Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects rapid failed local Windows account logon attempts consistent with Pony malware's built-in dictionary brute-force routine, which enumerates local accounts via NetUserEnum and cycles passwords via LogonUserA (advapi32). A high volume of type-2 interactive logon failures against multiple local accounts in a short window is the key behavioral signal.
Detects a browser session that contacts an EvilTokens Device Code phishing endpoint (/api/device/status/) and then immediately navigates to onedrive.live.com, indicating the attacker backend returned status:completed and the kit executed window.location.replace to redirect the victim to OneDrive after token capture. This sequence suggests a successful device code phishing attack where the victim's credentials or tokens have been captured, and they are then redirected to a legitimate service to avoid suspicion.
Detects a browser session that contacts an EvilTokens Device Code phishing endpoint (/api/device/status/) and then immediately navigates to onedrive.live.com, indicating the attacker backend returned status:completed and the kit executed window.location.replace to redirect the victim to OneDrive after token capture. This sequence suggests a successful device code phishing attack where the victim's credentials or tokens have been captured, and they are then redirected to a legitimate service to avoid suspicion.
This rule detects successful user sign-ins from anomalous IP addresses, indicative of an Adversary-in-the-Middle (AiTM) reverse proxy attack. It correlates successful logins with the receipt of a phishing email containing specific subject keywords ('Code of Conduct') within a two-hour window, suggesting that the login was a direct result of the phishing lure.
Detects Node.js executing the malicious setup.cjs script or easy-day-js with the --no-warnings flag, associated with the Mastra npm supply chain compromise.
Detects EvilTokens phishing kit gate check requests to /api/device/gate/ used to identify and burn phishing pages for security researchers
This rule detects outgoing network connections from the IIS worker process (w3wp.exe) to public-facing URLs that match common Microsoft Office WOPI (Web Application Open Platform Interface) request patterns, but are directed to domains other than trusted Microsoft-affiliated infrastructure. This behavior can be indicative of a Server-Side Request Forgery (SSRF) attempt, where an attacker tries to coerce the server into making unauthorized requests to external resources.
This rule monitors for the creation of .exe or .msi files within common user-writable directories (Downloads, Temp, AppData) initiated by web browser processes (Chrome, Edge, Firefox, Brave). This behavior is often indicative of drive-by downloads or users interacting with malicious web content that delivers installers to the host.
Identifies network connections to algorithmically generated Cloudflare Workers domains used by Kali365, specifically targeting the `-s-account.workers.dev` format. This pattern is indicative of phishing campaigns, potentially for credential harvesting or token replay.
This rule detects successful configuration modifications to diagnostic settings or storage services where the destination resource is identified as being outside of the organization's trusted subscriptions or expected storage naming conventions. This activity may indicate an adversary attempting to exfiltrate logs or data to an attacker-controlled storage account.
This rule detects potentially malicious software supply chain activity by monitoring for the execution of 'node-gyp' or 'node-pre-gyp' during the installation of Node.js packages (e.g., via npm or yarn). It specifically flags instances where the process or its parent involves suspicious file names or directory paths often associated with 'Miasma' supply chain campaigns, such as references to 'leo-logger', 'binding.gyp', or 'index.js' inside 'node_modules' directories.
This rule detects the execution of known remote access tools (such as AnyDesk, TeamViewer, or RustDesk) that are spawned as child processes from common web browsers. It correlates process creation events with subsequent network connections by these tools to public IP addresses over specific ports commonly used by remote access software, within a 30-minute timeframe of the process start.
Detects messages sent or chats created by external/guest users in Microsoft Teams where the sender's name or user ID impersonates IT or support personnel, indicating a potential phishing attempt.
This rule detects indicators of Adversary-in-the-Middle (AiTM) phishing infrastructure, specifically targeting Evilginx2 and similar proxy frameworks. It identifies unauthorized execution of Evilginx2 binaries, the presence of specific session and configuration files (.evilginx/, phishlets, session databases), unauthorized modifications to Nginx configurations, and the dropping of suspicious HTML lure files into web document roots by scripting interpreters.
This rule detects potentially malicious activity within Azure Automation by monitoring for the creation or modification of runbooks and jobs, combined with the presence of suspicious command-line or script patterns in job stream output, such as credential harvesting (e.g., Get-AutomationPSCredential) or execution of web-based payloads (e.g., IEX, Invoke-WebRequest).
Detects potential Adversary-in-the-Middle (AiTM) phishing activity by identifying successful user sign-ins from a new, historically unseen IP address occurring within 30 minutes of a legitimate sign-in from a known IP, indicating the potential replay of a stolen session or refresh token.
Detects modifications or publication of Azure Automation runbooks by users or service principals other than known, pre-approved automation service principals. This activity may indicate an unauthorized user or compromised service principal attempting to inject malicious code into automation workflows.
This rule detects successful OAuth logins originating from IP addresses identified as residential proxies. This behavior can indicate an adversary attempting to bypass geo-restrictions or obscure their true location, potentially as part of a credential stuffing attack, account takeover, or to evade detection.
This rule monitors for administrative VMware operations (cloning, snapshotting, or process manipulation) performed on sensitive infrastructure servers (such as Domain Controllers, PKI, Vault, or ADFS) that occur outside of a designated maintenance window by non-authorized accounts. It leverages tools like vim-cmd, esxcli, or govc to detect potential unauthorized VM manipulation that could lead to data theft, snapshot-based credential extraction, or unauthorized system changes.
Detects potential execution chains related to QR code phishing (Quishing) by monitoring for suspicious process creation or outbound network connections originating from document readers, office applications, and QR scanner utilities. It covers three primary vectors: suspicious process spawning (e.g., Office apps launching cmd or powershell), non-standard network connections from document readers, and malicious URL-based LNK files spawned via explorer.exe.
Detects the execution of known adversary-in-the-middle (AiTM) phishing frameworks, such as Evilginx or Modlishka, by identifying process names, paths, or specific command-line arguments (e.g., --phishlets, --proxy, --lure) used to initiate these proxy-based phishing sessions.
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