Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
461 detections
Filters
Last updated
All Time
Detection languages
276
67
44
26
23
Contributors
167
111
62
17
9
Categories
238
219
140
83
78
Platforms
338
217
143
29
23
Products / Services
286
157
50
38
27
MITRE Techniques
18,123
15,639
12,581
8,394
6,215
CVEs
13
5
3
2
1
IDS Classtypes
5
3
2
IDS Protocols
9
Detects anomalous non-interactive sign-ins that bypass multi-factor authentication (MFA) requirements from a geographical location not previously seen in the user's historical sign-in baseline. This pattern is indicative of attackers using stolen OAuth access or refresh tokens to maintain persistence and bypass standard authentication controls.
Detects potential N0va-style phishing or token-exchange abuse where a user completes a device-code authentication flow, followed immediately by an access token usage from a different IP address or device. This sequence indicates an adversary has captured the resulting token/session and is attempting to bypass multi-factor authentication (MFA) to gain unauthorized access to cloud resources.
This rule detects OAuth authorization requests (targeting Microsoft or Google identity providers) that use 'prompt=none' (silent authentication) and redirect the user to an unexpected or untrusted domain. This pattern is commonly used in OAuth consent phishing or token theft attacks, where an attacker attempts to silently acquire an authorization code by redirecting it to a malicious application server instead of the legitimate service provider's domain.
Detects anomalous authentication patterns indicative of session token replay attacks. The rule identifies instances where a user successfully performs an MFA-authenticated, interactive sign-in, followed shortly (within 5-60 minutes) by a single-factor sign-in for the same user. The suspicious activity is characterized by a shift to a different IP/ASN, an unmanaged device (non-trusted), and a change in browser or OS identifiers, all while the user's geolocation (city/country) remains consistent. This behavioral pattern is highly consistent with the use of residential proxy infrastructure to replay stolen session tokens, effectively bypassing MFA.
This rule detects user authentication events that deviate significantly from a 14-day historical baseline of known devices, operating systems, browsers, and network locations (ASN). It flags logins where at least two out of four key metadata signals (DeviceId, OS, Browser, ASN) have changed compared to the user's historical pattern, specifically targeting successful authentications that did not trigger a fresh multi-factor authentication (MFA) challenge, potentially indicating token theft or unauthorized session reuse.
Detects Microsoft 365 sign-in events where a session token is reused without a fresh MFA challenge, originating from known residential proxy service providers. This behavior is indicative of threat actors using stolen session tokens (Session Replay) to bypass MFA protections.
This rule monitors Azure AD Sign-in logs to identify 'impossible travel' scenarios, where a user authenticates successfully from two geographically distant locations within a time window (1 hour) that would require travel speeds exceeding a plausible threshold. This behavior is a common indicator of credential compromise or session token replay attacks.
This rule detects OAuth authorization requests (targeting Microsoft or Google identity providers) that use 'prompt=none' (silent authentication) and redirect the user to an unexpected or untrusted domain. This pattern is commonly used in OAuth consent phishing or token theft attacks, where an attacker attempts to silently acquire an authorization code by redirecting it to a malicious application server instead of the legitimate service provider's domain.
This rule detects user authentication events that deviate significantly from a 14-day historical baseline of known devices, operating systems, browsers, and network locations (ASN). It flags logins where at least two out of four key metadata signals (DeviceId, OS, Browser, ASN) have changed compared to the user's historical pattern, specifically targeting successful authentications that did not trigger a fresh multi-factor authentication (MFA) challenge, potentially indicating token theft or unauthorized session reuse.
Detects high-frequency creation and revocation of service principal credentials, indicating potential malicious credential cycling, or unusual bursts of service account JWT/refresh token rotation across multiple IP addresses, which may indicate an attempt to evade detection or maintain persistence.
Detects high-frequency creation and revocation of service principal credentials, indicating potential malicious credential cycling, or unusual bursts of service account JWT/refresh token rotation across multiple IP addresses, which may indicate an attempt to evade detection or maintain persistence.
This rule identifies successful user sign-ins (ResultType 0) originating from specific Autonomous System Numbers (197540, 216154) where Multi-Factor Authentication (MFA) was not required or not recorded, and where Conditional Access policies either did not apply or succeeded without enforcing MFA.
This rule identifies successful user sign-ins (ResultType 0) originating from specific Autonomous System Numbers (197540, 216154) where Multi-Factor Authentication (MFA) was not required or not recorded, and where Conditional Access policies either did not apply or succeeded without enforcing MFA.
Detects OAuth2 JWT bearer-grant token requests to Google's OAuth endpoint using the attacker-controlled service-account identity and Sheets API scope observed in the fake GlobalProtect campaign
This rule detects potential session token replay attacks in Azure Active Directory by correlating interactive or single-factor sign-in events using 'python-requests' user agents (common in automated tooling) with subsequent 'Register device' and 'Register security info' (Windows Hello for Business) activity within a 48-hour window. This behavior often indicates an adversary attempting to register a device to a compromised account to maintain persistence or bypass MFA requirements.
Detects anomalous authentication patterns indicative of session token replay attacks. The rule identifies instances where a user successfully performs an MFA-authenticated, interactive sign-in, followed shortly (within 5-60 minutes) by a single-factor sign-in for the same user. The suspicious activity is characterized by a shift to a different IP/ASN, an unmanaged device (non-trusted), and a change in browser or OS identifiers, all while the user's geolocation (city/country) remains consistent. This behavioral pattern is highly consistent with the use of residential proxy infrastructure to replay stolen session tokens, effectively bypassing MFA.
Detects anomalous authentication patterns indicative of session token replay attacks. The rule identifies instances where a user successfully performs an MFA-authenticated, interactive sign-in, followed shortly (within 5-60 minutes) by a single-factor sign-in for the same user. The suspicious activity is characterized by a shift to a different IP/ASN, an unmanaged device (non-trusted), and a change in browser or OS identifiers, all while the user's geolocation (city/country) remains consistent. This behavioral pattern is highly consistent with the use of residential proxy infrastructure to replay stolen session tokens, effectively bypassing MFA.
Detects anomalous authentication patterns indicative of session token replay attacks. The rule identifies instances where a user successfully performs an MFA-authenticated, interactive sign-in, followed shortly (within 5-60 minutes) by a single-factor sign-in for the same user. The suspicious activity is characterized by a shift to a different IP/ASN, an unmanaged device (non-trusted), and a change in browser or OS identifiers, all while the user's geolocation (city/country) remains consistent. This behavioral pattern is highly consistent with the use of residential proxy infrastructure to replay stolen session tokens, effectively bypassing MFA.
Detects anomalous sign-in behavior where a device code authentication flow is initiated from one location, but the resulting session is replayed from a different IP address in a different country within a one-hour window. This behavior is indicative of an adversary capturing an authentication token via a device code phishing or relay attack and attempting to use it from their own infrastructure to bypass conditional access policies and MFA.
This rule detects user authentication events that deviate significantly from a 14-day historical baseline of known devices, operating systems, browsers, and network locations (ASN). It flags logins where at least two out of four key metadata signals (DeviceId, OS, Browser, ASN) have changed compared to the user's historical pattern, specifically targeting successful authentications that did not trigger a fresh multi-factor authentication (MFA) challenge, potentially indicating token theft or unauthorized session reuse.
Detects Microsoft 365 sign-in events where a session token is reused without a fresh MFA challenge, originating from known residential proxy service providers. This behavior is indicative of threat actors using stolen session tokens (Session Replay) to bypass MFA protections.
Page 13 of 22