Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects the use of 'findstr' or PowerShell to query sensitive configuration files (such as web.config or applicationHost.config) for credential-related keywords like 'connectionString', 'AccountKey', or 'machineKey'. This behavior is indicative of an adversary attempting to harvest credentials or sensitive configuration data stored in cleartext on the filesystem.
Detects the execution of known Python scripts (CES_Enroll.py, ntlm_ces_relay.py, ces_negotiate_ntlm.py) often associated with NTLM relay attacks or forced authentication techniques. These scripts leverage Python to interact with authentication protocols or relay requests.
Detects unauthorized abuse of the OAuth 2.0 device authorization grant flow in Salesforce environments, commonly used by threat actors like ShinyHunters (UNC6240) to gain programmatic access. Attackers register malicious connected apps to impersonate legitimate tools, then use social engineering (vishing) to trick victims into approving device codes. This rule flags token issuance from non-allowlisted connected apps using the device_code grant type.
Detects an suspicious progression of activities originating from identified AI assistant/agent processes. The rule monitors for a chain of behaviors occurring within a 15-minute window: execution of an AI agent/tool, followed by host discovery (network/system enumeration), access to sensitive local credential files, archival of data, and outbound network communication to common file-sharing/exfiltration platforms.
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 Node.js or ProfileQuickHost processes performing file operations (read, write, rename) on sensitive browser directories, such as 'Local Extension Settings' or wallet-related folders. This pattern is commonly observed in credential-stealing malware attempting to extract browser-stored secrets or cryptocurrency wallet data.
Detects account modification activity (e.g., password resets or MFA registration changes) followed within 24 hours by a sign-in event for the same user account that is flagged as risky or exhibits anomalous device/location characteristics, indicative of social engineering campaigns like Scattered Spider.
This rule detects potential identity-led lateral movement in two scenarios: 1) A single account performing RDP authentications to multiple distinct hosts within a short time frame (RDP Fan-Out), or 2) An RDP login occurring on a host shortly after evidence of LSASS process memory access on that same host, suggesting potential credential theft followed by immediate reuse.
This rule detects when new credentials (secrets or certificates) are added to an Azure Active Directory Service Principal. It flags this activity as suspicious if it occurs outside of typical business hours or if it exhibits signs of 'rapid re-keying', where multiple credentials are added to the same service principal within a 24-hour window. This behavior is a common indicator of persistence maintenance or credential rotation to support unauthorized access.
Detects high-risk activities related to SAML token-signing certificates, including certificate export from Active Directory Federation Services (AD FS) or Active Directory Certificate Services (AD CS) and unauthorized configuration changes to enterprise application certificate management. These activities are potential precursors to Golden SAML attacks, where an adversary attempts to forge authentication tokens.
Detects unauthorized processes attempting to read or create sensitive browser credential files ('Login Data', 'Web Data', 'Cookies', 'logins.json', 'key4.db'). This activity is commonly associated with information-stealing malware (such as Lumma, StealC, Vidar, RedLine, and Amadey) that targets browser databases to extract stored secrets, often bypassing standard browser process access.
Detects unauthorized processes attempting to read or create sensitive browser credential files ('Login Data', 'Web Data', 'Cookies', 'logins.json', 'key4.db'). This activity is commonly associated with information-stealing malware (such as Lumma, StealC, Vidar, RedLine, and Amadey) that targets browser databases to extract stored secrets, often bypassing standard browser process access.
This rule detects scenarios where a user account grants high-privilege permissions (e.g., mail access or file modification) to an OAuth application within a short time frame (2 hours) following a sign-in event flagged as risky by Entra ID (Azure AD). This pattern is indicative of an adversary abusing OAuth consent flows as a persistence and credential-access mechanism following account compromise.
Detects instances where a process that is not a recognized web browser (chrome, msedge, firefox, brave, opera, or explorer) accesses multiple unique browser cookie files. This is a common pattern for credential harvesting malware attempting to steal browser sessions.
Detects access to sensitive browser credential and cookie files by processes that are not recognized web browsers or their designated update components. This behavior is highly indicative of credential theft activity where an adversary is attempting to extract stored credentials or session cookies from local browser storage.
This rule detects potential Adversary-in-the-Middle (AiTM) phishing activity by identifying session token or cookie reuse. It monitors for instances where a valid authentication session is established from one IP address and then immediately used again by the same user with the same session ID from a different IP address and potentially a different User-Agent, indicating that an attacker has hijacked and replayed the MFA-satisfied session.
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 unauthorized access attempts to Chromium-based browser credential storage files, specifically 'Login Data' (SQLite database) and 'Local State' (master key storage), by processes other than standard browsers or known security products. This activity is a common indicator of credential harvesting by information stealers such as LummaC2, Vidar, and RedLine.
Detects AWS CloudTrail events where an actor performs identity or session token manipulation, specifically through the use of sts:GetFederationToken or sts:AssumeRole, or by modifying MFA configurations such as DeactivateMFADevice or CreateVirtualMFADevice on privileged accounts. This activity may indicate an attempt to gain persistent access, bypass security controls, or masquerade as a legitimate principal.
Detects the addition of a new client secret or certificate to an application or service principal by a user account. This activity is correlated with recent high-risk or suspicious sign-in events. Adversaries often perform this action following account takeover to establish durable, MFA-immune persistence in the cloud tenant.
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.
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