Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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This rule detects authentication events occurring from devices that have not been seen in the user's history over the previous 14 days and involve non-compliant, unmanaged, or non-Azure AD joined devices. The detection specifically looks for sign-ins that do not utilize interactive credential prompts (token-based), which may indicate an adversary using a stolen session token (e.g., a Primary Refresh Token) from an unrecognized or insecure source.
Detects multiple successful GitLab authentication events from a specific source IP that contain parameters indicating an attempt to skip or bypass two-factor authentication, consistent with known GitLab 2FA bypass vulnerabilities.
Detects successful authentication events using known default or low-privileged Cisco Firepower Management Center (FMC) service accounts, or IDS alerts (Snort SID 66883) associated with credential abuse activity potentially related to CVE-2026-20316.
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.
Detects high-volume authentication attempts against Fortinet network appliances where multiple distinct source IPs successfully authenticate against multiple distinct accounts matching specific administrative naming patterns. This pattern is indicative of potential credential stuffing or brute-force campaigns targeting edge infrastructure.
This rule detects instances where Google Workspace flags a sensitive action as risky (such as sign-ins from suspicious locations, unusual devices, or known-bad IPs), yet the action is allowed to proceed. This indicates a potential bypass of security controls or an account compromise where an adversary is utilizing a valid account despite triggered risk indicators.
This rule monitors for successful sign-ins to sensitive cloud resources (such as Microsoft Authentication Broker, Office 365 Exchange Online, or Graph API) that originate from IP addresses not observed in the user's recent history (14-day baseline) or from known datacenter/hosting network ranges. The rule also filters for common scripted User-Agent strings (e.g., Python requests, curl) or empty User-Agent strings to increase the likelihood of detecting programmatic or malicious automated access rather than standard browser-based user activity.
Detects anomalous, non-interactive sign-in activity in Microsoft 365 that suggests session cookie replay (Pass-the-Cookie). The rule correlates successful, MFA-validated interactive sign-ins against subsequent, non-interactive sign-ins that lack MFA, occur from a different device and IP address within a short time window, and bypass standard interactive re-authentication.
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.
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.
This rule detects potentially unauthorized access to cloud resources or OAuth applications by identifying sign-ins or API calls originating from IP addresses or locations not previously observed for the user in the 30 days prior to a documented Coder registry compromise exposure window.
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 anomalous, non-interactive sign-in activity in Microsoft 365 that suggests session cookie replay (Pass-the-Cookie). The rule correlates successful, MFA-validated interactive sign-ins against subsequent, non-interactive sign-ins that lack MFA, occur from a different device and IP address within a short time window, and bypass standard interactive re-authentication.
Detects successful VPN logins following a burst of failed attempts from multiple IP addresses, where the successful login originates from a known malicious IP or an IP associated with the preceding failed attempts. This rule specifically identifies potential credential stuffing or password spraying activity targeting VPN services, often associated with the BlackSuit ransomware group.
Detects anomalous certificate-based authentication attempts to Jasmine network services characterized by rapid, impossible-travel scenarios or geo-inconsistent session patterns. This behavior is indicative of a stolen certificate being reused from a different geographic location or network, bypassing standard authentication patterns.
Detects anomalous certificate-based authentication attempts to Jasmine network services characterized by rapid, impossible-travel scenarios or geo-inconsistent session patterns. This behavior is indicative of a stolen certificate being reused from a different geographic location or network, bypassing standard authentication patterns.
Detects anomalous certificate-based authentication attempts to Jasmine network services characterized by rapid, impossible-travel scenarios or geo-inconsistent session patterns. This behavior is indicative of a stolen certificate being reused from a different geographic location or network, bypassing standard authentication patterns.
Detects successful authentication events using known default or low-privileged Cisco Firepower Management Center (FMC) service accounts, or IDS alerts (Snort SID 66883) associated with credential abuse activity potentially related to CVE-2026-20316.
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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