Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
289 detections
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This analytic detects network traffic to TCP port 4786, which is used by the Cisco Smart Install protocol. Smart Install is a plug-and-play configuration and image-management feature that helps customers to deploy Cisco switches. This protocol has been exploited via CVE-2018-0171, a vulnerability that allows unauthenticated remote attackers to execute arbitrary code or cause denial of service conditions. Recently, Cisco Talos reported that a Russian state-sponsored threat actor called "Static Tundra" has been actively exploiting this vulnerability to compromise unpatched and end-of-life network devices. Monitoring for traffic to this port can help identify potential exploitation attempts or unauthorized Smart Install activity.
Detects SQL injection attempts targeting FortiWeb (CVE-2025-25257) in HTTP GET requests to /api/fabric/device/status. The rule identifies known exploit payloads in the Authorization header, generic SQL injection patterns, and SQL statements like DROP, DELETE, INSERT, UPDATE, CREATE. It also detects known malicious source IPs targeting the vulnerable endpoint.
This rule detects communication with known malicious domains and IP addresses, as well as the presence or execution of files with known malicious MD5 or SHA256 hashes. It correlates threat intelligence with network and endpoint telemetry to identify compromised devices or accounts.
This analytic identifies instances where an internal host has attempted to communicate with 250 or more destination IP addresses using the same port and protocol. Horizontal port scans from internal hosts can indicate reconnaissance or scanning activities, potentially signaling malicious intent or misconfiguration. By monitoring network traffic logs, this detection helps detect and respond to such behavior promptly, enhancing network security and preventing potential threats.
This analytic identifies instances where an internal host has attempted to communicate with 250 or more destination IP addresses using one of the NMAP top 20 ports. Horizontal port scans from internal hosts can indicate reconnaissance or scanning activities, potentially signaling malicious intent or misconfiguration. By monitoring network traffic logs, this detection helps detect and respond to such behavior promptly, enhancing network security and preventing potential threats.
Detects potential Remote Code Execution (RCE) on a FortiWeb device, possibly related to CVE-2025-25257. The rule correlates a suspicious web request to a potential 'fabric' endpoint with a subsequent shell or utility process launch by the web server process on the same device.
Detects attempts to exploit FortiWeb Pre-Auth SQL Injection leading to Remote Code Execution (CVE-2025-25257) via the /api/fabric/device/status endpoint. The rule specifically looks for SQL injection payloads, including boolean-based and INTO OUTFILE techniques, within the Authorization header of HTTP requests.
Detects potential exploitation of FortiWeb CVE-2025-25257, a pre-authentication SQL injection vulnerability. This rule identifies an SQL injection attempt against the '/api/fabric/device/status' endpoint, followed by either a suspicious Python file drop in a library directory or a request to a specific CGI script used to trigger the payload.
This rule detects successful network connections from external (internet) IP addresses to common Industrial Control System (ICS) ports, such as Modbus (502) and EtherNet/IP (44818). This activity could indicate unauthorized access attempts, reconnaissance, or exploitation of internet-exposed ICS assets. The rule specifically filters for allowed/accepted/permitted connections and excludes connections from RFC 1918 private IP addresses and a configurable list of authorized external IPs.
The analytic detects when a Windows RDP client attempts to execute an RDP file from a temporary directory, downloads directory, or Outlook directories. This detection is significant as it can indicate an attempt for an adversary to deliver a .rdp file, which may be leveraged by attackers to control or exfiltrate data. If confirmed malicious, this activity could lead to unauthorized access, data theft, or further lateral movement within the network.
The following analytic detects unusual Remote Desktop Protocol (RDP) traffic on TCP/3389 by filtering out known RDP sources and destinations, focusing on atypical connections within the network. This detection leverages network traffic data to identify potentially unauthorized RDP access. Monitoring this activity is crucial for a SOC as unauthorized RDP access can indicate an attacker's attempt to control networked systems, leading to data theft, ransomware deployment, or further network compromise. If confirmed malicious, this activity could result in significant data breaches or complete system and network control loss.
The analytic detects network traffic associated with known remote access software applications, such as AnyDesk, GoToMyPC, LogMeIn, and TeamViewer. It leverages Palo Alto traffic logs mapped to the Network_Traffic data model in Splunk. This activity is significant because adversaries often use remote access tools to maintain unauthorized access to compromised environments. If confirmed malicious, this activity could allow attackers to control systems remotely, exfiltrate data, or deploy additional malware, posing a severe threat to the organization's security.
The analytic identifies the execution of the native mimikatz.exe binary on Windows systems, including instances where the binary is renamed. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and original file names. This activity is significant because Mimikatz is a widely used tool for extracting authentication credentials, posing a severe security risk. If confirmed malicious, this activity could allow attackers to obtain sensitive credentials, escalate privileges, and move laterally within the network, leading to potential data breaches and system compromise.
The following analytic detects the execution of known remote access software within the environment. It leverages network logs mapped to the Web data model, identifying specific URLs and user agents associated with remote access tools like AnyDesk, GoToMyPC, LogMeIn, and TeamViewer. This activity is significant as adversaries often use these utilities to maintain unauthorized remote access. If confirmed malicious, this could allow attackers to control systems remotely, exfiltrate data, or further compromise the network, posing a severe security risk.
Detects network traffic to or from IP addresses known to be associated with the active exploitation of the SharePoint vulnerability CVE-2025-53770.
KQL Query from file: Fortigate Belsen Leak KQL Check
KQL Query from file: External connection to High Risk Countries
'The purpose of this content is to identify successful phishing links accessed by users. Once a user clicks on a phishing link, we observe successful network activity originating from non-Microsoft network devices. These devices may include Palo Alto Networks, Fortinet, Check Point, and Zscaler devices.'
'Identifies patterns in the time deltas of contacts between internal and external IPs in Fortinet network data that are consistent with beaconing.
Accounts for randomness (jitter) and seasonality such as working hours that may have been introduced into the beacon pattern.
The lookback is set to 1d, the minimum granularity in time deltas is set to 60 seconds and the minimum number of beacons required to emit a detection is set to 4.
Increase the lookback period to capture beacons with larger periodicities.
The jitter tolerance is set to 0.2 - This means we account for an overall 20% deviation from the infered beacon periodicity. Seasonality is dealt with automatically using series_outliers.
Note: In large environments it may be necessary to reduce the lookback period to get fast query times.'
Accounts for randomness (jitter) and seasonality such as working hours that may have been introduced into the beacon pattern.
The lookback is set to 1d, the minimum granularity in time deltas is set to 60 seconds and the minimum number of beacons required to emit a detection is set to 4.
Increase the lookback period to capture beacons with larger periodicities.
The jitter tolerance is set to 0.2 - This means we account for an overall 20% deviation from the infered beacon periodicity. Seasonality is dealt with automatically using series_outliers.
Note: In large environments it may be necessary to reduce the lookback period to get fast query times.'
'This content is utilized to identify instances of successful login by risky users, who have been observed engaging in potentially suspicious network activity on non-Microsoft network devices.'
'This content is employed to correlate with Microsoft Defender XDR phishing-related alerts. It focuses on instances where a user successfully connects to a phishing URL from a non-Microsoft network device and subsequently makes successful sign-in attempts from the phishing IP address.'
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