Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
280 detections
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Detects the reuse of a single authenticated session or refresh token from multiple distinct IP addresses or ASN origins within a short timeframe following a successful MFA event. This pattern is indicative of session hijacking, where an adversary uses an Adversary-in-the-Middle (AiTM) toolkit (e.g., Evilginx) to capture a valid session cookie from a victim and replay it from their own infrastructure to bypass authentication.
Detects successful sign-in events flagged by the identity provider with risk indicators suggesting session token replay or theft. This includes anomalies such as unexpected token issuers, suspicious browsers, or unfamiliar device features, which are characteristic of session cookie theft and replay attacks.
Detects potential Adversary-in-the-Middle (AiTM) session token theft by monitoring for an initial interactive sign-in followed shortly by a non-interactive token refresh from a different IP address and browser fingerprint without a multi-factor authentication (MFA) challenge. This behavior is indicative of an adversary replaying captured session cookies via reverse-proxy infrastructure.
Detects potential Adversary-in-the-Middle (AiTM) session token theft by monitoring for an initial interactive sign-in followed shortly by a non-interactive token refresh from a different IP address and browser fingerprint without a multi-factor authentication (MFA) challenge. This behavior is indicative of an adversary replaying captured session cookies via reverse-proxy infrastructure.
Detects web browser processes (e.g., Chrome, Edge, Firefox) spawned by parent processes that are not typical for web browser execution. This behavior is often associated with malware (such as infostealers) using unconventional process spawning to inject code or perform credential theft.
Detects unauthorized access to critical Chromium-based browser credential databases (such as Login Data, Cookies, and History) by processes other than recognized browser executables. This behavior is indicative of credential-harvesting activity, specifically matching techniques employed by infostealers like RevStealer.
Detects the use of anomalous session tokens in Azure Active Directory (Azure AD) sign-in events where the token is being used in a non-interactive manner without a corresponding interactive authentication event. This behavior is indicative of token theft or infostealer activity, where an adversary replays a stolen session cookie from a new, untrusted, or non-compliant device to bypass authentication protections.
Detects instances where common browser credential and cookie storage files (Login Data, Cookies) for Google Chrome or Microsoft Edge are accessed by non-browser processes such as PowerShell, csc.exe, or the Search Indexer. This behavior is highly indicative of credential theft or session hijacking attempts.
Detects Azure AD sign-in events indicative of a session token replay attack. The rule identifies successful sign-ins where the traffic source originates from known residential proxy infrastructure (anonymizing services often used by attackers) and the authentication process lacks a fresh interactive MFA challenge, suggesting the use of a stolen, valid session token.
Detects unauthorized processes attempting to access sensitive browser files, including Login Data (passwords) and Network/Cookies databases for Chrome, Edge, and Firefox. These files are primary targets for credential dumping and session hijacking.
Detects unauthorized processes (non-browser applications) creating, modifying, or renaming sensitive browser data files such as Login Data, Cookies, or local state files within standard browser profile directories. This activity is indicative of credential harvesting or session hijacking attempts by malicious software.
Detects anomalous authentication patterns in Microsoft 365 logs where a valid session is utilized across multiple distinct devices within a short timeframe (under 5 minutes) without a corresponding multi-factor authentication (MFA) challenge. This behavior is indicative of a session token replay attack, where an adversary uses a stolen session cookie to bypass MFA and access cloud services.
Detects the creation of specific staging files and directories associated with the Rapuncel infostealer (e.g., UserInformation.txt, installed_applications.txt, sends.log, browser_decryption.log, or the Filegraber directory) within Windows temporary storage paths. This activity typically occurs following the unauthorized collection of sensitive data such as credentials, digital wallet information, and messenger session data.
This rule detects network traffic indicative of N0va Phishkit verification or session initialization. It looks for specific GET request URI parameters (/api/verification/init?session=, flow=, prompt_profile=) that align with the initial handshake phase of this phishing kit, which is often used for credential and session cookie theft.
Detects potential Adversary-in-the-Middle (AiTM) phishing attacks by identifying an initial legitimate sign-in followed by a second sign-in from a different IP address within a short timeframe (indicative of session token replay), immediately succeeded by high-risk post-compromise actions such as OAuth application consent or the creation of malicious mailbox rules.
Detects network communication to specific Gusercontent endpoints consistent with malicious browser extensions exfiltrating captured session tokens or cookies. The rule monitors for POST requests containing parameters like leadId, email, and data, often used to transmit stolen authentication material.
This rule detects non-browser processes attempting to access sensitive web browser credential files, such as 'Login Data', 'logins.json', and 'key4.db'. Such activity is highly characteristic of credential-stealing malware (including BabylonRAT) attempting to exfiltrate stored passwords or session data from Google Chrome or Mozilla Firefox profiles.
Detects network activity and HTTP request headers associated with the GhostCode phishing kit. The rule identifies specific URI patterns, custom site keys, malicious User-Agent strings, and session cookie artifacts indicative of interaction with a phishing server.
This rule detects network traffic indicative of N0va Phishkit verification or session initialization. It looks for specific GET request URI parameters (/api/verification/init?session=, flow=, prompt_profile=) that align with the initial handshake phase of this phishing kit, which is often used for credential and session cookie theft.
This rule detects network traffic indicative of N0va Phishkit verification or session initialization. It looks for specific GET request URI parameters (/api/verification/init?session=, flow=, prompt_profile=) that align with the initial handshake phase of this phishing kit, which is often used for credential and session cookie theft.
This rule detects potential Adversary-in-the-Middle (AiTM) phishing attacks targeting Microsoft 365 authentication flows. It identifies suspicious proxy traffic that contains authentication-related URIs or session-related cookies (such as ESTSAUTHPERSISTENT or refresh tokens) originating from hosts other than recognized Microsoft domains. This behavior is indicative of an attacker using a reverse proxy, such as Evilginx2, to intercept user credentials and session cookies in real-time.
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