Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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This rule identifies risky sign-in attempts in Azure Active Directory where the sign-in originates from an IP address that has not been seen before or has been seen fewer than 3 times in the last 29 days for that specific user. It filters out guest users, non-compliant devices, and unmanaged devices, focusing on sign-ins with an aggregated risk level greater than 1 and a sign-in risk level of 50 or higher. Excluded applications are specified to reduce false positives.
The following analytic detects potential distributed password spraying attacks in an Azure AD environment. It identifies a spike in failed authentication attempts across various user-and-IP combinations from multiple source IPs and countries, using different user agents. This detection leverages Azure AD SignInLogs, focusing on error code 50126 for failed authentications. This activity is significant as it indicates an adversary's attempt to bypass security controls by distributing login attempts. If confirmed malicious, this could lead to unauthorized access, data breaches, privilege escalation, and lateral movement within the organization's infrastructure.
Detects the exploitation of CVE-2022-37966, a security bypass and elevation of privilege vulnerability in Kerberos that allows for weak RC4-HMAC negotiation. This event indicates a Kerberos KDC error related to this vulnerability.
Detects errors when a target server doesn't have suitable keys for generating Kerberos tickets. This issue can occur, for example, when a service uses a user account or a computer account that is configured for only DES encryption on a computer that is running Windows 7 which has DES encryption for Kerberos authentication disabled. This can indicate a misconfiguration or an attempt by an attacker to exploit weak encryption types.
This rule identifies unusual activity from locations not previously observed for a user within a defined lookback period. It leverages Microsoft Entra ID (Azure AD) Behavior Analytics and SigninLogs to baseline legitimate user locations and filter out trusted devices/IPs. The rule flags unusual activity where the source IP location is new for the user and exceeds a configurable threshold of 'unusual' behavior indicators, while also excluding specified trusted countries.
Detects multiple denied or failed multi-factor authentication (MFA) prompts for a single user within a short time window. This pattern is commonly associated with MFA fatigue or push bombing attacks, where an attacker attempts to trick the user into approving an MFA request by spamming prompts repeatedly.
This detection uses Azure AD sign-in logs and does not require asset inventory or conditional access policies to be effective.
This detection uses Azure AD sign-in logs and does not require asset inventory or conditional access policies to be effective.
This query is designed to detect a series of anomalous user behaviors that, when combined, may indicate a compromised account. It flags activity that is uncharacteristic for a specific user, such as performing an unusual action, using an unfamiliar application, connecting from an unexpected ISP or country, or logging in from a new device for the first time. The final filter | where InvestigationPriority > 5 focuses the results on high-priority alerts, which are more likely to be legitimate threats. This approach uses behavioral analytics to identify deviations from a user's normal baseline, making it effective for detecting both insider threats and external attacks that have breached credentials.
Detects logon events (successful or failed) originating from workstations with suspicious or generic names, which can be an indicator of threat actor activity. This pattern has been associated with Akira ransomware campaigns.
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 identifies disabled service accounts within the environment. The goal is to help audit the state of service accounts, identify unused or stale accounts, ensure only necessary service accounts are active to reduce the attack surface, and support compliance and security posture assessments. While not directly indicative of an attack, monitoring disabled service accounts can help identify potential misconfigurations or accounts that could be re-enabled and abused.
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.
The analytic identifies potential Remote Desktop Protocol (RDP) brute force attacks by monitoring network traffic for RDP application activity. It detects potential RDP brute force attacks by identifying source IPs that have made more than 10 connection attempts to the same RDP port on a host within a one-hour window. The results are presented in a table that includes the source and destination IPs, destination port, number of attempts, and the times of the first and last connection attempts, helping to prioritize IPs based on the intensity of activity.
The analytic detects an unusually high number of denied Multi-Factor Authentication (MFA) requests for a single user within a 10-minute window, specifically when more than nine MFA prompts are declined. It leverages Azure Active Directory (Azure AD) sign-in logs, focusing on "Sign-in activity" events with error code 500121 and additional details indicating "MFA denied; user declined the authentication." This behavior is significant as it may indicate a targeted attack or account compromise attempt, with the user actively declining unauthorized access. If confirmed malicious, it could lead to data exfiltration, lateral movement, or further malicious activities.
This detection monitors for signs of a shared or potentially compromised root account on ESXi hosts by tracking the number of unique IP addresses logging in as root within a short time window. Multiple logins from different IPs in a brief period may indicate credential misuse, lateral movement, or account compromise.
KQL Query from file: GraphRunner Recon Detection & Response
KQL Query from file: Sentinel KQL Detection for ShadowHound
KQL Query from file: Detect Microsoft Graph API Abuse
KQL Query from file: Monitor Privilege User SSPR
KQL Query from file: Password Spraying Detection in Active Directory
KQL Query
KQL Query
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