Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
2,132 detections
Filters
Last updated
All Time
Detection languages
761
532
431
179
94
Contributors
499
335
285
191
168
Categories
1,525
1,087
390
369
240
Platforms
934
681
611
142
99
Products / Services
552
383
359
337
293
MITRE Techniques
18,123
15,639
12,581
8,394
6,215
2,132
CVEs
51
50
44
42
29
IDS Classtypes
320
140
29
13
5
IDS Protocols
454
34
13
5
4
Suspicious SQL Query
Sigma
Detects suspicious SQL query keywrods that are often used during recon, exfiltration or destructive activities. Such as dropping tables and selecting wildcard fields
This analytic identifies remote code execution (RCE) attempts targeting F5 BIG-IP, BIG-IQ, and Traffix SDC devices, specifically exploiting CVE-2020-5902. It uses regex to detect patterns in syslog data that match known exploit strings such as "hsqldb;" and directory traversal sequences. This activity is significant because successful exploitation can allow attackers to execute arbitrary commands on the affected devices, leading to full system compromise. If confirmed malicious, this could result in unauthorized access, data exfiltration, or further lateral movement within the network.
The following analytic detects attempts to exploit CVE-2022-40684, a Fortinet appliance authentication bypass vulnerability. It identifies REST API requests to the /api/v2/ endpoint using various HTTP methods (GET, POST, PUT, DELETE) that may indicate unauthorized modifications, such as adding SSH keys or creating new users. This detection leverages the Web datamodel to monitor specific URL patterns and HTTP methods. This activity is significant as it can lead to unauthorized access and control over the appliance. If confirmed malicious, attackers could gain persistent access, reroute network traffic, or capture sensitive information.
This detection identifies potential exploitation of CVE-2025-24813 in Apache Tomcat through the second stage of the attack. This phase occurs when an attacker attempts to trigger deserialization of a previously uploaded malicious session file by sending a GET request with a specially crafted JSESSIONID cookie. These requests typically have specific characteristics, including a JSESSIONID cookie with a leading dot that matches a previously uploaded filename, and typically result in a HTTP 500 error when the exploitation succeeds.
The analytic detects suspicious behavior indicative of ProxyShell exploitation against on-premise Microsoft Exchange servers. It identifies HTTP POST requests to `autodiscover.json` containing `PowerShell` in the URI, leveraging server-side request forgery (SSRF) to access backend PowerShell. Monitoring this activity is crucial as it may indicate an attacker attempting to execute commands or scripts on the Exchange server. If confirmed malicious, this could lead to unauthorized access, privilege escalation, or persistent control over the Exchange environment.
The following analytic detects attempts to exploit the CVE-2023-46747 vulnerability, an authentication bypass flaw in F5 BIG-IP's Configuration utility (TMUI). It identifies this activity by monitoring for specific URI paths such as "*/mgmt/tm/auth/user/*" with the PATCH method and a 200 status code. This behavior is significant for a SOC as it indicates potential unauthorized access attempts, leading to remote code execution. If confirmed malicious, an attacker could gain unauthorized access, execute arbitrary code, steal data, disrupt systems, or conduct further malicious activities within the network.
The analytic identifies potential exploitation of Log4Shell CVE-2021-44228 by correlating multiple MITRE ATT&CK tactics detected in risk events. It leverages Splunk's risk data model to calculate the distinct count of MITRE ATT&CK tactics from Log4Shell-related detections. This activity is significant because it indicates a high probability of exploitation if two or more distinct tactics are observed. If confirmed malicious, this activity could lead to initial payload delivery, callback to a malicious server, and post-exploitation activities, potentially resulting in unauthorized access, lateral movement, and further compromise of the affected systems.
The analytic identifies POST requests targeting specific Ivanti Connect Secure endpoints vulnerable to the SSRF issue (CVE-2024-21893). It focuses on endpoints such as /dana-ws/saml20.ws, /dana-ws/saml.ws, /dana-ws/samlecp.ws, and /dana-na/auth/saml-logout.cgi. The detection filters for POST requests that received an HTTP 200 OK response, indicating successful execution. This activity is significant as it may indicate an attempt to exploit SSRF vulnerabilities, potentially allowing attackers to access internal services or sensitive data. If confirmed malicious, this could lead to unauthorized access and data exfiltration.
The following analytic detects attempts to exploit the F5 BIG-IP iControl REST API vulnerability (CVE-2022-1388) for unauthenticated remote code execution. It identifies suspicious URI paths and POST HTTP methods, along with specific request headers containing potential commands in the `utilcmdargs` field and a random base64 encoded value in the `X-F5-Auth-Token` field. This activity is significant as it targets a critical vulnerability that can allow attackers to execute arbitrary commands on the affected system. If confirmed malicious, this could lead to full system compromise and unauthorized access to sensitive data.
This analytic identifies attempts to exploit the CVE-2023-46805 and CVE-2024-21887 vulnerabilities in Ivanti Connect Secure. It detects GET requests to the `/api/v1/totp/user-backup-code/../../system/system-information` URI, which leverage an authentication bypass to access system information. The detection uses the Web datamodel to identify requests with a 200 OK response, indicating a successful exploit attempt. This activity is significant as it reveals potential unauthorized access to sensitive system information. If confirmed malicious, attackers could gain critical insights into the system, facilitating further exploitation and compromise.
