Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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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.
Detects a specific sign-in sequence indicative of a Payroll Pirate adversary-in-the-middle (AiTM) attack. The pattern involves a Microsoft Outlook interactive sign-in resulting in a 50199 error (Login:reprocess) followed within 10 minutes by a Cmsi:cmsi authentication attempt, both originating from the same user and the specific user-agent Firefox 142.0. This behavior is associated with 2FA proxying techniques where the attacker captures and replays session information.
Detects AMOS malware presenting a fake System Preferences password dialog via osascript to harvest user credentials, followed by validation using dscl -authonly. This is a key technique in the macOS ClickFix campaign delivering Atomic macOS Stealer.
Detects instances where a user successfully authenticates using Multi-Factor Authentication (MFA) from one location, followed by a Single-Factor Authentication (SFA) logon from a different location within a 10-minute window. This behavior is indicative of potential session hijacking or session cookie theft where an attacker reuses an authenticated session or manipulates the authentication flow to bypass MFA requirements.
This rule identifies potential session hijacking or session replay attacks by detecting successful logins from the same user account using the same User-Agent string but originating from different source IP addresses within a one-hour window. This behavior is indicative of an attacker replaying stolen session cookies, effectively bypassing Multi-Factor Authentication (MFA) requirements associated with initial authentication.
Identifies anomalous session cookie replays where a user token is suddenly used from an entirely different geographic location or ISP within minutes. This rule specifically looks for non-interactive sign-ins (indicating session cookie reuse) where a single AccountUpn shows sign-ins from at least two different IP addresses and two different geographic locations within a 10-minute window.
Detects instances where a Node.js process accesses, modifies, or creates files within web browser extension directories. This behavior is indicative of unauthorized access to sensitive browser data, such as extension configurations, authentication tokens, or stored secrets.
This rule detects processes other than standard web browsers (Chrome, Edge, Brave) attempting to access sensitive directories within Chrome extension user data paths, such as IndexedDB, Local Storage, or Sync Data. Such activity is often indicative of credential or session cookie theft by malicious scripts or malware.
Detects outbound network traffic from browser processes to known BadBlocker extension command-and-control infrastructure (api.adblock-for-youtube.com or get.adblock-for-youtube.com), followed by subsequent outbound HTTPS connections to arbitrary external domains. This sequence is indicative of malicious browser extension activity involving the exfiltration of session cookies or authentication tokens from the victim's browser.
Detects anomalous browser activity where an endpoint retrieves configuration or scriptlets from the BadBlocker-related API, followed shortly by outbound network connections to suspicious domains or the exfiltration of sensitive browser-based session data like document.cookie, localStorage, or sessionStorage.
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.
Detects unauthorized processes attempting to access browser cookie storage files, which are commonly targeted by infostealer malware to extract session cookies for services like Jira and Atlassian SSO. Extracting these tokens allows attackers to bypass multi-factor authentication (MFA) and gain unauthorized access to accounts.
Detects the LokiBot information stealer by identifying its characteristic aPLib compressed payloads, specific C2 URI patterns, known C2 infrastructure, and credential theft routines targeting applications like 1Password, Keepass, and WinBox.
Detects instances where processes not associated with standard web browsers (e.g., node.exe) attempt to create, modify, or rename browser-related sensitive files, specifically cookie databases and DPAPI-protected Local State files. Such activity is often indicative of credential and session cookie theft.
Slack Anomalous Activity Detected
Cortex XDR
This rule detects anomalous activity reported by Slack's internal anomaly detection system. This includes events such as impossible travel, new device access, suspicious IP changes, and other behavioral anomalies that could indicate a compromised account or unauthorized access to a Slack workspace.
Slack Anomalous Activity Detected
Cortex XDR
This rule detects anomalous activity reported by Slack's internal anomaly detection system. This includes events such as impossible travel, new device access, suspicious IP changes, and other behavioral anomalies that could indicate a compromised account or unauthorized access to a Slack workspace.
Detects the 'Adblock for YouTube' browser extension (specific ID cmedhionkhpnakcndndgjdbohmhepckk) performing suspicious file system operations related to the injection of remotely-fetched scriptlets into the browser's MAIN world. This activity is indicative of an attempt to bypass browser security boundaries to access session cookies, local/session storage, and login form data.
Detects unauthorized access to Chrome and Firefox browser credential (Login Data, logins.json, key4.db) and cookie database files on macOS by non-browser processes, commonly observed in credential theft campaigns like ClickFix.
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.
Detects instances where non-browser processes access sensitive browser-related files (Login Data, Cookies, or Local State) located within AppData directories. This behavior is highly indicative of credential and session token theft, often associated with AiTM or post-compromise information harvesting activities.
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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