Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects potential remote shell behavior where a process named libgcrypt establishes an outbound connection on TCP port 9999 and spawns a shell (such as sh, bash, dash, zsh, or ash) within a short time window.
Detects DNS lookups, TLS connections, and network traffic associated with the PoeLLM cryptominer communicating with Kryptex mining pools, as well as Stratum mining protocol login attempts.
Detects DNS lookups, TLS connections, and network traffic associated with the PoeLLM cryptominer communicating with Kryptex mining pools, as well as Stratum mining protocol login attempts.
Detects a sequential pattern where a browser process (Chrome, Edge, or Safari) accesses an AI chat platform's conversation API, followed within 5 minutes by a network connection to a known unauthorized exfiltration destination (api.pbapi.xyz). This pattern suggests the potential use of a browser-based tool or extension (e.g., Poper Blocker or similar) to intercept and exfiltrate AI chat history.
The following analytic detects the execution of curl commands with insecure flags within the Cisco Isovalent environment. It identifies this activity by monitoring process execution logs for curl commands that use the -k or --insecure flags. This behavior is significant for a SOC as it could allow an attacker to bypass SSL/TLS verification, potentially exposing the Kubernetes infrastructure to man-in-the-middle attacks. If confirmed malicious, this activity could lead to data interception, service disruptions, or unauthorized access to sensitive information.
The following analytic detects suspicious file and directory discovery activities, which may indicate an attacker's effort to locate sensitive documents and files on a compromised system. This behavior often precedes data exfiltration, as adversaries seek to identify valuable or confidential information for theft. By identifying unusual or unauthorized attempts to browse or enumerate files and directories, this analytic helps security teams detect potential reconnaissance or preparatory actions by an attacker, enabling timely intervention to prevent data breaches or unauthorized access.
The following analytic detects attempts to access or dump the contents of /etc/passwd and /etc/shadow files on Linux systems. It leverages data from Linux Auditd, focusing on processes like 'cat', 'nano', 'vim', and 'vi' accessing these files. This activity is significant as it may indicate credential dumping, a technique used by adversaries to gain persistence or escalate privileges. If confirmed malicious, privileges. If confirmed malicious, attackers could obtain hashed passwords for offline cracking, leading to unauthorized access and potential system compromise.
The following analytic detects the suspicious use of the whoami command, which may indicate an attacker trying to gather information about the current user account on a compromised system. The whoami command is commonly used to verify user privileges and identity, especially during initial stages of an attack to assess the level of access. By monitoring for unusual or unauthorized executions of whoami, this analytic helps in identifying potential reconnaissance activities, enabling security teams to take action before the attacker escalates privileges or conducts further malicious operations.
The following analytic detects the use of the curl command with specific switches (-F, --form, --upload-file, -T, -d, --data, --data-raw, -I, --head) to upload AWS credentials or configuration files to a remote destination. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions and process details. This activity is significant as it may indicate an attempt to exfiltrate sensitive AWS credentials, a technique known to be used by the TeamTNT group. If confirmed malicious, this could lead to unauthorized access and potential compromise of AWS resources.
The following analytic detects the use of the 'dd' command to overwrite files on a Linux system. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs that include command-line details. This activity is significant because adversaries often use the 'dd' command to destroy or irreversibly overwrite files, disrupting system availability and services. If confirmed malicious, this behavior could lead to data destruction, making recovery difficult and potentially causing significant operational disruptions.
The following analytic detects the behavior of decoding base64-encoded data and passing it to a Linux shell. Additionally, it mitigates the potential damage and protects the organization's systems and data.The detection is made by searching for specific commands in the Splunk query, namely "base64 -d" and "base64 --decode", within the Endpoint.Processes data model. The analytic also includes a filter for Linux shells. The detection is important because it indicates the presence of malicious activity since Base64 encoding is commonly used to obfuscate malicious commands or payloads, and decoding it can be a step in running those commands. It suggests that an attacker is attempting to run malicious commands on a Linux system to gain unauthorized access, for data exfiltration, or perform other malicious actions.
