Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects a file named dnsapi.dll created/modified outside System32/SysWOW64/WinSxS (the only legitimate locations for the real system DLL). Now requires the file to BOTH land in one of the known NeedyMantis sideload staging folders AND match the published 3448-byte spoofed-config size -- previously the size check alone could match unrelated dnsapi.dll copies anywhere on disk; requiring both signals together removes that bypass.
Detects a file named dnsapi.dll created/modified outside System32/SysWOW64/WinSxS (the only legitimate locations for the real system DLL). Now requires the file to BOTH land in one of the known NeedyMantis sideload staging folders AND match the published 3448-byte spoofed-config size -- previously the size check alone could match unrelated dnsapi.dll copies anywhere on disk; requiring both signals together removes that bypass.
Detects Impacket-style hands-on-keyboard staging: a NeedyMantis bundle file dropped by cmd.exe/wmiprvse.exe/services.exe carrying the distinctive remote-execution fingerprint (ADMIN$ share reference, %COMSPEC% invocation, or 2>&1 output redirection used by wmiexec/smbexec/atexec), followed within 15 minutes by execution referencing the same bundle folder with the same fingerprint. Requiring the ADMIN$/%COMSPEC%/redirection fingerprint on both the copy and the execution -- rather than just matching on common process names like cmd.exe or svchost.exe -- removes the bulk of ordinary software installation and update activity that also uses cmd.exe to stage files.
Detects behavior indicative of the MLTBackdoor malware, specifically the creation or modification of a DLL file (such as *dlp.dll) shortly after or before the creation of an RC4-encrypted 'data.bin' file on the same host. The rule also looks for an optional preceding archive extraction event of common file types from Temp or Downloads directories by standard extraction utilities, which may indicate the staging of the initial payload.
Detects the modification of AI agent configuration files (e.g., mcp.json, config.json) followed shortly by the agent spawning a child process that matches typical Model Context Protocol (MCP) server execution patterns. This behavior may indicate an attacker has modified the configuration to inject malicious server commands or tools that the AI agent will execute upon next initialization, effectively achieving persistent code execution via the agent's legitimate functionality.
Detects the modification of AI agent configuration files (e.g., mcp.json, config.json) followed shortly by the agent spawning a child process that matches typical Model Context Protocol (MCP) server execution patterns. This behavior may indicate an attacker has modified the configuration to inject malicious server commands or tools that the AI agent will execute upon next initialization, effectively achieving persistent code execution via the agent's legitimate functionality.
Detects the modification of AI agent configuration files (e.g., mcp.json, config.json) followed shortly by the agent spawning a child process that matches typical Model Context Protocol (MCP) server execution patterns. This behavior may indicate an attacker has modified the configuration to inject malicious server commands or tools that the AI agent will execute upon next initialization, effectively achieving persistent code execution via the agent's legitimate functionality.
Detects instances where AI-based coding or agent-driven tools (e.g., Cursor, Claude Code, AutoGPT) perform suspicious file archiving/packaging activities followed by network egress to unauthorized or known exfiltration-prone services. This helps identify potential data exfiltration by AI agents or compromised development environments.
Detects instances where AI-based coding or agent-driven tools (e.g., Cursor, Claude Code, AutoGPT) perform suspicious file archiving/packaging activities followed by network egress to unauthorized or known exfiltration-prone services. This helps identify potential data exfiltration by AI agents or compromised development environments.
Detects unauthorized processes attempting to read or copy sensitive browser data files (e.g., Cookies, Login Data, Local Storage) often targeted by info-stealing malware such as Vidar, LummaC2, or RedLine. The rule filters out legitimate browser-related processes and adds security context by requiring evidence of unsigned code, execution from suspicious locations (Temp/Downloads), or associated outbound network activity to reduce false positives.
This rule detects modifications to Group Policy Objects (GPOs) that are either explicitly marked as malicious based on known indicators (GUIDs or 'PAYLOAD' strings) or involve unauthorized modifications to domain-root or organizational unit GPO links. It monitors Active Directory security event logs for object changes.
This rule detects modifications to Group Policy Objects (GPOs) that are either explicitly marked as malicious based on known indicators (GUIDs or 'PAYLOAD' strings) or involve unauthorized modifications to domain-root or organizational unit GPO links. It monitors Active Directory security event logs for object changes.
Detects suspicious file create or write operations within the domain SYSVOL Policies directory. This pattern is commonly associated with attackers attempting to deploy malicious payloads, such as ransom notes or configuration changes, via Group Policy Objects (GPO). The rule monitors Windows Event ID 4663 to identify unauthorized modification attempts by non-system accounts.
This rule detects potential execution of Vidar Stealer by identifying specific diagnostic loader strings (e.g., 'Loader: write failed') within file modification and process execution events. The presence of these strings in file metadata or command-line arguments indicates stage-specific activity related to the malware's loading process.
Detects PowerShell processes using the .NET System.IO.File methods (SetLastWriteTime, SetCreationTime, SetLastAccessTime) to modify file timestamps, a common anti-forensic technique to obfuscate file creation or modification times.
This rule detects potential Training Data Poisoning (AML.T0020) by monitoring for anomalous activities in ML data pipelines. It triggers when ingestion jobs utilize untrusted sources, unregistered source paths, unauthorized accounts write to datasets, or when significant data volume bursts or schema/checksum drifts occur immediately prior to a scheduled training run.
Detects potential Business Email Compromise (BEC) fraud involving AI voice-cloning. The rule correlates external or unrecognized Microsoft Teams activity (calls/chats) directed at finance users with high-value financial transactions or vendor bank detail updates occurring on the same day, specifically when these transactions lack evidence of legitimate out-of-band or second-approver verification.
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 unauthorized modifications to AI agent configuration files, including system prompts, tool allow-lists, and MCP server registration files. Such changes are often performed by processes outside of approved configuration management or infrastructure-as-code pipelines, potentially allowing for persistent manipulation of AI model behavior and capabilities.
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.
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