Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
338 detections
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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 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.
The analytic detects instances of `regsvcs.exe` running without command line arguments. This behavior typically indicates process injection, where another process manipulates `regsvcs.exe`. The detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names, IDs, and command-line executions. This activity is significant as it may signal an attempt to evade detection and execute malicious code. If confirmed malicious, the attacker could achieve code execution, potentially leading to privilege escalation, persistence, or access to sensitive information.
The following analytic detects the execution of "dsquery.exe" with arguments targeting `TrustedDomain` queries directly from the command line. This behavior is identified using Endpoint Detection and Response (EDR) telemetry, focusing on process names and command-line arguments. This activity is significant as it often indicates domain trust discovery, a common step in lateral movement or privilege escalation by adversaries. If confirmed malicious, this could allow attackers to map domain trusts, potentially leading to further exploitation and unauthorized access to trusted domains.
The analytic identifies unauthorized replication RPC calls from non-domain controller devices. It leverages Zeek wire data to detect specific RPC operations like DrsReplicaAdd and DRSGetNCChanges, filtering out legitimate domain controllers. This activity is significant as it may indicate an attempt to introduce a rogue domain controller, which can compromise the integrity of the Active Directory environment. If confirmed malicious, this could allow attackers to manipulate directory data, escalate privileges, and persist within the network, posing a severe security risk.
The following analytic detects the presence of punycode within x509 certificates using Zeek x509 logs. It identifies punycode in the subject alternative name email and other fields by searching for the "xn--" prefix. This activity is significant as punycode can be used in phishing attacks or to bypass domain filters, posing a security risk. If confirmed malicious, attackers could use these certificates to impersonate legitimate domains, potentially leading to unauthorized access or data breaches.
The following analytic detects the execution of ngrok.exe on a Windows operating system. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process names and command-line arguments. This activity is significant because while ngrok is a legitimate tool for creating secure tunnels, it is increasingly used by adversaries to bypass network defenses and establish reverse proxies. If confirmed malicious, this could allow attackers to exfiltrate data, maintain persistence, or facilitate further attacks by tunneling traffic through the compromised system.
The analytic identifies Domain Generation Algorithm (DGA) generated domains using a pre-trained deep learning model. It leverages network resolution data to analyze domain names and detect unusual character sequences indicative of DGA activity. Adversaries often use DGAs to generate numerous domain names for command-and-control servers, making it harder to block malicious traffic. If confirmed malicious, this activity could enable attackers to maintain persistent communication with compromised systems, evade detection, and execute further malicious actions.
This analytic detects the execution of the 'shred' command on a Linux machine, which is used to overwrite files to make them unrecoverable. It leverages data from Linux Auditd, focusing on process names and command-line arguments. This activity is significant because the 'shred' command can be used in destructive attacks, such as those seen in the Industroyer2 malware targeting energy facilities. If confirmed malicious, this activity could lead to the permanent destruction of critical files, severely impacting system integrity and data availability.
The analytic identifies modifications to the registry path .wav\\OpenWithProgIds, associated with the Snake Malware campaign. This activity is significant because Snake's WerFault.exe uses this registry path to decrypt an encrypted blob containing critical components like the AES key, IV, and paths for its kernel driver and loader. If confirmed malicious, this could allow the attacker to load and execute Snake's kernel driver, leading to potential system compromise and persistent access.
The following analytic identifies DNS requests with unusually large query lengths for the record type being requested. It leverages the Network_Resolution data model and applies a machine learning model to detect outliers in DNS query lengths. This activity is significant because unusually large DNS queries can indicate data exfiltration or command-and-control communication attempts. If confirmed malicious, this activity could allow attackers to exfiltrate sensitive data or maintain persistent communication channels with compromised systems.
The analytic identifies the use of SNICat tool commands within the TLS SNI field, indicating potential data exfiltration attempts. It leverages Zeek SSL data to detect specific SNICat commands such as LIST, LS, SIZE, LD, CB, EX, ALIVE, EXIT, WHERE, and finito in the server_name field. This activity is significant as SNICat is a known tool for covert data exfiltration using TLS. If confirmed malicious, this could allow attackers to exfiltrate sensitive data undetected, posing a severe threat to data confidentiality and integrity.
Detects HTTP requests to low reputation Top-Level Domains (TLDs) or for files with suspicious extensions, which may indicate an attempt to download malicious content or establish command and control.
Detects HTTP requests to low reputation TLDs (e.g. .xyz, .top, .ru) or ending in suspicious file extensions (.exe, .dll, .hta), which may indicate malicious activity.
This rule detects a suspicious sequence of events: a process launching with a low prevalence executable (meaning it's rarely seen in the environment) followed by that same process initiating an HTTP connection to a low prevalence domain (a domain rarely accessed in the environment) within a 10-minute window. This combination can indicate the execution of novel or custom malware attempting to establish command and control or exfiltrate data to an unusual destination.
Detects suspicious DNS queries to Monero mining pools
This rule detects the transfer of sensitive files containing credential data over network shares. It specifically looks for filenames or paths commonly associated with credential dumping, such as 'mimidrv', 'lsass', 'windows\minidump\', 'hiberfil', 'sqldmpr', 'sam', 'ntds.dit', and 'security'. This activity often indicates an attempt to exfiltrate credentials after they have been dumped from a system.
Detects Kerberos Ticket-Granting Service (TGS) requests that utilize RC4 encryption. This is often indicative of Kerberoasting attacks, where adversaries request service tickets for Service Principal Names (SPNs) and then attempt to crack the RC4-encrypted hash offline to obtain plaintext credentials. The rule specifically excludes requests from computer accounts (service accounts ending with '$') to reduce false positives.
This rule detects suspicious DNS queries where the Z flag bit in the DNS protocol header is set to a non-zero value. The Z flag is reserved (unused) per IETF design, though it has recently been used in DNSSec. A non-zero Z flag value should be rare outside of legitimate DNSSec usage. The rule includes exclusions for common TLDs, query types (NS, MX), responses, and NetBIOS related traffic to reduce false positives. The rule's intent is to identify potential command and control (C2) or data exfiltration attempts leveraging DNS anomalies.
Detects remote credential dumping activity using Impacket's secretdump tool by monitoring SMB file access patterns. Specifically, it looks for access to the ADMIN$ share with paths containing 'SYSTEM32\' and ending with '.tmp', which is characteristic of secretdump creating temporary files during its operation.
This rule identifies DNS queries made to domains associated with known cryptocurrency mining pools. It specifically looks for DNS queries where the 'query' field ends with any of the listed mining pool domains. The rule excludes queries with answers '127.0.0.1' or '0.0.0.0' and those marked as rejected, to reduce false positives. The primary goal is to detect potential resource hijacking through cryptomining on compromised systems.
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