Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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KQL Query
Detects a multi-stage attack chain where an AI-agent's configuration file (e.g., mcp.json) is modified, followed immediately by the execution of a tool process (node/python) spawned by the agent, the subsequent spawning of a shell process, and finally an outbound network connection to a rare, non-standard domain. This sequence is indicative of AI agent tool poisoning, where malicious tool definitions are introduced to execute arbitrary commands and exfiltrate data. Coverts T1195.002, T1565.001, T1059
Detects unauthorized processes (excluding firefox.exe and explorer.exe) accessing or modifying sensitive Firefox browser profile files, specifically places.sqlite (history/bookmarks) and cookies.sqlite (session cookies). This activity is often indicative of credential or session hijacking attempts by malware or malicious scripts.
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 modifications to the creation time of web-accessible script files (.aspx, .php, .asp, .jsp) located in common web directories (wwwroot, inetpub). Adversaries often use timestomping to hide the creation of backdoored web shells, making them appear older and potentially bypassing forensic or integrity-checking mechanisms.
This rule detects the suspicious copying of a Chromium-based browser 'Web Data' file (which stores sensitive information like saved passwords, credit cards, or address data) to a temporary file, followed by an outbound network connection to a specific exfiltration endpoint pattern within a 30-minute window.
Detects the modification or creation of a 'loginwindow.plist' file within the '/Library/LaunchAgents/' directory, correlated with a process containing 'renderer' and 'launchctl' or 'LaunchAgents' in its command line. This pattern suggests potential persistence mechanisms being established by a renderer process (e.g., an Electron-based application or browser process).
Detects instances where the Redis server process or the Redis AOF check utility writes directly to common SSH authorized_keys file paths. This behavior is indicative of an attacker exploiting a Redis misconfiguration or vulnerability (e.g., Redis RCE) to establish persistence by injecting an unauthorized SSH key.
Detects attempts to modify the Redis configuration (dir or dbfilename) followed by a SAVE command, which can be leveraged to write arbitrary data to critical system files such as crontabs for persistence.
Detects potential abuse of Cloud Shell to persist access in cloud environments. For GCP, it monitors the injection of SSH keys into instance or project metadata from a Cloud Shell session. For Azure, it detects a pattern involving Cloud Shell storage provisioning followed by a subsequent VM run-command or SSH public key update, indicating potential unauthorized management of virtual machines.
This rule detects unauthorized access, creation, or modification of Git credential files (.git-credentials, .netrc) by processes other than standard Git credential management utilities. It also monitors command-line activity that references these credential stores to identify potential attempts to exfiltrate or manipulate stored credentials.
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 persistence and execution abuse within hidden IDE/AI assistant workspace directories (e.g., .vscode, .cursor, .claude). The rule monitors for the creation or modification of configuration files used for automated task execution and identifies process execution patterns originating from these directories that invoke downloaders (curl, wget) or decoders (base64) to fetch or execute payloads.
This rule detects the creation, modification, or renaming of .lnk shortcut files within the Windows Startup folders (both current user and roaming appdata). It then correlates this activity with the execution of AutoIt3.exe or processes identified as 'AutoIt' that are running scripts with .a3x or .au3 extensions, which are commonly used for persistence and execution of malicious automation scripts.
Detects persistence modification via Registry Run/RunOnce keys or the addition of suspicious files to the Windows Startup folder. The rule specifically monitors for registry values pointing to common volatile directories, script extensions, or suspicious command-line interpreters. It also flags new executables or scripts being written directly into startup directory paths. This rule covers both direct registry manipulation and startup folder placement, often associated with malware or adversary persistence mechanisms.
Detects unauthorized processes attempting to read or modify Discord's local storage leveldb files, which store user authentication tokens, and correlates this with suspicious network activity to Discord's API or the presence of token-stealing keywords in command-line arguments.
This rule detects suspicious activity originating from the PaperCut 'pc-app.exe' process. It monitors for the creation or execution of a file named 'mscopilot.exe' within the non-standard directory 'C:\Microsoft.Office365\'. The rule matches based on a known malicious SHA1 hash or the specific file-write-then-execute behavior within a one-hour window, indicating potential AdaptixC2 loader activity.
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 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.
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.
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