Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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This rule detects suspicious code search queries within Azure DevOps audit logs that may indicate an adversary is attempting to find sensitive information such as passwords, API keys, tokens, or private keys. It specifically looks for keywords like 'password', 'secret', 'token', 'apikey', 'api_key', 'connectionstring', 'connstring', 'credential', 'private key', and 'BEGIN RSA PRIVATE KEY' in code search queries.
Detects the use of the GNU Debugger (GDB) to dump memory from a live process. This technique is used by frameworks like ShadowHS to extract credentials and other secrets directly from memory, bypassing file-based security controls.
This rule detects suspicious network connections to known threat actor infrastructure associated with FortiGate exploitation, successful administrative or VPN logins to FortiGate devices from these IPs, and DCSync-style directory replication access (EventCode 4662) on Windows systems. The rule is composed of three distinct parts targeting different aspects of the attack chain.
Detects potential JSON Web Token (JWT) authentication bypass attempts where the algorithm in the token header is explicitly set to "none". A vulnerable server might skip signature validation for such tokens, allowing an attacker to forge arbitrary claims and potentially gain unauthorized access or escalate privileges.
Detects two persistence techniques used by EncystPHP: the creation of backdoored root-level accounts and the injection of SSH keys into user accounts. Specifically, it looks for 'useradd' commands creating accounts with UID 0 and specific generic usernames, and modifications to '.ssh/authorized_keys' files by non-root/non-sshd processes.
Detects processes attempting to read or copy the Apple Keychain database file (login.keychain-db). Unauthorized access to this file is a strong indicator of credential theft activity, as seen with stealers like AMOS.
Detects a process requesting read access (GrantedAccess 0x1010) to the LSASS process memory (lsass.exe). This technique is commonly used by threat actors to dump credentials for lateral movement and ransomware deployment. The rule specifically looks for the GrantedAccess value '0x1010', which corresponds to PROCESS_VM_READ and PROCESS_QUERY_INFORMATION access rights, and excludes a list of known legitimate processes.
Detects the execution of 'osascript' to display a deceptive system dialog box mimicking a 'System Preferences' password prompt. This technique is used by malware like the Atomic macOS Stealer (AMOS) to trick users into entering their administrative password for credential theft.
This rule detects potential brute force login attempts against Windows systems by monitoring Windows Security Event ID 4625 (failed logon attempts). It aggregates failed attempts by IP address and computer within a 15-minute window and triggers an alert if the number of failed attempts exceeds a defined threshold (default 10). This indicates an adversary systematically guessing passwords to gain unauthorized access.
This rule detects password spraying attacks where a single IP address attempts to authenticate against multiple unique user accounts within a specified time window, resulting in failed sign-in attempts. It specifically looks for a high number of unique users failing authentication from the same IP, which is characteristic of password spraying to avoid account lockouts.
This rule detects high-volume access to Azure Key Vault secrets, keys, or certificates within a one-hour window. It specifically looks for successful read operations on secrets, keys, and certificates, as well as modifications to access policies. A high volume (more than 10 accesses) from a single caller within an hour is flagged as potential credential harvesting.
Detects a Node.js process accessing sensitive files such as environment configuration files (.env) or browser credential/session stores. This behavior was observed in a developer-targeting campaign where attackers exfiltrated secrets from compromised developer machines.
This rule detects the presence of hardcoded tokens associated with the dYdX supply chain attack. These tokens were used for C2 authentication and as an API key for data exfiltration. The rule searches across process command lines, network URLs, network headers, and common security logs for these specific tokens.
Detects the execution of osascript to display a deceptive dialog box mimicking 'System Preferences' to harvest user passwords. This technique was observed being used by the Atomic Stealer (AMOS) malware.
Identifies successful Entra ID sign-ins using the Device Code Flow from a geographic location not seen for that user in the last 30 days. The device code flow involves two separate interactions (the attacker's device and the victim's browser), so a mismatch between the sign-in location and the user's established locations is a strong indicator of a session hijacking attempt.
Detects a suspicious sequence where a new credential (PAT, SSH key, or OAuth authorization) is created and then used to establish durable persistence via webhooks or deploy keys. This behavior typically occurs after an account compromise as an attacker attempts to "lock in" access that survives credential rotation and provides real-time exfiltration of repository data.
Multi-layered detection rule that identifies anomalous behavior on privileged accounts across six independent detection layers: absolute rule violations ("never actions"), self-calibrating ratio-based thresholds, behavioral technique substitution (e.g., WMI drop-off while other lateral movement continues), time-series statistical anomaly detection via series_decompose_anomalies(), reconnaissance burst detection on LDAP/directory queries, and MFA gap analysis. All layers roll up into a composite risk score for analyst prioritization. Originally derived from diffpatterns() analysis of real penetration test data vs. normal operations, this community edition replaces environment-specific hardcoded thresholds with self-calibrating statistical baselines. Requires Microsoft Defender for Identity and Entra ID Sign-in Logs.
Detects a sequence of events where an AWS IAM Policy is modified or created and then immediately attached to a principal (user, role, or group) within a short time window (10 minutes). This behavior is highly suspicious and often indicates a rapid privilege escalation attempt or a lateral movement step where an attacker grants themselves new permissions for immediate use.
Detects the addition of an SSH public key to a GitHub account using programmatic access (Personal Access Tokens, OAuth, or GitHub Apps). Unlike keys added via the Web UI, programmatic creation often bypasses manual verification steps and provides long-lived access that survives password resets and MFA changes. This is a common technique for attackers to establish persistent command line access to repositories.
This rule detects attempts to access specific Active Directory object types (identified by their GUIDs) with an access mask of 0x100 (which typically indicates 'Write Property' access). It filters out common service accounts and system accounts to reduce noise.
This rule detects sign-ins by Azure Managed Identities where the token was likely obtained via the Instance Metadata Service (IMDS). It identifies these sign-ins by looking for 'ImdsIdentityProvider' in the UserAgent or 'Imds' in the ClientCredentialType within AADManagedIdentitySignInLogs. This activity could indicate lateral movement through abuse of managed identities attached to virtual machines.
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