Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
158 detections
Filters
Last updated
All Time
Detection languages
93
25
16
9
7
Contributors
75
40
10
6
5
Categories
105
50
44
39
34
Platforms
94
93
51
13
10
Products / Services
104
53
19
15
11
MITRE Techniques
18,115
15,635
12,576
8,388
6,215
CVEs
1
IDS Classtypes
1
1
1
IDS Protocols
2
Detects potential session cookie replay attacks by identifying sign-in events using SSO or session tokens from different IP addresses, geographic locations, or devices compared to an established user baseline, without a corresponding fresh MFA challenge in the same timeframe. This behavior is indicative of unauthorized access to an already-authenticated session.
Detects HTTP proxy traffic containing specific header or cookie patterns associated with the Evilginx2 or BigBear adversary-in-the-middle (AiTM) phishing frameworks. These frameworks use these artifacts to proxy connections and facilitate the theft of session tokens for MFA bypass.
Detects an anomalous OfficeHome sign-in from an unmanaged device that completes secondary auth (error 50074) followed by a keep-me-signed-in event (error 50140), with the same session/user then rapidly accessing multiple sensitive applications (My Apps, My Profile, Account Controls, My SignIns, SharePoint/OneDrive/Outlook) - consistent with AiTM-assisted MFA bypass and persistent session establishment
This rule detects scenarios where an existing SSO or Identity Provider (IdP) session is reused from a device context that differs from the one that originally performed MFA. It identifies this by joining authentication events by SessionId, specifically looking for instances where MFA occurred in one event and a subsequent non-MFA event for the same session has a different User-Agent, DeviceId, JA3 hash, or Operating System build, indicating potential session token theft or reuse.
Detects anomalous NetScaler session activity potentially indicative of session hijacking or token forgery. This includes identifying session tokens reused across different source IP addresses or user-agents within a short timeframe, as well as detecting session tokens utilized in traffic without a preceding recorded authentication or logon event.
Detects potential session hijacking where an Entra ID session is reused by a non-managed/non-compliant device from a different IP and country shortly after an initial authentication from a managed device. This pattern is consistent with infostealer malware stealing session tokens and replaying them on attacker-controlled infrastructure to bypass MFA.
Detects potential session hijacking where an Entra ID session is reused by a non-managed/non-compliant device from a different IP and country shortly after an initial authentication from a managed device. This pattern is consistent with infostealer malware stealing session tokens and replaying them on attacker-controlled infrastructure to bypass MFA.
This rule hunts for indicators associated with the TrustSink technique, which involves using rogue External Authentication Methods (EAM) or malicious OIDC providers to bypass authentication or maintain persistence within a cloud environment. It monitors Azure AD sign-in and audit logs, alongside DNS and network events, for references to known malicious domains and URLs used for EAM-based attacks.
Detects anomalous authentication behavior where a single session identifier is used to access three or more distinct SaaS applications within a one-hour window. This behavior often indicates the unauthorized use of a stolen session cookie or OAuth token (Pass-the-Cookie) across multiple SSO-integrated platforms.
Detects impossible-travel activity where a single user account authenticates successfully from multiple distinct countries or IP addresses within a short 15-minute window. This behavior is highly indicative of potential session hijacking or the concurrent use of a stolen session token by both an attacker and a legitimate user.
This rule detects successful non-interactive sign-ins to high-value applications (e.g., Office 365, Azure, Okta) where no multi-factor authentication (MFA) challenge was performed during the current session. Such activity is a strong indicator of session hijacking or token replay attacks, where adversaries reuse stolen session cookies to bypass interactive login requirements and MFA.
Detects potential account compromise by aggregating multiple high-fidelity indicators of adversary activity for a single user within a 72-hour window. The rule triggers when at least three distinct stages of an attack chain are identified, specifically: credential theft/infostealer activity, anomalous sign-in (token reuse/impossible travel), mailbox or OAuth configuration changes, and mass data exfiltration.
This rule detects scenarios where an existing SSO or Identity Provider (IdP) session is reused from a device context that differs from the one that originally performed MFA. It identifies this by joining authentication events by SessionId, specifically looking for instances where MFA occurred in one event and a subsequent non-MFA event for the same session has a different User-Agent, DeviceId, JA3 hash, or Operating System build, indicating potential session token theft or reuse.
Detects anomalous sign-in behavior where the same Entra ID session or refresh token is reused from two geographically distant IP addresses within a short timeframe. This activity is correlated with a prior suspicious OAuth consent-grant or device-code authentication event on the same account, which is a common indicator of an 'EvilTokens' style session-token replay attack.
Detects potential session hijacking or malicious OAuth consent grants within Okta. This rule triggers either when an authenticated session start is associated with risky flags (impossible travel, anomalous device/location) or when an OAuth consent grant for 'recall' or cross-tenant functionality occurs shortly after a high-risk sign-in event.
Detects evidence of account persistence and potential mailbox manipulation following an anomalous sign-in event in Google Workspace. The rule identifies suspicious post-compromise activity such as the addition of new MFA methods, new OAuth application grants, or mailbox forwarding/inbox rules, which are consistent with the reuse of captured authenticated sessions.
Detects potential Adversary-in-the-Middle (AiTM) activity by identifying a successful interactive MFA-protected sign-in followed within 15 minutes by a non-interactive (refresh token) sign-in for the same user from a different IP address, ASN, or User-Agent. This pattern is indicative of session hijacking where an attacker reuses a stolen session token.
Detects evidence of account persistence and potential mailbox manipulation following an anomalous sign-in event in Google Workspace. The rule identifies suspicious post-compromise activity such as the addition of new MFA methods, new OAuth application grants, or mailbox forwarding/inbox rules, which are consistent with the reuse of captured authenticated sessions.
This rule detects a correlation between a successful non-interactive authentication event (e.g., token-based sign-in) and a subsequent device registration event performed by the same user identity within a short time window. This sequence is a known pattern for attackers seeking to register an adversary-controlled device against a compromised account to establish durable persistence and potentially bypass conditional access policies (PRT persistence).
This rule detects a correlation between a successful non-interactive authentication event (e.g., token-based sign-in) and a subsequent device registration event performed by the same user identity within a short time window. This sequence is a known pattern for attackers seeking to register an adversary-controlled device against a compromised account to establish durable persistence and potentially bypass conditional access policies (PRT persistence).
Detects anomalous reuse of a single session identifier across geographically distinct locations and/or multiple federated SaaS applications within a short time frame. The detection specifically filters for non-interactive sign-in events, which is characteristic of session hijacking and token replay rather than standard user activity.
Page 1 of 8