Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects the insertion of malicious prompt injection payloads—containing imperative commands to AI agents or hidden unicode/non-printable characters—into wiki pages, ticketing systems, or other shared knowledge bases. These payloads are designed to be ingested by AI agents or Retrieval-Augmented Generation (RAG) pipelines, leading to unauthorized instructions or behavioral overrides upon retrieval.
Detects instances where a process not associated with standard web browsers accesses, copies, or modifies sensitive browser files such as cookies, local storage, or session tokens. This behavior is highly indicative of information-stealer malware (e.g., Vidar, Lumma, RedLine) attempting to exfiltrate session data for account hijacking.
Detects the presence or execution of the 'Boot Time Removal Tool' (BTR.sys) driver when it originates from locations outside of the legitimate Windows Defender platform or is initiated by processes not associated with the Defender ecosystem. This behavior is indicative of potential BYOVD (Bring Your Own Vulnerable Driver) attacks where attackers deploy known vulnerable drivers to gain kernel-mode privileges or interfere with security software.
Detects sensitive cPanel/WHM administrative commands or system file modifications occurring within one hour of a suspected authentication bypass event, indicative of potential post-exploitation activity related to CVE-2026-41940.
Detects the use of PowerShell to remove the 'Zone.Identifier' NTFS Alternate Data Stream (ADS) from files. This stream is responsible for the 'Mark-of-the-Web' (MOTW) which triggers security warnings in Windows, including SmartScreen and Protected View. Adversaries use this technique to evade security controls on downloaded malicious files.
Detects anomalous multi-agent chaining where one AI-enabled agent instance initiates a connection to an AI API and subsequently hands off information to a second, distinct agent instance, which then performs an suspicious downstream action such as spawning a shell or initiating an unauthorized external network connection. This behavior is intended to identify potential abuse of AI-coding assistant tools for automated execution chains.
Detects anomalous multi-agent chaining where one AI-enabled agent instance initiates a connection to an AI API and subsequently hands off information to a second, distinct agent instance, which then performs an suspicious downstream action such as spawning a shell or initiating an unauthorized external network connection. This behavior is intended to identify potential abuse of AI-coding assistant tools for automated execution chains.
Detects anomalous multi-agent chaining where one AI-enabled agent instance initiates a connection to an AI API and subsequently hands off information to a second, distinct agent instance, which then performs an suspicious downstream action such as spawning a shell or initiating an unauthorized external network connection. This behavior is intended to identify potential abuse of AI-coding assistant tools for automated execution chains.
Detects anomalous multi-agent chaining where one AI-enabled agent instance initiates a connection to an AI API and subsequently hands off information to a second, distinct agent instance, which then performs an suspicious downstream action such as spawning a shell or initiating an unauthorized external network connection. This behavior is intended to identify potential abuse of AI-coding assistant tools for automated execution chains.
Detects anomalous multi-agent chaining where one AI-enabled agent instance initiates a connection to an AI API and subsequently hands off information to a second, distinct agent instance, which then performs an suspicious downstream action such as spawning a shell or initiating an unauthorized external network connection. This behavior is intended to identify potential abuse of AI-coding assistant tools for automated execution chains.
Detects suspicious force push events to the 'main' branch in GitHub repositories. The rule flags pushes that include hardcoded commit hashes associated with known malicious activity, pushes where the author and committer identities do not match, or pushes containing specific indicators such as 'indexe.cjs' or 'preinstall' scripts which are common in software supply chain attacks.
This rule detects potentially malicious modifications to package.json files in npm projects or suspicious npm registry telemetry. It flags manifest files containing common obfuscation markers or excessive/unusual dependencies often associated with supply chain attacks, such as crypto, child_process, and common third-party packages frequently used in malicious npm activity.
Detects unauthorized modifications to SSH authorized_keys files. Attackers often modify these files to inject their own public keys, ensuring persistent access to a compromised Linux host. This rule excludes known configuration management and provisioning tools to reduce noise.
Detects the modification of registry keys under HKCU\Software\Classes\CLSID\...\InProcServer32, which is a common method for achieving persistence. The rule flags when these registry keys point to file paths within the local user profile (AppData), which is frequently used by malicious actors to load custom DLLs when the corresponding COM object is initialized.
This rule detects potentially malicious activity involving Node.js modules. It monitors for the deletion of specific 'sharedLoad.min.js' or 'extended' files within 'node_modules' directories, the execution of Node.js processes attempting file deletion operations (like 'unlink' or 'rm') within 'node_modules', and the creation or modification of JavaScript files within 'node_modules' that contain 'btree' references. This activity may indicate an attempt to tamper with application dependencies, inject malicious code, or remove artifacts.
Detects execution of Node.js processes loading specifically named JavaScript files ('sharedLoad.min.js') within the 'node_modules/indexed-btree' directory, or file modifications involving these specific paths, which may indicate a malicious loader or supply chain compromise attempt.
Detects a multi-stage, automated attack chain performed by AI-based development agents (e.g., Claude, Copilot, Cursor). The rule correlates process discovery, credential access, file collection (staging), and exfiltration over web services within a tight 15-minute window, identifying potential autonomous exploitation and data exfiltration. Covered T1046, T1552.001, T1078, T1005, T1567
Detects a multi-stage, automated attack chain performed by AI-based development agents (e.g., Claude, Copilot, Cursor). The rule correlates process discovery, credential access, file collection (staging), and exfiltration over web services within a tight 15-minute window, identifying potential autonomous exploitation and data exfiltration. Covered T1046, T1552.001, T1078, T1005, T1567
Detects a multi-stage, automated attack chain performed by AI-based development agents (e.g., Claude, Copilot, Cursor). The rule correlates process discovery, credential access, file collection (staging), and exfiltration over web services within a tight 15-minute window, identifying potential autonomous exploitation and data exfiltration. Covered T1046, T1552.001, T1078, T1005, T1567
Detects a multi-stage, automated attack chain performed by AI-based development agents (e.g., Claude, Copilot, Cursor). The rule correlates process discovery, credential access, file collection (staging), and exfiltration over web services within a tight 15-minute window, identifying potential autonomous exploitation and data exfiltration. Covered T1046, T1552.001, T1078, T1005, T1567
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.
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