The following analytic detects attempts to exploit the Zerologon CVE-2020-1472 vulnerability via Zeek RPC. It leverages Zeek DCE-RPC data to identify specific operations: NetrServerPasswordSet2, NetrServerReqChallenge, and NetrServerAuthenticate3. This activity is significant because it indicates an attempt to gain unauthorized access to a domain controller, potentially leading to a complete takeover of an organization's IT infrastructure. If confirmed malicious, the impact could be severe, including data theft, ransomware deployment, or other devastating outcomes. Immediate investigation of the identified IP addresses and RPC operations is crucial.
This detection identifies potential exploitation of CVE-2025-24813 in Apache Tomcat through the initial stage of the attack. This first phase occurs when an attacker attempts to upload a malicious serialized Java object with a .session file extension via an HTTP PUT request. When successful, these uploads typically result in HTTP status codes 201 (Created) or 409 (Conflict) and create the foundation for subsequent deserialization attacks by placing malicious content in a location where Tomcat's session management can access it.
The analytic identifies potential exploit attempts involving command and script interpreters that are blocked by Zscaler. It uses web proxy logs to detect incidents where actions are blocked due to exploit references. The detection compiles statistics by user, threat name, URL, hostname, file class, and filename. This activity is significant as it helps identify and mitigate exploit attempts, which are critical for maintaining security. If confirmed malicious, such activity could lead to unauthorized code execution, privilege escalation, or persistent access within the environment, posing a severe threat to organizational security.
The analytic detects outbound network connections from Java processes to default ports used by LDAP and RMI protocols, which may indicate exploitation of the CVE-2021-44228 Log4j vulnerability. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and network traffic logs. Monitoring this activity is crucial as it can signify an attacker’s attempt to perform JNDI lookups and retrieve malicious payloads. If confirmed malicious, this activity could lead to remote code execution and further compromise of the affected server.
The analytic identifies access to a specific Ivanti Connect Secure endpoint, `/api/v1/configuration/users/user-roles/user-role/rest-userrole1/web/web-bookmarks/bookmark`, which is known to be vulnerable to CVE-2023-46805 and CVE-2024-21887. It specifically looks for GET requests that result in a 403 Forbidden HTTP status code with an empty response body. This behavior is indicative of potential exploitation attempts against Ivanti Connect Secure systems. Successful exploitation could lead to unauthorized access or control, potentially resulting in data breaches or system compromise.
The analytic identifies potential exploitation attempts against Citrix ADC related to CVE-2023-3519. It detects POST requests to specific web endpoints associated with this vulnerability by leveraging the Web datamodel. This activity is significant as CVE-2023-3519 involves a SAML processing overflow issue that can lead to memory corruption, posing a high risk. If confirmed malicious, attackers could exploit this to execute arbitrary code, escalate privileges, or disrupt services, making it crucial for SOC analysts to monitor and investigate these alerts promptly.
The analytic identifies attempts to exploit the CVE-2023-46805 and CVE-2024-21887 vulnerabilities in Ivanti Connect Secure. It detects POST requests to specific URIs that leverage command injection to execute arbitrary commands. The detection uses the Web datamodel to monitor for these requests and checks for a 200 OK response, indicating a successful exploit attempt. This activity is significant as it can lead to unauthorized command execution on the server. If confirmed malicious, attackers could gain control over the system, leading to potential data breaches or further network compromise.
The analytic detects the creation of a Kubernetes NodePort service, which exposes a service to the external network. It identifies this activity by monitoring Kubernetes Audit logs for the creation of NodePort services. This behavior is significant for a SOC as it could allow an attacker to access internal services, posing a threat to the Kubernetes infrastructure's integrity and security. If confirmed malicious, this activity could lead to data breaches, service disruptions, or unauthorized access to sensitive information.
The analytic identifies potential exploitation of CVE-2023-20198 in the Web User Interface of Cisco IOS XE software. It detects suspicious activity involving specific URL patterns and HTTP methods, indicative of unauthorized administrative access and potential implant deployment. This activity can lead to full control of the device, compromising its integrity and security.
This analytic detects attempts to exploit the ConnectWise ScreenConnect CVE-2024-1708 vulnerability using Windows SACL EventCode 4663. It identifies path traversal attacks by monitoring file system events related to the ScreenConnect service. This activity is significant as it allows unauthorized access to sensitive files and directories, potentially leading to data exfiltration or arbitrary code execution. If confirmed malicious, attackers could gain unauthorized access to critical data or execute harmful code, compromising the integrity and security of the affected system. Immediate remediation by updating to version 23.9.8 or above is recommended.
This analytic detects and analyzes PowerShell Web Access (PSWA) usage in Windows environments. It tracks both connection attempts (EventID 4648) and successful logons (EventID 4624) associated with PSWA, providing a comprehensive view of access patterns. The analytic identifies PSWA's operational status, host servers, processes, and connection metrics. It highlights unique target accounts, domains accessed, and verifies logon types. This information is crucial for detecting potential misuse, such as lateral movement, brute force attempts, or unusual access patterns. By offering insights into PSWA activity, it enables security teams to quickly assess and investigate potential security incidents involving this powerful administrative tool.
Page 92 of 102