Detections

Explore public detection logic contributed by the community across SIEM and rule languages.

1,666 detections

Detects a sequence of events starting with a device code OAuth sign-in, followed within two hours by the creation or modification of inbox rules and high-frequency enumeration of email items (contacts, folders, messages, threads, people) via Microsoft Graph API. This behavior is indicative of post-compromise activity where an adversary uses OAuth device code grants to maintain access and subsequently automates inbox analysis or data harvesting.
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Arnold Chan@slaz
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Detections.ai Community
2 months ago
3015
Detects a suspicious sequence of interactive sign-ins to Microsoft management and profile-related applications from unmanaged devices occurring within a tight 15-minute window. This behavior is indicative of post-compromise activity, potentially associated with activity clusters such as UNC6671, where an attacker interacts with account settings shortly after compromising a session.
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Arnold Chan@slaz
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Detections.ai Community
2 months ago
000
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.
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Arnold Chan@slaz
Defender - KQL
2 months ago
000
Detects successful Azure AD sign-ins where the user agent identifies as a Python requests library version (2.33.1 or 2.34.2) that has not been observed for that specific user account in the preceding 14 days. This behavior is indicative of anomalous automated or scripted access.
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Arnold Chan@slaz
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Detections.ai Community
2 months ago
000
This rule correlates successful Microsoft 365 sign-in events with preceding network events involving identified NovaCookies AiTM phishing infrastructure (specifically targeting .vu domains or specific URL patterns). It triggers when a user successfully authenticates using MFA shortly after accessing a known phishing link, which is a strong indicator of an active AiTM session hijacking attempt.
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Arnold Chan@slaz
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Detections.ai Community
2 months ago
2013
Detects instances where a user performs an interactive login followed by a non-interactive login within a 1-hour window, where the source IP address and geographical location of both sessions differ. This behavior is indicative of potential session hijacking or token theft where an attacker is using stolen session artifacts from a different location.
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Ankit Mehta@Secvyn
Bharat Cyber Guardians
2 months ago
000
Detects anomalous authentication behavior indicative of an Adversary-in-the-Middle (AiTM) phishing kit. The rule identifies multiple password submission attempts (failures) followed by a successful authentication within a short period, consistent with an attacker testing multiple harvested credentials against an active session.
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Arnold Chan@slaz
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Midnight Slayer
2 months ago
000
Detects anomalous reuse of an authenticated Microsoft 365 session cookie shortly after a successful MFA-satisfied sign-in. The detection identifies scenarios where the subsequent access originates from a different IP address, device, or user agent without an intervening MFA challenge, which is a pattern indicative of session hijacking techniques like Adversary-in-the-Middle (AiTM) or session token replay.
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Arnold Chan@slaz
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Detections.ai Community
2 months ago
3012
Detects non-interactive sign-in events involving OIDC or external authentication tokens shortly after a known Coder registry compromise. The rule flags high-risk or at-risk single-factor authentication sessions, which may indicate the replay of stolen tokens harvested during the compromise window.
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Arnold Chan@slaz
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Detection & Hunting Community
2 months ago
000
Detects potential session replay or cookie theft attacks against Google Workspace accounts. The rule identifies anomalous session activity where a single session ID is associated with multiple country codes within a short timeframe (less than 60 minutes), suggesting that a stolen session token is being used by an adversary in a different location than the legitimate user.
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Arnold Chan@slaz
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Detection & Hunting Community
2 months ago
000
Detects potential session replay or cookie theft attacks against Google Workspace accounts. The rule identifies anomalous session activity where a single session ID is associated with multiple country codes within a short timeframe (less than 60 minutes), suggesting that a stolen session token is being used by an adversary in a different location than the legitimate user.
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Arnold Chan@slaz
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Detections.ai Community
2 months ago
000
This rule identifies potentially compromised Entra ID accounts by correlating multiple suspicious behaviors within a 14-day window. It aggregates four specific indicators: successful authentication following repeated MFA failures, impossible travel (sign-ins from multiple countries within a short timeframe), unusual hour logons (00:00-05:00), and sign-ins flagged with medium/high risk or reported as compromised. The rule triggers when an account exhibits two or more of these distinct anomalous behaviors.
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Arnold Chan@slaz
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Detections.ai Community
2 months ago
11011
KQL Query
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Sagar Kotireke@Sagar5200
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01 | 🇨🇭 Swiss Cyber Hunters
2 months ago
2019
Detects successful authentication to Microsoft Teams or Slack where MFA was successfully completed, but the device is reported as unmanaged or non-compliant and is accompanied by sign-in risk signals. This behavior is consistent with Adversary-in-the-Middle (AitM) proxy attacks where an attacker captures credentials and session tokens, bypassing MFA, and using the hijacked session from an unrecognized device or location.
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Tim Peck@timpeck
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Detections.ai Community
2 months ago
12024
This rule identifies potentially compromised Entra ID accounts by correlating multiple suspicious behaviors within a 14-day window. It aggregates four specific indicators: successful authentication following repeated MFA failures, impossible travel (sign-ins from multiple countries within a short timeframe), unusual hour logons (00:00-05:00), and sign-ins flagged with medium/high risk or reported as compromised. The rule triggers when an account exhibits two or more of these distinct anomalous behaviors.
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Arnold Chan@slaz
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Detections.ai Community
2 months ago
2010
Detects a pattern of multiple failed authentication attempts (brute force) from an external IP address followed by a successful authentication event on remote access services such as RDP, VPN, or NTLM. The rule filters for non-private IP addresses and flags successes occurring outside of standard business hours.
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Arnold Chan@slaz
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Detections.ai Community
2 months ago
5010
Detects anomalous sign-in behavior shortly after a successful MFA-authenticated session in Microsoft 365. This rule flags scenarios where a subsequent session for the same user occurs within a short window (30 minutes) from a different IP, ASN, User-Agent, or device identifier, potentially indicating the replay of a stolen session cookie obtained via Adversary-in-the-Middle (AiTM) phishing.
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Arnold Chan@slaz
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Detections.ai Community
2 months ago
309
Detects successful authentication events to the Okta Admin Console originating from known proxy services, anonymization networks, or suspicious infrastructure. This activity may indicate an attempt to use compromised administrative credentials to gain unauthorized access to the Identity Provider (IdP) for downstream reconnaissance or persistence.
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Lacey Cochrane@NullVectorX
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XQL Threat Forge
2 months ago
11112
Detects non-interactive sign-ins, such as those using refresh or session tokens, originating from an IP address, ASN, device, or country that does not match the user's recent interactive sign-in history. The absence of an MFA challenge for these new contexts is characteristic of session token replay attacks, often associated with AiTM phishing campaigns.
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Ankit Mehta@Secvyn
Bharat Cyber Guardians
2 months ago
000
Detects successful device code authentication flows from users who have not utilized this authentication method within the past 14 days. This rule monitors Entra ID (Azure AD) sign-in logs and non-interactive logs, enriching results with Intune device compliance data and authentication protocol details to assist in identifying anomalous access patterns that may indicate credential abuse or device-code phishing.
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Rudra Verma@Rudraverma
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Detections.ai Community
2 months ago
7041
This rule detects scenarios where a user account registers or adds a new device and subsequently performs a sign-in session within one hour that is either marked as high/medium risk or occurs via sensitive applications like the Azure Portal or Microsoft Graph. This pattern often indicates account compromise, where an attacker has registered a rogue device to gain persistent access or perform administrative tasks.
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Shadows VMB@Vemorian_Mort
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Detections.ai Community
2 months ago
14019
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