Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
1,235 detections
Filters
Last updated
All Time
Detection languages
734
214
105
78
51
Contributors
206
187
159
111
90
Categories
396
296
264
260
247
Platforms
492
314
281
204
110
Products / Services
379
234
152
109
106
MITRE Techniques
18,123
15,639
12,581
8,394
6,215
1,235
CVEs
9
9
4
4
4
IDS Classtypes
11
6
4
2
1
IDS Protocols
10
5
3
Detects unauthorized creation of a WordPress administrator account through direct SQL injection or manual database modification by monitoring for insertions into the 'wp_users' table or modifications to the 'wp_usermeta' table with 'administrator' capabilities.
Detects a multi-stage cloud attack sequence involving a sign-in anomaly, modification of user authentication methods (e.g., registering an attacker-controlled device), reconnaissance using Microsoft Graph API, and subsequent abnormal data access/download, typically observed in Storm-3121 and Storm-3032 activities.
Detects bulk file deletion activities (three or more files deleted or moved to the recycle bin) within SharePoint or OneDrive by a specific user within a 30-minute window, which may indicate malicious activity or data destruction.
This rule detects successful sign-in activity to the 'My Sign-Ins' application where the device is not marked as trusted or the sign-in carries a non-zero risk level. This behavior may indicate an adversary attempting to gain access to account security settings or sensitive portal information using compromised or unmanaged devices.
Detects outbound network traffic from internal devices interacting with Dahua Easy4IP cloud relay services, specifically identifying device resolution requests, probe actions, and the submission of WSSE authentication headers (UsernameToken and PasswordDigest) to the cloud relay host.
Detects rapid, sequential identity-validation API calls (such as GetCallerIdentity, GetSessionToken, or AssumeRole) performed across a large number of distinct AWS access keys from a single source within a short time window. This pattern is indicative of automated tooling validating a harvested corpus of AWS access keys to identify active credentials.
Detects rapid, sequential identity-validation API calls (such as GetCallerIdentity, GetSessionToken, or AssumeRole) performed across a large number of distinct AWS access keys from a single source within a short time window. This pattern is indicative of automated tooling validating a harvested corpus of AWS access keys to identify active credentials.
This detection identifies enabled user accounts that have not had a successful sign-in for the last 90 days, highlighting potential security risks from dormant accounts.
Detects successful remote logons to network devices (routers, VPNs, NAS) using the 'admin' account. This pattern is indicative of credential spraying or brute-force attacks targeting weak default credentials on externally facing management interfaces, often associated with botnets or threat actors performing reconnaissance and initial access operations.
Detects instances where a single proxy service API key or account is used from an abnormally high number of distinct IP addresses within a short timeframe. This behavior is indicative of credential theft and unauthorized sharing or exploitation of proxy credentials by third-party actors.
Detects instances where a single proxy service API key or account is used from an abnormally high number of distinct IP addresses within a short timeframe. This behavior is indicative of credential theft and unauthorized sharing or exploitation of proxy credentials by third-party actors.
Detects instances where a single proxy service API key or account is used from an abnormally high number of distinct IP addresses within a short timeframe. This behavior is indicative of credential theft and unauthorized sharing or exploitation of proxy credentials by third-party actors.
Detects instances where a single proxy service API key or account is used from an abnormally high number of distinct IP addresses within a short timeframe. This behavior is indicative of credential theft and unauthorized sharing or exploitation of proxy credentials by third-party actors.
Detects unauthorized access and data retrieval attempts from a MinIO object storage instance using default administration credentials (minioadmin:minioadmin). The rule monitors both command-line execution of MinIO client (mc) operations and network connections to MinIO service ports (9000/9001) that involve the use of the default credential pair.
Detects lateral movement activity where a host identified as running the Langflow framework attempts to connect to internal MySQL (3306) or Nacos (8848) services while utilizing root credentials, potentially indicating an attacker pivoting from a compromised application server.
Detects lateral movement activity where a host identified as running the Langflow framework attempts to connect to internal MySQL (3306) or Nacos (8848) services while utilizing root credentials, potentially indicating an attacker pivoting from a compromised application server.
This rule identifies successful user sign-in events specifically targeted at Microsoft's management portals (myaccount.microsoft.com and mysignins.microsoft.com). It aggregates these events by user, tracking the frequency, timeframe, and originating IP addresses for successful access. This monitoring is useful for detecting abnormal access to identity settings, which could indicate credential abuse or unauthorized account management.
This rule detects a user account performing its first-ever GitHub OAuth or repository access within a short timeframe (30 minutes) after authenticating to a designated low-trust OpenAI community forum. This pattern is designed to identify potential adversary activity where a compromised or newly created account is used to pivot from a low-trust environment to corporate repository access.
This rule detects a user account performing its first-ever GitHub OAuth or repository access within a short timeframe (30 minutes) after authenticating to a designated low-trust OpenAI community forum. This pattern is designed to identify potential adversary activity where a compromised or newly created account is used to pivot from a low-trust environment to corporate repository access.
Detects GitHub repository actions (git.clone, oauth_authorization.create, repo.download_zip) from actor IPs not seen for that user in the prior 30 days, requiring either 3+ actions or activity across 2+ distinct repositories from the new IP within the same day. Aggregating to burst/multi-repo behavior (rather than alerting on a single new-IP event) filters out one-off false positives from travel, VPNs, or dynamic IP reassignment while still surfacing sustained anomalous access patterns consistent with account compromise or bulk exfiltration.
Detects GitHub repository actions (git.clone, oauth_authorization.create, repo.download_zip) from actor IPs not seen for that user in the prior 30 days, requiring either 3+ actions or activity across 2+ distinct repositories from the new IP within the same day. Aggregating to burst/multi-repo behavior (rather than alerting on a single new-IP event) filters out one-off false positives from travel, VPNs, or dynamic IP reassignment while still surfacing sustained anomalous access patterns consistent with account compromise or bulk exfiltration.
Page 15 of 59