Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
30 detections
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Detects the use of the 'xattr' command to remove the 'com.apple.quarantine' extended attribute from a file. This attribute is used by macOS Gatekeeper to identify files downloaded from the internet and trigger security warnings. Removing it is a common technique to bypass Gatekeeper and execute untrusted software without user notification.
Detects the use of osascript to generate fake macOS system-style dialog boxes that solicit user credentials or administrator privileges. This technique is often employed by macOS infostealers to conduct GUI-based credential harvesting or to bypass user interaction requirements for privilege escalation.
Detects dropper/staging scripts that curl-fetch a second-stage Mach-O payload to /tmp/helper or /tmp/update, chmod +x it immediately afterward in the same command sequence, ad-hoc/unsigned sign it, and strip quarantine attributes, while excluding scripts that verify a legitimate Developer ID signature
This rule detects macOS infostealer activity, such as AMOS, MacSync, or SHub, by identifying suspicious, correlated access to sensitive credential stores including the macOS Keychain, web browser credential databases (Chrome), SSH keys, and cryptocurrency wallet configuration files. It uses a combination of path/command-line strings and generic indicators of credential harvesting behavior.
Detects scripted use of the macOS 'security find-generic-password' command targeting the Chrome Keychain item from a shell or unsigned temp-directory parent process, consistent with BlueNoroff's post-ClickFix Keychain master-key theft technique.
Detects modifications to the OpenClaw agent's SOUL.md file, which dictates the core behavior and instructions of the agent. Attackers exploit this by tampering with the file to persistently alter the agent's actions across all future sessions without needing traditional code execution, as seen in attacks against NVIDIA NemoClaw sandboxes.
Detects a command line pattern where `curl` is used to download a payload, which is then piped to `base64` for decoding and `gunzip` for decompression. This technique is commonly used by macOS infostealers like DigitStealer and MacSync to execute malware in memory without writing it to disk.
Detects the execution of a piped command chain typical of 'ClickFix' lures used by the MacSync infostealer. The malware tricks users into pasting a command into the terminal which uses curl to download a payload, decodes it with base64, and pipes it directly into a shell (zsh, sh) or osascript for execution, bypassing macOS security controls like Gatekeeper.
Detects the execution of `sysctl` commands querying for specific Apple Silicon M2+ hardware features. This behavior is used by the DigitStealer malware for anti-analysis purposes to evade detection and target specific hardware.
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.
This analytic detects the configuration of TFTP services on Cisco IOS devices that could be used to exfiltrate sensitive configuration files. Threat actors like Static Tundra have been observed configuring TFTP servers to make device configuration files accessible for exfiltration after gaining initial access. The detection specifically looks for commands that expose critical configuration files such as startup-config, running-config, and other sensitive system information through TFTP. This activity is particularly concerning as it may represent an attempt to steal credentials, network topology information, and other sensitive data stored in device configurations.
Detects the persistence mechanism and command and control (C2) communication used by the Atomic macOS Stealer (AMOS) backdoor. This includes the creation of a LaunchDaemon, download of a helper binary, and C2 task polling.
This analytic detects suspicious modifications to Unix shell configuration files, such as `.bashrc` or `.profile`, which may indicate an attempt to alter system behavior or gain unauthorized access. Unauthorized changes to these files can be used to execute malicious commands, escalate privileges, or hide malicious activities. By monitoring for unusual or unauthorized modifications to shell configuration files, this analytic helps identify potential security threats, allowing security teams to respond quickly and mitigate risks.
The following analytic detects attempts to stop a service on Linux systems. It leverages data from Linux Auditd. This activity is significant as adversaries often stop or terminate security or critical services to disable defenses or disrupt operations, as seen in malware like Industroyer2. If confirmed malicious, this could lead to the disabling of security mechanisms, allowing attackers to persist, escalate privileges, or deploy destructive payloads, severely impacting system integrity and availability.
The following analytic detects the suspicious auditd service stop. This behavior is critical for a SOC to monitor because it may indicate attempts to gain unauthorized access or maintain control over a system. Such actions could be signs of malicious activity. If confirmed, this could lead to serious consequences, including a compromised system, unauthorized access to sensitive data, or even a wider breach affecting the entire network. Detecting and responding to these signs early is essential to prevent potential security incidents.
The analytic detects cloud API calls executed by user roles that have not previously run these commands. It leverages the Change data model in Splunk to identify commands executed by users with the user_type of AssumedRole and a status of success. This activity is significant because new commands from different user roles can indicate potential malicious activity or unauthorized actions. If confirmed malicious, this behavior could lead to unauthorized access, data breaches, or other damaging outcomes by exploiting new or unmonitored commands within the cloud environment.
The analytic detects the creation of cloud compute instances using previously unseen image IDs. It leverages cloud infrastructure logs to identify new image IDs that have not been observed before. This activity is significant because it may indicate unauthorized or suspicious activity, such as the deployment of malicious payloads or unauthorized access to sensitive information. If confirmed malicious, this could lead to data breaches, unauthorized access, or further compromise of the cloud environment. Immediate investigation is required to determine the legitimacy of the instance creation and to mitigate potential threats.
This analytic aggregates and summarizes alerts from Microsoft Defender for Endpoint. It extracts details such as source, file name, severity, process command line, IP address, registry key, signature, description, and unique ID. The detection is not intended to identify new activity from raw data but rather to leverage existing Microsoft Defender for Endpoint alerts for correlation and risk-based alerting. It filters out alerts with a 'clean' verdict and dynamically maps MITRE techniques and assigns risk scores based on the alert's severity.
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