Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects network traffic associated with Adversary-in-the-Middle (AiTM) phishing kits that impersonate Microsoft and Google login pages. The rules identify the presence of specific HTML elements (like Cloudflare Turnstile anti-bot gates) and authentication session cookie headers served from non-authorized/non-official domains, indicating a reverse-proxy phishing attack.
Detects unauthorized processes attempting to access sensitive browser profile files, such as login credentials, cookies, and session state files. This behavior is indicative of credential harvesting or session theft by malicious software.
This rule detects potential browser-based credential and session theft by monitoring for a process that accesses multiple sensitive web browser artifacts (cookies, local state, session storage) across several user profiles in a short timeframe. It correlates this activity with the subsequent creation of a compressed archive in a temporary directory followed by an outbound network connection, indicating a multi-stage exfiltration workflow.
Detects a single process reading browser cookie SQLite database files across multiple distinct browser profile directories or different web browsers within a short time window. This behavior is indicative of infostealer activity attempting to harvest session cookies for session hijacking and MFA bypass.
This rule detects potential session hijacking by correlating endpoint-based detections of known infostealer malware (e.g., Vidar, Lumma, Redline) with a subsequent cloud Identity Provider (IdP) login for the same user. The correlation specifically focuses on instances where the IdP authentication originates from an IP address different from the infected host, suggesting the use of stolen session cookies by an external actor to access corporate resources.
Detects successful Azure AD sign-in events that are flagged with a medium or high risk level and identified as anomalous by Azure AD Identity Protection. The specific combination of successful authentication alongside risk details like unfamiliar features, anonymized IP addresses, or malicious IP addresses is highly indicative of potential cloud account takeover, potentially following session token theft or infostealer activity.
Detects a multi-stage activity chain indicative of infostealer malware, including reading the browser 'Local State' file (containing the master key), accessing a browser process (likely for memory dumping or token extraction), and initiating an immediate outbound network connection to an external destination.
Detects network traffic associated with Adversary-in-the-Middle (AiTM) phishing kits that impersonate Microsoft and Google login pages. The rules identify the presence of specific HTML elements (like Cloudflare Turnstile anti-bot gates) and authentication session cookie headers served from non-authorized/non-official domains, indicating a reverse-proxy phishing attack.
Detects potential browser-based infostealer activity where a non-standard process, launched from temporary or user-download directories, accesses sensitive browser data files (cookies, login data) followed by a suspicious outbound network connection to a previously unseen domain, matching common patterns for LummaStealer or Vidar malware.
Detects HTTP traffic from the Psychedelic Stealer malware as it exfiltrates stolen browser passwords and session tokens to a specific command-and-control endpoint, and identifies the staging of Chromium Web Data files ('wd_tmp.db') within HTTP POST requests.
Detects potential Adversary-in-the-Middle (AiTM) activity by identifying a successful interactive MFA-protected sign-in followed within 15 minutes by a non-interactive (refresh token) sign-in for the same user from a different IP address, ASN, or User-Agent. This pattern is indicative of session hijacking where an attacker reuses a stolen session token.
Detects outbound HTTP GET requests containing an 'auth=' parameter with a long string of alphanumeric characters, characteristic of an OAuth token exfiltration attempt by a malicious Twitch extension (JeetBot). The rule flags requests to external destinations that are not official Twitch, Twitch Video Network, or JTV domains.
Detects unauthorized processes (excluding firefox.exe and explorer.exe) accessing or modifying sensitive Firefox browser profile files, specifically places.sqlite (history/bookmarks) and cookies.sqlite (session cookies). This activity is often indicative of credential or session hijacking attempts by malware or malicious scripts.
This rule detects potential browser-based credential and session theft by monitoring for a process that accesses multiple sensitive web browser artifacts (cookies, local state, session storage) across several user profiles in a short timeframe. It correlates this activity with the subsequent creation of a compressed archive in a temporary directory followed by an outbound network connection, indicating a multi-stage exfiltration workflow.
This rule detects potential browser-based credential and session theft by monitoring for a process that accesses multiple sensitive web browser artifacts (cookies, local state, session storage) across several user profiles in a short timeframe. It correlates this activity with the subsequent creation of a compressed archive in a temporary directory followed by an outbound network connection, indicating a multi-stage exfiltration workflow.
This rule detects potential browser-based credential and session theft by monitoring for a process that accesses multiple sensitive web browser artifacts (cookies, local state, session storage) across several user profiles in a short timeframe. It correlates this activity with the subsequent creation of a compressed archive in a temporary directory followed by an outbound network connection, indicating a multi-stage exfiltration workflow.
This rule detects a multi-stage attack chain where a device shows signs of credential theft (accessing browser credential stores or cookies) followed closely by a successful cloud authentication from that same user using a device identifier not previously observed in the last 30 days.
Detects unauthorized access by a non-browser process to Chromium-based 'Cookies' SQLite database files, which are used by browsers like Chrome, Edge, and Brave. This behavior is a strong indicator of credential theft or session hijacking, as attackers target these files to extract session cookies and bypass MFA for cloud services.
Detects the ChainScript RAT, executing via a masqueraded Node.js process, enumerating Local Extension Settings for browser wallet plugins. This reconnaissance activity is a precursor to exfiltrating crypto wallet data.
Detects the reuse of session tokens from distinct network locations, devices, or user agents shortly after a successful MFA-protected login. This behavioral pattern is a primary indicator of Adversary-in-the-Middle (AiTM) phishing, where attackers capture session tokens via reverse proxy to bypass authentication controls.
Detects Azure AD sign-in events where the user-agent matches common scripting or automation libraries such as python-requests. This behavior is often indicative of automated token replay attacks, where attackers attempt to leverage stolen session cookies or tokens (e.g., following AiTM phishing) via proxy infrastructure.
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