Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
172 detections
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The following analytic detects suspicious attempts to find credentials stored in password stores, indicating a potential attacker's effort to access sensitive login information. Password stores are critical repositories that contain valuable credentials, and unauthorized access to them can lead to significant security breaches. By monitoring for unusual or unauthorized activities related to password store access, this analytic helps identify potential credential theft attempts, allowing security teams to respond promptly and prevent unauthorized access to critical systems and data.
Detects potential Adversary-in-the-Middle (AiTM) phishing activity, commonly associated with frameworks like Evilginx. The rule identifies suspicious domain patterns where legitimate identity provider (IdP) domains are used as subdomains of another domain, or where IdP-related keywords are used in hyphenated subdomains.
This analytic detects the configuration of TFTP services on Cisco IOS devices that could be used to exfiltrate sensitive configuration files. Threat actors like Static Tundra have been observed configuring TFTP servers to make device configuration files accessible for exfiltration after gaining initial access. The detection specifically looks for commands that expose critical configuration files such as startup-config, running-config, and other sensitive system information through TFTP. This activity is particularly concerning as it may represent an attempt to steal credentials, network topology information, and other sensitive data stored in device configurations.
Detects multiple failed Multi-Factor Authentication (MFA) push attempts for a user from a single IP address, followed by a successful attempt within a short time window. This pattern can indicate an MFA push bombing or fatigue attack where an adversary spams a user with MFA requests until they approve one.
This rule detects suspicious Okta login attempts where a user successfully passes the initial authentication factor but then fails or abandons a subsequent Multi-Factor Authentication (MFA) challenge, originating from a new IP address, device (user agent), and city combination. It filters out events from known corporate VPN IP addresses to reduce false positives.
Detects successful user authentication using the FIDO2/WebAuthn cross-device (hybrid) sign-in feature. This technique can be abused by attackers in Adversary-in-the-Middle (AiTM) phishing campaigns to bypass FIDO key protections, as seen in the PoisonSeed campaign.
Detects the successful registration of a new FIDO2/WebAuthn security key for a user account. This activity can be legitimate, but attackers who have compromised an account may register their own FIDO key to maintain persistent access. This activity should be correlated with other suspicious events, such as a recent password reset or login from an unusual location.
This rule identifies 'improbable travel' scenarios where a user successfully logs in from two geographically distant locations within a short time frame, suggesting potential account compromise or abuse, possibly related to Remote Employment Fraud (REF). It calculates the speed of travel required between consecutive login locations and flags events where the speed and distance exceed predefined thresholds. It also checks if the login country differs from the user's known work country.
This analytic detects suspicious modifications to Unix shell configuration files, such as `.bashrc` or `.profile`, which may indicate an attempt to alter system behavior or gain unauthorized access. Unauthorized changes to these files can be used to execute malicious commands, escalate privileges, or hide malicious activities. By monitoring for unusual or unauthorized modifications to shell configuration files, this analytic helps identify potential security threats, allowing security teams to respond quickly and mitigate risks.
This rule detects Okta authentication attempts originating from known VPN providers. Threat actors, particularly those involved in Remote Employment Fraud (REF), often use VPNs to mask their true physical location and evade detection. By identifying connections from a listing of commonly used VPN services, this rule aims to flag potentially suspicious access to Okta accounts. The rule extracts user information, client details, geographical context, and debug data to provide comprehensive insights into the VPN usage.
The following analytic detects attempts to stop a service on Linux systems. It leverages data from Linux Auditd. This activity is significant as adversaries often stop or terminate security or critical services to disable defenses or disrupt operations, as seen in malware like Industroyer2. If confirmed malicious, this could lead to the disabling of security mechanisms, allowing attackers to persist, escalate privileges, or deploy destructive payloads, severely impacting system integrity and availability.
The following analytic detects the suspicious auditd service stop. This behavior is critical for a SOC to monitor because it may indicate attempts to gain unauthorized access or maintain control over a system. Such actions could be signs of malicious activity. If confirmed, this could lead to serious consequences, including a compromised system, unauthorized access to sensitive data, or even a wider breach affecting the entire network. Detecting and responding to these signs early is essential to prevent potential security incidents.
