Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
7,142 detections
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Detects unauthorized processes (those not identified as standard web browsers) accessing browser profile directories (Local Storage or IndexedDB) associated with major AI platforms such as OpenAI, ChatGPT, Anthropic, and Copilot. This behavior is indicative of credential theft or session token exfiltration from local browser storage.
Detects the execution of known adversary-in-the-middle (AiTM) phishing frameworks, such as Evilginx or Modlishka, by identifying process names, paths, or specific command-line arguments (e.g., --phishlets, --proxy, --lure) used to initiate these proxy-based phishing sessions.
Detects file creation or modification events targeting sensitive cloud credential storage locations, such as AWS credentials files, Azure configurations, and Google Cloud credentials databases. The rule filters out known legitimate CLI tools, flagging potential unauthorized access or exfiltration of cloud credentials by other processes.
Detects suspicious child processes spawned by a Java process, which may indicate post-exploitation activity related to the Oracle E-Business Suite vulnerability CVE-2025-61882. The exploit chain often results in a reverse shell.
Hunting rule for nmap used for internal network discovery.
Legitimate vulnerability management and pentests also run nmap; prioritize hits when the source is a server or workload not in your scanner inventory, the parent is an interactive shell or script dropped, or the event chains with
tunneling (chisel, proxychains), or lateral movement.
Strongest signals: RFC1918 subnet sweeps, proxychains-wrapped
-sT/-Pn scans, and full-port sweeps across private ranges.
Legitimate vulnerability management and pentests also run nmap; prioritize hits when the source is a server or workload not in your scanner inventory, the parent is an interactive shell or script dropped, or the event chains with
tunneling (chisel, proxychains), or lateral movement.
Strongest signals: RFC1918 subnet sweeps, proxychains-wrapped
-sT/-Pn scans, and full-port sweeps across private ranges.
Detects the installation of known malicious PyPI packages ('frint', 'skytext', 'slogsec', 'logcrypt.cryptography') associated with the ChocoPoC supply chain campaign. The rule monitors process execution commands related to 'pip' package installation and tracks the appearance of malicious compiled extension files ('gradient.pyd', 'gradient.so') on the filesystem.
Detects developers cloning a repository (mimicking a coding interview test) and immediately executing an anomalous binary or script hidden within the project structure. This behavior is indicative of the Void Dokkaebi threat actor's tactics.
Identifies package managers (npm, pip) executing pre-install scripts that perform immediate system reconnaissance (whoami, hostname, sw_vers, systeminfo) characteristic of Void Dokkaebi payloads. This rule looks for a sequence where a package manager initiates a process, and that initiated process then launches a reconnaissance command.
Detects the decoded execution of bash/sh reverse shells initiated from processes like node.exe or python.exe, which is indicative of the Void Dokkaebi threat actor's tactics. The rule specifically looks for command lines containing 'echo' with base64 encoded strings, followed by 'base64 -d' and then 'sh' or 'bash' for execution.
Detects unauthorized execution of Oracle DBMS_SCHEDULER jobs combined with UTL_FILE output retrieval, a TTP observed in Operation Escaneo. The rule identifies suspicious command lines involving common database-layer exploitation scripts, manipulation of DBMS_SCHEDULER jobs, and interaction with the /tmp directory to exfiltrate or read command outputs.
Detects anomalous process execution patterns originating from SAP system processes or accounts, such as SAP work processes spawning shell commands, usage of known RCE-related scripts, and privileged accounts like WF-BATCH or TMSADM invoking administrative command-line tools.
Detects anomalous process execution patterns where a database process (postgres or postmaster) or a common system utility (nc, python, bash) is observed initiating unauthorized network connections to a known external C2 IP or accessing sensitive cryptographic files (e.g., server.key). This behavior is indicative of potential post-exploitation activity, specifically the exfiltration of credentials or sensitive data via database-level command execution (sys_eval) or direct file manipulation.
Sshpass Lateral Movement
YARA-L
Detects sshpass automation usage by administrative accounts
Monitors for the use of 'sshpass' to automate SSH logins using specific accounts commonly associated with Oracle/PeopleSoft administration (psoft, oracle, linuxadm). This technique was observed in ShinyHunters attacks.
Detects the execution of portable Impacket tools ('mini_imp'), including psexec.py, secretsdump.py, and ntlmrelayx.py. This activity is indicative of potential credential harvesting, remote service execution, or NTLM relay attacks, as observed in the MexicanMafia 'Operation Escaneo' campaign.
Monitors for the use of 'sshpass' to automate SSH logins using specific accounts commonly associated with Oracle/PeopleSoft administration (psoft, oracle, linuxadm). This technique was observed in ShinyHunters attacks.
Detects the execution of various reconnaissance tools and scripts identified as part of the Kimera framework, utilized by the threat actor group MexicanMafia in Operation Escaneo. This rule monitors for specific tool names (subfinder, assetfinder, gobuster, dnsx, naabu, httpx, gowitness, findomain), associated Python scripts, and specific command-line arguments indicative of aggressive scanning behavior.
Detects the abuse of SAP RFC function modules SXPG_CALL_SYSTEM and SXPG_COMMAND_INSERT to execute arbitrary OS commands. This activity, observed in the Operation Escaneo campaign, indicates potential unauthorized system command execution via SAP application components, often leveraging specific ZREDTEAM modules or scripts.
This rule detects potentially malicious activity within Oracle database audit and alert logs, specifically monitoring for the usage of packages like DBMS_SCHEDULER or UTL_FILE to execute OS commands or manipulate files. It also flags the execution of known exploit scripts or reconnaissance queries commonly associated with database-based attacks and unauthorized data dumping.
Detects execution of known reconnaissance tools and scripts associated with the Kimera framework, including subdomain and asset enumeration utilities (e.g., subfinder, nuclei, httpx) or custom Python scripts. The rule also monitors for reconnaissance-related ulimit adjustments and concurrent bursts of multiple enumeration tools, which are indicative of automated scanning activity.
Detects evidence of potential data exfiltration activities including the execution of known exfiltration scripts, the use of netcat for unauthorized data transfer to a specific C2 IP, and the presence or access of BloodHound dump files. The rule also monitors for network communication with a known command and control IP address.
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