Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
60 detections
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Detects application consent by a user shortly after the same account clicked a code-of-conduct or compliance-themed message. This behavior can indicate a multi-stage phishing campaign where a user is lured into clicking a malicious link (e.g., a fake policy document) and subsequently grants OAuth permissions to a malicious application.
Detects application consent by a user shortly after the same account clicked a code-of-conduct or compliance-themed message. This behavior can indicate a multi-stage phishing campaign where a user is lured into clicking a malicious link (e.g., a fake policy document) and subsequently grants OAuth permissions to a malicious application.
This rule detects EvilTokens device code OAuth phishing by identifying rapid, repeated POST requests to the /common/oauth2/token endpoint with a device_code grant and authorization_pending responses, followed by a successful token issuance and sign-in without an MFA claim. This pattern is consistent with EvilTokens session harvesting.
This rule detects impossible travel scenarios for Azure logins by calculating the speed required to travel between two consecutive login locations for the same user. It flags logins where the calculated speed exceeds the maximum speed of a commercial passenger aircraft (900 km/h) and the logins originate from different countries. Mobile device logins are excluded to reduce false positives related to VPN usage.
This rule detects impossible travel scenarios for Azure logins by calculating the speed required to travel between two consecutive login locations for the same user. It flags logins where the calculated speed exceeds the maximum speed of a commercial passenger aircraft (900 km/h) and the logins originate from different countries. Mobile device logins are excluded to reduce false positives related to VPN usage.
Detects URL click events or browser launches where the URL targets Microsoft or Google OAuth endpoints and contains the combination of 'prompt=none' and 'scope=invalid'. This specific combination is used by attackers to force a silent error redirect to a malicious URI.
Detects a suspicious Salesforce OAuth login followed by high-volume data exfiltration activity within a short period. This pattern is associated with the threat actor UNC6040, which uses malicious or compromised OAuth applications to steal data.
https://www.proofpoint.com/us/blog/threat-insight/beyond-credentials-weaponizing-oauth-applications-persistent-cloud-access
The following analytic detects the use of default or publicly known named pipes associated with Cobalt Strike. It leverages Sysmon EventID 17 and 18 to identify specific named pipes commonly used by Cobalt Strike's Artifact Kit and Malleable C2 Profiles. This activity is significant because Cobalt Strike is a popular tool for adversaries to conduct post-exploitation tasks, and identifying its named pipes can reveal potential malicious activity. If confirmed malicious, this could indicate an active Cobalt Strike beacon, leading to unauthorized access, data exfiltration, or further lateral movement within the network.
This analytic detects suspicious data transfer activities involving the use of the `split` syscall with the `-b` option, which specifies the size of each output file. This technique can be used by attackers to evade detection by breaking large files into smaller parts, bypassing size-based security controls, and facilitating covert exfiltration of sensitive data. By monitoring for unusual or unauthorized use of the `split` syscall with a specified block size, this analytic helps identify potential data exfiltration attempts.
This analytic identifies the bulk creation of ransomware notes (e.g., .txt, .html, .hta files) on an infected machine. It leverages Sysmon EventCode 11 to detect multiple instances of these file types being created within a short time frame (10 seconds). A detection is triggered if 15 or more unique readme paths are observed. This activity is a strong indicator of an active ransomware attack, where the attacker is notifying the victim of the encryption. If confirmed malicious, this behavior could lead to widespread data encryption, rendering critical files inaccessible and potentially causing significant operational disruption.
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 a user authorizing a Salesforce application via the '/setup/connect' page. This specific TTP is associated with ShinyHunters vishing campaigns where attackers impersonate IT staff and trick employees into entering a connection code, thereby granting a malicious application access to the organization's Salesforce environment.
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.
KQL Query
KQL Query
KQL Query from file: Critical Identities OAuth Grant
KQL Query from file: Overprivileged Admin-Consented OAuth Applications
'This query searches for successful user logins from different countries within 3 hours.
To use this analytics rule, make sure you have deployed the [ASIM normalization parsers](https://aka.ms/ASimAuthentication)'
To use this analytics rule, make sure you have deployed the [ASIM normalization parsers](https://aka.ms/ASimAuthentication)'
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.
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