Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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This rule detects non-browser processes that simultaneously access a web browser's 'Local State' file and the Windows DPAPI master key storage directory. This behavior is a common precursor to credential dumping, as adversaries must decrypt browser-protected secrets (passwords and cookies) using DPAPI keys stored in the user's profile.
Detects instances where a user account that has been flagged as risky (e.g., unfamiliar features, impossible travel, or malicious IP activity) performs a modification or registration of multi-factor authentication (MFA) security information within a short timeframe (30 minutes). This behavior is highly indicative of an adversary attempting to establish persistence or bypass authentication controls after a credential compromise.
This rule detects potential account compromise by identifying 'impossible travel' scenarios where successful logins for the same user occur from different geolocations within a short timeframe (less than 1 hour), specifically when at least one authentication event uses a refresh or session token rather than interactive MFA. It correlates these suspicious logins with previous endpoint infostealer detections on the same device within the last 72 hours, indicating that the token may have been stolen by malware.
Detects instances where a risky Azure AD sign-in (e.g., token replay or confirmed compromise) is closely followed (within 24 hours) by sensitive application or service principal credential modifications by the same user account. This behavior is indicative of potential persistence establishment via account manipulation.
Detects the addition of new credentials (certificates, secrets, or keys) to Azure AD Service Principals or Applications, occurring outside of standard business hours (6 AM to 8 PM). This pattern is often indicative of an adversary establishing or maintaining persistent access to an enterprise cloud environment.
Detects the addition of new credentials (certificates, secrets, or keys) to Azure AD Service Principals or Applications, occurring outside of standard business hours (6 AM to 8 PM). This pattern is often indicative of an adversary establishing or maintaining persistent access to an enterprise cloud environment.
Detects unauthorized access to sensitive Chromium-based browser files such as 'Login Data' (password database) or 'Local State' (encryption keys) by processes other than standard web browsers (Chrome, Edge, Brave, Firefox). The rule also elevates risk if the access is followed by the execution of known data-handling tools like sqlite, python, or powershell, which are frequently used by information stealers to parse and exfiltrate browser-stored credentials.
Detects potential infostealer activity where an unsigned or non-system process accesses the Windows DPAPI master key store followed by access to common browser credential and cookie storage files within a 10-minute window. This behavior is indicative of malware attempting to decrypt and exfiltrate saved browser passwords and cookies.
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 addition of new client secrets or certificates to existing applications or service principals in Azure AD. This activity is a common method for adversaries to establish persistence in a cloud environment by ensuring they maintain MFA-exempt application-level access even if compromised user accounts or session cookies expire.
Detects non-interactive sign-in events in Entra ID that occur without a preceding interactive sign-in session on the same device that successfully satisfied a multi-factor authentication (MFA) challenge. This behavior is indicative of token replay attacks or session hijacking where an adversary attempts to use harvested session cookies or tokens to access resources.
Detects unauthorized access to common browser credential storage files (Login Data, Cookies, Web Data, Local State) by processes other than standard web browsers. This rule identifies potentially malicious activity by filtering for rare or unsigned binaries executing from common staging directories (Temp, Downloads) or accessing browser files shortly after the process launch, which is a common behavior pattern for info-stealer malware.
Detects successful authentication attempts using legacy protocols or non-interactive refresh tokens for user accounts previously flagged within a 14-day window as being involved in infostealer or credential exposure events.
Detects attempts to enumerate or access Windows Credential Manager and vault files, often used by adversaries to extract cached credentials. The rule monitors for execution of vaultcmd.exe, invocation of keymgr.dll via rundll32.exe, and unauthorized access to credential storage paths in AppData by non-system processes.
This rule detects potential session hijacking by monitoring for the reuse of session or refresh tokens from unrecognized devices, browsers, or user agents, particularly when such activity occurs without an interactive MFA challenge. This behavior is indicative of an attacker attempting to use stolen session cookies or tokens to access cloud resources.
Detects processes that access multiple categories of sensitive data (browser autofill, cryptocurrency wallets, password manager exports, or system fingerprinting files) within a short time window. This behavior is highly indicative of modular infostealers or data-collection malware conducting 'stacking access' to stage sensitive information for exfiltration.
This rule detects unauthorized access, creation, or modification of Git credential files (.git-credentials, .netrc) by processes other than standard Git credential management utilities. It also monitors command-line activity that references these credential stores to identify potential attempts to exfiltrate or manipulate stored credentials.
Detects Node.js or Python processes exhibiting behavior characteristic of information-stealing malware (such as the WaterPlum suite: BeaverTail, InvisibleFerret, OtterCookie, StoatWaffle). The rule monitors for these processes accessing sensitive files related to browser credentials, cryptocurrency wallets, scanned identification documents, or performing collection activities like keylogging and screen capturing.
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 potential Adversary-in-the-Middle (AiTM) activity by identifying successful multi-factor authenticated sign-in events from the same user across different geographic regions and IP addresses within a one-hour timeframe, which is characteristic of session cookie replay attacks.
This rule detects activities indicative of an attacker attempting to establish an adversary-in-the-middle (AiTM) position within an on-premises network. It flags potential NTLM relay attempts (via loopback or invalid workstation indicators) and unauthorized name resolution spoofing (LLMNR/NBT-NS) used to intercept and relay authentication traffic for MFA bypass or credential theft.
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