Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
159 detections
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Detects process command lines containing Magento database audit strings (cms_block, cms_page) alongside specific loader domains, or network events communicating with these known loader domains.
This detection identifies potentially suspicious usage of sqlcmd.exe, focusing on command patterns that may indicate data exfiltration, reconnaissance, or malicious database operations. The detection looks for both short-form (-X) and long-form (--flag) suspicious parameter combinations, which have been observed in APT campaigns targeting high-value organizations. For example, threat actors like CL-STA-0048 have been known to abuse sqlcmd.exe for data theft and exfiltration from compromised MSSQL servers. The detection monitors for suspicious authentication attempts, output redirection, and potentially malicious query patterns that could indicate unauthorized database access or data theft.
Detects anomalous, high-volume data retrieval from Salesforce via OAuth-authenticated Connected Apps using SOQL or Bulk API. This behavior is indicative of a compromised integration token being used by an adversary to exfiltrate sensitive CRM data, such as User, Account, Case, or Opportunity records, by bypassing standard user interface workflows.
Detects anomalous large-scale data export operations in Google BigQuery (via jobs.insert or jobs.query) performed by identities (service accounts, API keys) not previously observed in the environment. This activity is indicative of credential abuse where stolen third-party analytics or monitoring service tokens are utilized for illicit data exfiltration, a behavior consistent with tactics employed by threat actors such as ShinyHunters.
Detects anomalous or potentially malicious LDAP queries on Domain Controllers, utilizing Event ID 1644 to monitor query filters. The rule identifies patterns commonly associated with reconnaissance and enumeration activities, including Kerberoasting (SPN enumeration), AS-REP roasting, delegation abuse, Active Directory Certificate Services (AD CS) enumeration, BloodHound collection, and bulk user, group, or computer discovery.
Detects a suspicious sequence of events where a remote access tool (RAT) is installed on a device, followed by significantly elevated and anomalous database query activity performed by the same account over an extended period. The rule utilizes a baseline window to identify deviations from normal query volumes while excluding known service accounts and administrative ETL/BI tasks.
Detects a suspicious sequence of events where a remote access tool (RAT) is installed on a device, followed by significantly elevated and anomalous database query activity performed by the same account over an extended period. The rule utilizes a baseline window to identify deviations from normal query volumes while excluding known service accounts and administrative ETL/BI tasks.
Detects a suspicious sequence of events where a remote access tool (RAT) is installed on a device, followed by significantly elevated and anomalous database query activity performed by the same account over an extended period. The rule utilizes a baseline window to identify deviations from normal query volumes while excluding known service accounts and administrative ETL/BI tasks.
Detects anomalous database activity on Magento e-commerce platforms where a SELECT operation against sensitive payment-related tables is followed by a DELETE operation on the same table within the same host context. This sequence is indicative of an attacker exfiltrating credit card data and subsequently attempting to remove forensic evidence of the theft.
Detects a suspected automated exploitation sequence where a web storefront file (e.g., .phtml, .js, .php) is modified to inject malicious script code (skimmer), followed by an unauthorized SQL DELETE operation on sensitive database tables containing payment, customer, or order information, originating from the same host within a 6-hour window.
Detects high-risk destructive database operations (DROP, TRUNCATE, or UPDATE) on sensitive tables executed within six hours of a large outbound data transfer from the same host, a pattern indicative of anti-forensic database wiping following data exfiltration.
Detects suspicious command line activity or network interactions targeting MongoDB, characterized by the use of 'eval' combined with specific function calls (such as 'process', 'load', 'require', or constructor-chaining) indicative of attempts to escape the MongoDB JavaScript sandbox.
Detects a suspicious sequence of events where a remote access tool (RAT) is installed on a device, followed by significantly elevated and anomalous database query activity performed by the same account over an extended period. The rule utilizes a baseline window to identify deviations from normal query volumes while excluding known service accounts and administrative ETL/BI tasks.
Detects a suspicious sequence of events where a remote access tool (RAT) is installed on a device, followed by significantly elevated and anomalous database query activity performed by the same account over an extended period. The rule utilizes a baseline window to identify deviations from normal query volumes while excluding known service accounts and administrative ETL/BI tasks.
Detects the execution of destructive SQL commands (e.g., DROP, TRUNCATE, mass DELETE) via common command-line database clients. These patterns are often observed when threat actors attempt to destroy forensic evidence or interrupt database availability immediately following unauthorized data exfiltration.
Detects a single Salesforce user or connected-app session performing an anomalous volume of SOQL queries against sensitive objects including Case, Account, Contact, and Opportunity. This behavior is indicative of mass data enumeration and potential exfiltration, as observed in historical incidents where attackers targeted CRM platforms to extract embedded secrets and sensitive record data.
Detects anomalous large-scale data export operations in Google BigQuery (via jobs.insert or jobs.query) performed by identities (service accounts, API keys) not previously observed in the environment. This activity is indicative of credential abuse where stolen third-party analytics or monitoring service tokens are utilized for illicit data exfiltration, a behavior consistent with tactics employed by threat actors such as ShinyHunters.
Detects AI agent tooling (e.g., LangChain, Copilot, Claude Code) performing read operations on databases containing sensitive data patterns, or performing suspicious bulk data enumeration. The rule monitors Azure SQL Database security audit events for suspicious application name signatures and non-schema introspective queries.
Detects AI agent tooling (e.g., LangChain, Copilot, Claude Code) performing read operations on databases containing sensitive data patterns, or performing suspicious bulk data enumeration. The rule monitors Azure SQL Database security audit events for suspicious application name signatures and non-schema introspective queries.
Detects anomalous data mutations in machine learning training pipelines, such as bulk insertions or label distribution shifts, performed by low-reputation or untrusted contributors. This activity is indicative of attempts to poison model training data to induce backdoors or skew decision boundaries.
Detects AI agent tooling (e.g., LangChain, Copilot, Claude Code) performing read operations on databases containing sensitive data patterns, or performing suspicious bulk data enumeration. The rule monitors Azure SQL Database security audit events for suspicious application name signatures and non-schema introspective queries.
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