The analytic detects cloud API calls executed by user roles that have not previously run these commands. It leverages the Change data model in Splunk to identify commands executed by users with the user_type of AssumedRole and a status of success. This activity is significant because new commands from different user roles can indicate potential malicious activity or unauthorized actions. If confirmed malicious, this behavior could lead to unauthorized access, data breaches, or other damaging outcomes by exploiting new or unmonitored commands within the cloud environment.
The analytic detects the creation of cloud compute instances using previously unseen image IDs. It leverages cloud infrastructure logs to identify new image IDs that have not been observed before. This activity is significant because it may indicate unauthorized or suspicious activity, such as the deployment of malicious payloads or unauthorized access to sensitive information. If confirmed malicious, this could lead to data breaches, unauthorized access, or further compromise of the cloud environment. Immediate investigation is required to determine the legitimacy of the instance creation and to mitigate potential threats.
This rule detects user logins from multiple distinct states or countries within a one-hour window, leveraging GeoIP enrichment. It specifically looks for successful login events where a user logs in from at least two different states or at least one country, indicating potential 'impossible travel' or suspicious access patterns.
The following analytic identifies potential DLL side-loading instances involving unsigned DLLs mimicking Microsoft signatures. It detects this activity by analyzing Sysmon logs for Event Code 7, where both the `Image` and `ImageLoaded` paths do not match system directories like `system32`, `syswow64`, and `programfiles`. This behavior is significant as adversaries often exploit DLL side-loading to execute malicious code via legitimate processes. If confirmed malicious, this activity could allow attackers to execute arbitrary code, potentially leading to privilege escalation, persistence, and unauthorized access to sensitive information.
This analytic aggregates and summarizes alerts from Microsoft Defender for Endpoint. It extracts details such as source, file name, severity, process command line, IP address, registry key, signature, description, and unique ID. The detection is not intended to identify new activity from raw data but rather to leverage existing Microsoft Defender for Endpoint alerts for correlation and risk-based alerting. It filters out alerts with a 'clean' verdict and dynamically maps MITRE techniques and assigns risk scores based on the alert's severity.
The following analytic identifies modifications to Okta Identity Provider (IDP) lifecycle events, including creation, activation, deactivation, and deletion of IDP configurations. It uses OktaIm2 logs ingested via the Splunk Add-on for Okta Identity Cloud. Monitoring these events is crucial for maintaining the integrity and security of authentication mechanisms. Unauthorized or anomalous changes could indicate potential security breaches or misconfigurations. If confirmed malicious, attackers could manipulate authentication processes, potentially gaining unauthorized access or disrupting identity management systems.
The following analytic identifies when a new device is enrolled on an Okta account. It uses OktaIm2 logs ingested via the Splunk Add-on for Okta Identity Cloud to detect the creation of new device enrollments. This activity is significant as it may indicate a legitimate user settinging up a new device or an adversary adding a device to maintain unauthorized access. If confirmed malicious, this could lead to potential account takeover, unauthorized access, and persistent control over the compromised Okta account. Monitoring this behavior is crucial for detecting and mitigating unauthorized access attempts.
This correlation rule identifies when a user exceeds a predefined risk threshold based on multiple suspicious Okta activities within a 24-hour period. It aggregates risk events from "Suspicious Okta Activity," "Okta Account Takeover," and "Okta MFA Exhaustion" analytic stories. The detection is significant as it highlights potentially compromised user accounts exhibiting multiple tactics, techniques, and procedures (TTPs). If confirmed malicious, this activity could indicate a serious security breach, allowing attackers to gain unauthorized access, escalate privileges, or persist within the environment.
The following analytic detects attempts to disable multi-factor authentication (MFA) for an Azure AD user. It leverages Azure Active Directory AuditLogs to identify the "Disable Strong Authentication" operation. This activity is significant because disabling MFA can allow adversaries to maintain persistence using compromised accounts without raising suspicion. If confirmed malicious, this action could enable attackers to bypass an essential security control, potentially leading to unauthorized access and prolonged undetected presence in the environment.
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