Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
83 detections
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Detects when a dialog user reads a restricted SAP table using generic table browser transactions such as SE11, SE16, or SE16N.
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 abnormal bulk API operations (upsert, batch updates, or deletions) targeting vector database or Retrieval-Augmented Generation (RAG) knowledge stores. This activity is flagged when performed by non-authorized service identities or when ingestion volume exceeds a specific threshold within a short timeframe, potentially indicating an attempt to poison the agent's memory store or retrieval corpus to facilitate indirect prompt injection.
Detects high-volume database or CRM read activity performed by a user, followed by significant outbound network traffic to known public cloud storage, file-sharing, or messaging services. This pattern is consistent with data staging and exfiltration behaviors often attributed to the ShinyHunters threat actor group.
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.
Detects unauthorized or anomalous write operations (upsert/insert) to a vector database, which could indicate a Retrieval-Augmented Generation (RAG) injection attack. The rule monitors for writes from non-sanctioned identities, operations outside of established maintenance or ingestion windows, or entries that lack necessary provenance metadata such as source document IDs or content hashes.
Detects bulk insertions or content updates into vector databases, document stores, or knowledge bases that contain suspected instruction-override, jailbreak, or role-hijacking phrases, particularly when performed by unauthenticated or unknown identities.
Detects bulk insertions or content updates into vector databases, document stores, or knowledge bases that contain suspected instruction-override, jailbreak, or role-hijacking phrases, particularly when performed by unauthenticated or unknown identities.
Detects bulk insertions or content updates into vector databases, document stores, or knowledge bases that contain suspected instruction-override, jailbreak, or role-hijacking phrases, particularly when performed by unauthenticated or unknown identities.
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 high-risk destructive database operations (DROP, TRUNCATE, or mass DELETE without a WHERE clause) performed by automated identities (AI agents, service accounts, or automation scripts) in a production environment outside of an authorized change window. This rule is designed to identify potential excessive-agency incidents where automated systems perform unauthorized destructive actions.
This rule detects a sequence of events where a database user record is updated (inserting a new password hash) followed by a successful authentication for that same user, immediately following a recent login failure. This pattern is indicative of an attacker modifying credentials and then testing the new access.
This rule monitors for potential suspicious database operations executed via MySQL command-line utilities. It specifically detects the use of the AES_ENCRYPT function in conjunction with UPDATE statements, which is a technique often used by ransomware to encrypt database tables or data.
Detects a sequence of activity involving the use of the MySQL 'AES_ENCRYPT' function followed by a 'DROP TABLE', 'DROP SCHEMA', or 'DROP DATABASE' command on the same host within a 30-minute window. This behavior is consistent with extortion tactics, such as the JADEPUFFER campaign, where attackers encrypt sensitive database records and delete the original tables to compel ransom payments.
This rule detects the creation of a new administrator account in WordPress within one hour of a specific plugin update event (specifically for the 'admin-menu-editor-pro' plugin). This pattern is indicative of a post-exploitation activity where a compromised or vulnerable plugin is used to gain administrative access to the WordPress instance.
Detects the creation of a new administrator account in WordPress by monitoring for SQL INSERT operations targeting the wp_users and wp_usermeta tables that involve setting the wp_capabilities to administrator. This behavior is indicative of unauthorized account creation often associated with compromised or malicious WordPress plugin updates.
Detects potential database ransomware activity by correlating the usage of AES_ENCRYPT for record modification, followed by the dropping of tables and the insertion of ransom notes containing specific contact keywords (e.g., bitcoin, proton mail) within database queries or system logs.
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