related logs.
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