Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects network connections to known CountLoader C2 domains based on McAfee Labs analysis. This rule identifies outbound network traffic from devices to a predefined list of Command and Control (C2) domains associated with the CountLoader malware. The detection logic specifically looks for the presence of these domains in the 'RemoteUrl' field of network events.
Sublime Threat Intelligence and Research (STIR) identified a phishing campaign using a recently published open-source Javascript virtual machine and obfuscation tool called KrakVM. The initial KrakVM-encoded HTML file arrives as an attachment in phishing emails. These emails are short, often not even including a body, with subjects and HTML attachment filenames suggesting the target has a new voicemail, a vendor credit, or unpaid invoices
This rule detects potential command and control (C2) communications over non-standard ports. It identifies devices that make a high number of network connections to distinct remote IP addresses on ports other than common ones (80, 443, 53, 22, 21, 25, 110, 143, 993, 995) and not to private IP ranges. The rule aggregates connection counts and distinct remote IPs over 15-minute intervals and flags activity where there are more than 30 connections and more than 2 distinct remote IPs.
This rule detects a potential privilege escalation attempt on Linux systems. It looks for two main indicators: first, the execution of the 'su' command from a user's home directory by a process whose executable was recently created in a user's home directory and identified as an ELF binary. Second, it identifies systems where public-facing network connections were made by processes using common remote file transfer utilities (curl, wget, lftp, axel, aria2, scp, rsync, ftp) to fetch code, followed by the execution of a process with 'algif-aead' in its command line, which could indicate fileless execution after remote code fetching. The combination suggests an attacker might be fetching malicious code, potentially compiling it, and then using it to escalate privileges via 'su' or executing it filelessly.
For network requests to the GitHub API containing specific staging and fallback markers (LongLiveTheResistanceAgainstMachines or beautifulcastle) used by TeamPCP in the Bitwarden CLI campaign to retrieve C2 instructions.
This rule detects instances where MSBuild.exe initiates network connections to external IP addresses. MSBuild.exe is a legitimate Microsoft utility, but it can be abused by adversaries to proxy execution of malicious code and establish command and control (C2) communications. The rule filters out connections to internal, loopback, and non-routable IP spaces to focus on potentially malicious external communication attempts.
Detects the execution of common browser executables in headless mode with command-line arguments indicative of data exfiltration, malicious rendering (dump-dom), or external requests (http/data:text/html;base64,). This activity, especially when initiated by non-standard parent processes, can be a sign of automated web scraping, vulnerability scanning, or other malicious automated browser activity.
This rule detects network connections (both inbound and outbound) on TCP/UDP port 18789, excluding connections initiated by processes from well-known publishers like Microsoft, Apple, and Google. This port is not commonly used by legitimate applications and could indicate the presence of custom malware, command and control (C2) communication, or other unauthorized network activity.
This rule detects a suspicious attack chain, potentially related to 'ClickFix' or similar social engineering campaigns. It correlates multiple events within a 5-minute window: a browser being launched from an email client, a file download from a browser into the Downloads directory, and a command prompt being launched from explorer.exe executing a command that references the Downloads directory. Additionally, it looks for network connections initiated by common document types, which could indicate malicious document activity.
KQL Query
Detects Adobe Reader initiating unexpected network connections, which may indicate an exploit fetching a secondary payload or communicating with a C2 server.
Detects suspicious behavior associated with an Adobe Reader zero-day exploit, including specific network connections, suspicious user-agent strings, registry persistence, and specific image loads (e.g., ntdll.dll or bootsvc.dll).
Detects the execution of rundll32.exe with the oledb32.dll library to open a UDL file.
Threat actors can abuse this technique as a phishing vector to capture authentication credentials or other sensitive data.
Threat actors can abuse this technique as a phishing vector to capture authentication credentials or other sensitive data.
Detects unusual outbound network connections from ATM devices (identified by device names containing 'ATM', 'NCR', 'DIEBOLD', 'WINCOR'). The rule flags connections to public IP addresses, excluding common internal networks and standard web/DNS ports (80, 443, 53). Alerts are triggered if an ATM device shows a high number of connections, unique remote IPs, or significant data transfer, indicating potential compromise or unauthorized activity.
This rule detects suspicious activity across process events, file events, and network events by looking for specific SHA1/SHA256 hashes, command line arguments, folder paths, and remote URLs. The indicators suggest potential malware execution, file manipulation, or command and control communication.
This rule detects unusual network connections from processes to legitimate service endpoints that are commonly abused for Command and Control (C2) activities (LOLC2). It specifically looks for connections to services like Telegram, Slack, Discord, GitHub, Google APIs, Pastebin, Dropbox, and others, originating from processes that are not recognized as legitimate applications for those services, and not from common system processes or standard program file paths. This helps identify potential adversary communication channels masquerading as legitimate traffic.
Detects the installation or execution of compromised npm packages related to the Axios supply chain attack, with execution or reference to the dropped macOS RAT payload.
This rule detects network connections from devices to known malicious infrastructure, specifically the domain 'sfrclak.com' or the IP address '142.11.206.73'. These indicators are associated with potential compromise or command and control activity.
Detects installation and post-installation activity associated with OpenClaw and its known variants — an open-source AI agent family classified as a Potentially Unwanted Application (PUA). Monitors for npm global install commands, execution of shell installer scripts, OpenClaw-related binary or package files written to disk, and outbound network activity originating from known OpenClaw agent processes. Covers 33 known variants of the OpenClaw family including Clawdbot, Moltbot, Ironclaw, MalClaw, and others.
This rule detects unusual network connections from processes to legitimate service endpoints that are commonly abused for Command and Control (C2) activities (LOLC2). It specifically looks for connections to services like Telegram, Slack, Discord, GitHub, Google APIs, Pastebin, Dropbox, and others, originating from processes that are not recognized as legitimate applications for those services, and not from common system processes or standard program file paths. This helps identify potential adversary communication channels masquerading as legitimate traffic.
This rule detects unusual network connections from processes to legitimate service endpoints that are commonly abused for Command and Control (C2) activities (LOLC2). It specifically looks for connections to services like Telegram, Slack, Discord, GitHub, Google APIs, Pastebin, Dropbox, and others, originating from processes that are not recognized as legitimate applications for those services, and not from common system processes or standard program file paths. This helps identify potential adversary communication channels masquerading as legitimate traffic.
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