Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects the rapid, bulk launch of GPU-optimized or compute-intensive EC2 instances via API calls such as RunInstances, CreateFleet, or RequestSpotFleet within a short time window. This behavior is often indicative of cloud resource hijacking or cryptojacking, where compromised credentials are used to spin up expensive compute resources for illicit mining or intensive processing.
Detects sign-in events to high-privileged cloud management consoles (e.g., Azure Portal, Microsoft Entra, Intune, Okta, AWS, GCP) originating from sessions with elevated risk or anomalous characteristics. The detection monitors for factors such as the use of anonymized IP addresses, unmanaged devices, sessions marked as 'atRisk' by identity providers, or non-interactive service-based access to management APIs that deviate from standard administrative workflow patterns.
The following analytic detects when all virtual machines on an ESXi host are abruptly terminated, which may indicate malicious activity such as a deliberate denial-of-service, ransomware staging, or an attempt to destroy critical workloads.
Detects potential misuse of AI agentic frameworks (e.g., Semantic Kernel) where the host process executes destructive commands (e.g., file deletion, wiping, shadow copy deletion) or performs large-scale file deletion, which may indicate unauthorized tool invocation or compromised AI agents.
Detects the use of native Windows binaries such as vssadmin.exe, wmic.exe, bcdedit.exe, and wbadmin.exe to inhibit system recovery by deleting shadow copies, clearing backup catalogs, or disabling automatic system recovery.
This rule detects the use of native Windows utilities (vssadmin, wmic, wbadmin, bcdedit) to perform actions associated with ransomware, such as deleting volume shadow copies, deleting the backup catalog, or disabling system recovery features. This is a common precursor to data encryption to prevent the user from restoring files.
Detects autonomous AI coding-agent processes (e.g., Claude Code, Cursor, Aider) spawning shell or git processes to perform potentially destructive actions such as recursive file deletion, forced git pushes, database manipulation, or unauthorized access to sensitive credential files, indicating agentic 'excessive agency' or compromise.
Detects potential poisoning of an AI agent's RAG knowledge base or vector store through suspicious bulk ingestion or injection of imperative instruction-style content. The rule correlates these ingestion events with subsequent anomalous tool usage patterns by the same agent session, indicating a possible prompt injection or data manipulation attack aimed at altering agent behavior.
Detects potential poisoning of an AI agent's RAG knowledge base or vector store through suspicious bulk ingestion or injection of imperative instruction-style content. The rule correlates these ingestion events with subsequent anomalous tool usage patterns by the same agent session, indicating a possible prompt injection or data manipulation attack aimed at altering agent behavior.
Detects coordinated activity indicative of destructive malware propagation similar to NotPetya. The rule identifies a three-stage sequence on a single host: execution of a DLL via rundll32.exe, followed by file writes to administrative network shares (C$/ADMIN$), and remote process creation via WMI (WmiPrvSE.exe), correlated with a high volume of SMB connections.
Detects a multi-stage attack pattern involving a non-MFA login to a Citrix portal, followed by lateral movement, and concluding with ransomware deployment behaviors such as shadow copy deletion, recovery disablement, or the dropping of ransom notes associated with ALPHV/BlackCat ransomware.
Detects a multi-stage attack pattern involving a non-MFA login to a Citrix portal, followed by lateral movement, and concluding with ransomware deployment behaviors such as shadow copy deletion, recovery disablement, or the dropping of ransom notes associated with ALPHV/BlackCat ransomware.
Detects a multi-stage attack pattern involving a non-MFA login to a Citrix portal, followed by lateral movement, and concluding with ransomware deployment behaviors such as shadow copy deletion, recovery disablement, or the dropping of ransom notes associated with ALPHV/BlackCat ransomware.
Detects a multi-stage attack pattern involving a non-MFA login to a Citrix portal, followed by lateral movement, and concluding with ransomware deployment behaviors such as shadow copy deletion, recovery disablement, or the dropping of ransom notes associated with ALPHV/BlackCat ransomware.
Flags MFA method deletions performed by an actor other than the account owner, especially when followed by registration of a new authenticator — the signature of a helpdesk agent socially engineered into swapping a victim's MFA factor.
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 an abnormally high frequency of tool/function invocations by an AI agent within a one-minute timeframe. This pattern is indicative of a potential hijack where an attacker forces the agent into a runaway loop to perform resource exhaustion (DoS), repetitive brute-force operations, or automated exfiltration of sensitive data via manipulated tool execution.
Detects unauthorized or suspicious modifications to AI model artifacts, including fine-tuned weights and LoRA adapters. This rule identifies tampering attempts via unsigned uploads, bypassing CI/CD production deployment approvals, or mismatches in hash/checksum compared to known-good versions.
Detects potential poisoning of an AI agent's RAG knowledge base or vector store through suspicious bulk ingestion or injection of imperative instruction-style content. The rule correlates these ingestion events with subsequent anomalous tool usage patterns by the same agent session, indicating a possible prompt injection or data manipulation attack aimed at altering agent behavior.
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 potential poisoning of machine learning training data by identifying unauthorized file modifications to datasets, DVC repositories, or model registries that occur outside of authorized ETL or CI/CD windows.
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