Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects file events related to the 'plain-crypto-js' folder path, which has been associated with the AXIOS supply chain compromise. This rule monitors for any file activity within this specific folder, indicating potential malicious code injection or modification as part of a software supply chain attack.
This rule detects network connections to a predefined list of known Microstealer malicious domains. These domains are often associated with malware command and control (C2) infrastructure, phishing, or other malicious activities. Monitoring connections to such domains can help identify compromised systems or active malware infections.
Detects the creation or modification of a Claude Code settings file (`.claude/settings.json`). This file can be configured to automatically approve Model Context Protocol (MCP) servers by setting `enableAllProjectMcpServers` to `true` or by using `enabledMcpjsonServers`. This bypasses security prompts designed to warn users about potential command execution, which can lead to Remote Code Execution (RCE) as described in CVE-2025-59536.
Detects the 'codesign' utility being used to extract code-signing requirements from trusted macOS system binaries. This is a key step used by malware to forge TCC permissions and inherit the privileges of trusted applications. The rule specifically looks for 'codesign' execution with '-d' and '--requirements' flags targeting common trusted binaries, initiated by scripting interpreters.
Detects the creation or modification of a `.vscode/tasks.json` file that is configured to automatically run a task when a folder is opened.
Adversaries can abuse this feature in VS Code and GitHub Codespaces to achieve remote code execution by tricking a user into opening a malicious repository or pull request.
Adversaries can abuse this feature in VS Code and GitHub Codespaces to achieve remote code execution by tricking a user into opening a malicious repository or pull request.
Detects the modification of a `.vscode/settings.json` file to include a `PROMPT_COMMAND`. This technique allows an attacker to execute arbitrary commands every time a new terminal is opened in a Linux-based VS Code environment, such as GitHub Codespaces. It can be used for persistence or to exfiltrate credentials and secrets.
Detects the use of lifecycle hooks in `.devcontainer/devcontainer.json` files to execute potentially malicious commands. Adversaries can abuse hooks like `postCreateCommand` or `postStartCommand` in development environments like GitHub Codespaces to automatically run scripts upon container creation. This can be used to exfiltrate secrets, such as GitHub tokens, or establish further access.
Detects the use of lifecycle hooks in `.devcontainer/devcontainer.json` files to execute potentially malicious commands. Adversaries can abuse hooks like `postCreateCommand` or `postStartCommand` in development environments like GitHub Codespaces to automatically run scripts upon container creation. This can be used to exfiltrate secrets, such as GitHub tokens, or establish further access.
Detects network connections to Polygon (MATIC) blockchain RPC endpoints. The DeadLock ransomware strain has been observed using smart contracts on the Polygon network to retrieve C2 proxy server addresses, making this a key indicator of compromise.
Detects the silent installation and configuration of AnyDesk from the command line. This technique was observed being used by actors deploying DeadLock ransomware to establish persistence.
Detects the PeerBlight Linux backdoor masquerading as the '[ksoftirqd]' kernel thread. This is a defense evasion technique used by the malware to blend in with legitimate system processes.
Detects a process loading a commonly hijacked system DLL from a non-standard file path that is unsigned. This is indicative of DLL Search Order Hijacking, a technique used by malware like Rhadamanthys to achieve persistence and stealthy execution.
The following analytic detects the use of default or publicly known named pipes associated with Cobalt Strike. It leverages Sysmon EventID 17 and 18 to identify specific named pipes commonly used by Cobalt Strike's Artifact Kit and Malleable C2 Profiles. This activity is significant because Cobalt Strike is a popular tool for adversaries to conduct post-exploitation tasks, and identifying its named pipes can reveal potential malicious activity. If confirmed malicious, this could indicate an active Cobalt Strike beacon, leading to unauthorized access, data exfiltration, or further lateral movement within the network.
This analytic detects suspicious data transfer activities involving the use of the `split` syscall with the `-b` option, which specifies the size of each output file. This technique can be used by attackers to evade detection by breaking large files into smaller parts, bypassing size-based security controls, and facilitating covert exfiltration of sensitive data. By monitoring for unusual or unauthorized use of the `split` syscall with a specified block size, this analytic helps identify potential data exfiltration attempts.
This analytic identifies the bulk creation of ransomware notes (e.g., .txt, .html, .hta files) on an infected machine. It leverages Sysmon EventCode 11 to detect multiple instances of these file types being created within a short time frame (10 seconds). A detection is triggered if 15 or more unique readme paths are observed. This activity is a strong indicator of an active ransomware attack, where the attacker is notifying the victim of the encryption. If confirmed malicious, this behavior could lead to widespread data encryption, rendering critical files inaccessible and potentially causing significant operational disruption.
The following analytic detects suspicious attempts to find credentials stored in password stores, indicating a potential attacker's effort to access sensitive login information. Password stores are critical repositories that contain valuable credentials, and unauthorized access to them can lead to significant security breaches. By monitoring for unusual or unauthorized activities related to password store access, this analytic helps identify potential credential theft attempts, allowing security teams to respond promptly and prevent unauthorized access to critical systems and data.
The following analytic detects multiple executions of Living off the Land (LOLbin) binaries on macOS within a short period. It leverages osquery to monitor process events and identifies commands such as "find", "crontab", "screencapture", "openssl", "curl", "wget", "killall", and "funzip". This activity is significant as LOLbins are often used by attackers to perform malicious actions while evading detection. If confirmed malicious, this behavior could allow attackers to execute arbitrary code, escalate privileges, or persist within the environment, posing a significant security risk.
This analytic detects the presence of "api.telegram.org" in the command line of a process. This activity is significant because the Telegram API has been used as an exfiltration mechanism or as a Command and Control (C2) channel by adversaries. If confirmed malicious, this could indicate an attacker or malware is exfiltrating data or receiving additional C2 instructions, potentially leading to further compromise and persistence within the network. The rule leverages process execution logs from Endpoint Detection and Response (EDR) agents.
The analytic detects AMOS Stealer VM check activity on macOS. It identifies the execution of the "osascript" command along with specific commandline strings that include "set", "system_profiler", and either "VMware" or "QEMU". This activity is indicative of AMOS stealer attempting to determine if it is running within a virtual machine environment. If confirmed malicious, this behavior suggests the host is infected by the AMOS stealer, which could lead to arbitrary code execution, privilege escalation, information theft, or persistence, posing a significant security risk.
The analytic detects file writes with extensions indicative of a SamSam ransomware attack. It leverages file-system activity data to identify file names ending in .stubbin, .berkshire, .satoshi, .sophos, or .keyxml. This activity is significant because SamSam ransomware is highly destructive, leading to file encryption and ransom demands. If confirmed malicious, the impact includes significant financial losses, operational disruptions, and reputational damage. Immediate actions should include isolating affected systems, restoring files from backups, and investigating the attack source to prevent further incidents.
The analytic detects modifications to files with extensions commonly associated with ransomware. It leverages the Endpoint.Filesystem data model to identify changes in file extensions that match known ransomware patterns. This activity is significant because it suggests an attacker is attempting to encrypt or alter files, potentially leading to severe data loss and operational disruption. If confirmed malicious, this activity could result in the encryption of critical data, rendering it inaccessible and causing significant damage to the organization's data integrity and availability.
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