Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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This rule detects HTTP web access to domains that exhibit low prevalence within the monitored environment. It specifically filters out internal RFC 1918 IP addresses and ensures that only valid Fully Qualified Domain Names (FQDNs) are considered. The prevalence is determined by a rolling maximum count of unique days a domain has been observed, with alerts triggering for domains seen 3 or fewer times. The risk score is incremented based on how low the prevalence is, with the lowest prevalence (1 day) resulting in the highest risk score.
Detects exploitation attempts targeting Arcadyan routers, specifically looking for path traversal (CVE-2021-20090) and configuration file injection (CVE-2021-20091) vulnerabilities. The rule identifies requests containing '..%2f' (URL-encoded path traversal) and, for the configuration file injection, also 'apply_abstract.cgi', combined with requests to unauthenticated paths.
Detects potential exploitation of CVE-2021-26084, a Confluence RCE using OGNL injection. The rule looks for specific POST requests to Confluence endpoints, either '/pages/createpage-entervariables.action' with a 'SpaceKey=x' parameter or '/doenterpagevariables.action' with a 'u0027' string in the POST body's 'queryString' parameter. It specifically targets requests from 'anonymous' users that result in a 200 HTTP status code.
This rule detects web traffic to a recently expired domain followed by the creation of an executable (.exe) file on the same host. It correlates network access events with WHOIS data to identify expired domains and then looks for subsequent file creation events where the file path ends with '.exe'. The file creation must occur after the network access event on the same principal hostname.
This rule detects when a network connection is made to a domain that has expired. It leverages WHOIS data to identify domains whose expiration time is before the event timestamp. The rule specifically looks for HTTP network events and joins them with WHOIS information to identify access to recently expired domains. This could indicate access to potentially malicious infrastructure, as expired domains can be re-registered by threat actors for nefarious purposes.
Detects network connections with a suspicious user-agent string containing "katz-ontop", which may indicate Katz Stealer activity. This specific user-agent string is likely hardcoded or characteristic of the malware's C2 communication.
Detects network web access to a low prevalence domain
Alert on downloading a known file hash from a known IP with VT Relationships.
Example usage of WHOIS data, detecting an executable file download from a domain that's recently expired
Detects access attempts to a newly created domain via WHOIS enrichment.
Detects POST requests to the F5 BIG-IP iControl Rest API "bash" endpoint, which allows the execution of commands on the BIG-IP
Detects potential exploitation of a chained vulnerability attack targeting Ivanti EPMM 12.5.0.0. CVE-2025-4427 allows unauthenticated access to protected API endpoints via an authentication bypass, which can then be leveraged to trigger CVE-2025-4428 — a remote code execution vulnerability through template injection. This sequence enables unauthenticated remote code execution, significantly increasing the impact of exploitation.
Detect web traffic to a recently expired domain followed by an exe file creation event
The analytic detects local file inclusion (LFI) attacks targeting Kubernetes Nginx ingress controllers. It leverages Kubernetes logs, parsing fields such as `request` and `status` to identify suspicious patterns indicative of LFI attempts. This activity is significant because LFI attacks can allow attackers to read sensitive files from the server, potentially exposing critical information. If confirmed malicious, this could lead to unauthorized access to sensitive data, further exploitation, and potential compromise of the Kubernetes environment.
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 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.
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