The following analytic detects the use of 'curl' and 'wget' commands within a Linux environment. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names, user information, and command-line executions. This activity is significant as 'curl' and 'wget' are commonly used for downloading files, which can indicate potential ingress of malicious tools. If confirmed malicious, this activity could lead to unauthorized code execution, data exfiltration, or further compromise of the system. Monitoring and tuning this detection helps identify and differentiate between normal and potentially harmful usage.
The following analytic identifies the use of the 'kmod' process to list kernel modules on a Linux system. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line executions. While listing kernel modules is not inherently malicious, it can be a precursor to loading unauthorized modules using 'insmod'. If confirmed malicious, this activity could allow an attacker to load kernel modules, potentially leading to privilege escalation, persistence, or other malicious actions within the system.
The following analytic identifies potential enumeration of local network configuration on Linux systems.
It detects this activity by monitoring processes such as "arp," "ifconfig," "ip," "netstat," "firewall-cmd," "ufw," "iptables," "ss," and "route" within a 30-minute window.
This behavior is significant as it often indicates reconnaissance efforts by adversaries to gather network information for subsequent attacks.
If confirmed malicious, this activity could enable attackers to map the network, identify vulnerabilities, and plan further exploitation or lateral movement within the environment.
It detects this activity by monitoring processes such as "arp," "ifconfig," "ip," "netstat," "firewall-cmd," "ufw," "iptables," "ss," and "route" within a 30-minute window.
This behavior is significant as it often indicates reconnaissance efforts by adversaries to gather network information for subsequent attacks.
If confirmed malicious, this activity could enable attackers to map the network, identify vulnerabilities, and plan further exploitation or lateral movement within the environment.
Sweeps network connection, HTTP/proxy, file, and process telemetry for exact matches against known Cling botnet indicators: dedicated STUN C2/operator IPs, Cling MIPS loader staging URLs, and known Cling sample SHA256 hashes. Tightened to exclude broad domain-level matching on stun.l.google.com (legitimate, ubiquitous Google STUN infrastructure that Cling spoofs/abuses rather than owns) and to match payload-staging hosts only by full URL rather than bare IP, reducing false positives from shared/compromised hosting and unrelated legitimate STUN traffic.
This rule detects indicator of compromise (IOC) sweep events associated with the AhsayCBS campaign (CVE-2026-105133 and CVE-2026-105134) involving XMRig cryptominer activity. It checks for specific file and process SHA256 hashes, remote IP addresses, and domain connections related to known mining pools or attacker infrastructure using Sentinel Device tables.
This rule detects indicator of compromise (IOC) sweep events associated with the AhsayCBS campaign (CVE-2026-105133 and CVE-2026-105134) involving XMRig cryptominer activity. It checks for specific file and process SHA256 hashes, remote IP addresses, and domain connections related to known mining pools or attacker infrastructure using Sentinel Device tables.
Detects an unauthenticated remote code execution attempt against AhsayCBS via the UpdateReceivers.do endpoint using the checkSysPwd parameter, associated with CVE-2026-105133 and CVE-2026-105134.
Detects an unauthenticated remote code execution attempt against AhsayCBS via the UpdateReceivers.do endpoint using the checkSysPwd parameter, associated with CVE-2026-105133 and CVE-2026-105134.
Detects endpoint and network activity matching known Indicators of Compromise (IOCs) associated with BPFDoor, AVERAT, and Rekoobe backdoors based on Rapid7 research. The query checks for specific file hashes, IP addresses, and domain names across process, file, image load, network, and DNS events.
Detects endpoint and network activity matching known Indicators of Compromise (IOCs) associated with BPFDoor, AVERAT, and Rekoobe backdoors based on Rapid7 research. The query checks for specific file hashes, IP addresses, and domain names across process, file, image load, network, and DNS events.
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