Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects the execution of Python scripts named 'exploit.py' or 'exp.py', which are common naming conventions associated with proof-of-concept or exploit code.
Detects execution and file artifacts associated with the 'fscan' network scanning tool, which is commonly used for internal network discovery and vulnerability scanning. The rule monitors for the presence of the fscan binary or evidence of its output files, specifically 'result.txt', often in combination with scanning parameters like port strings or target service references.
This rule detects potentially malicious command execution patterns in Linux environments. It monitors for shell commands (bash, sh, echo, tee, cat) involving base64 decoding redirected to shell configuration files (e.g., .profile) or specific patterns involving 'gs-dbus' processes, which are often used by threat actors to decode obfuscated payloads or maintain persistence.
Detects unauthorized devices or processes attempting to communicate with the Telegram Bot API (/sendMessage or /sendDocument) and monitors for credential-related terms within the request metadata. This often indicates the use of Telegram as a command-and-control (C2) channel for data exfiltration or credential theft.
Detects network traffic consistent with the download of XMRig cryptocurrency miner release tarballs from GitHub, typically used in malicious deployment scenarios including automated cron-triggered installations.
Detects a malicious command sequence targeting Redis instances where an attacker uses 'CONFIG SET' to modify the 'dir' and 'dbfilename' configuration directives followed by a 'SLAVEOF' or 'REPLICAOF' command. This is a common exploit pattern used to achieve remote code execution by forcing the Redis instance to load a malicious database file (often containing binary webshells or cron jobs) from an attacker-controlled server.
Detects the deployment and activity of SilkParasite-associated Linux threats, including XMRig cryptocurrency miner shell scripts and standalone Meterpreter agents. The rule also captures the execution of a custom Python fleet-scanning tool used to interact with exposed Redis instances for unauthorized command execution and infrastructure management.
Detects outbound HTTP GET requests containing a specific '/ping' URI pattern and 'h=' query string parameter, characteristic of Redis-based cryptomining botnet C2 heartbeat check-ins.
Detects the creation of persistence mechanisms on Linux systems using APT Pre/Post-Invoke hooks (e.g., /etc/apt/apt.conf.d/99sys-update) or user profile initialization scripts (e.g., /etc/profile.d/sys-init.sh). The rule monitors for these files containing suspicious command strings such as curl downloads, xmrig mining configurations, or specific init-hook markers often used by cryptomining botnets.
Detects instances where a Redis server process, or its child process, writes a file named 'verify.html' into the web root directory '/var/www/html/'. This behavior is characteristic of adversaries abusing compromised Redis instances to deploy web shells or command-and-control beacons for persistent access.
Detects the presence of XMRig cryptocurrency miner ELF binaries by scanning for unique structural strings and configuration flags indicative of XMRig operations. This rule uses YARA to inspect binary file contents, enabling detection of miners regardless of their on-disk filename or obfuscation techniques such as hiding them within dotfiles.
This rule detects command-line activity where a remote resource is downloaded via 'curl', decoded using 'base64', and then piped directly into a shell interpreter (sh, bash, or zsh). This pattern is commonly used in fileless execution or stage-one payload delivery by attackers to execute malicious scripts directly from memory.
This rule detects the creation of cron jobs in /etc/cron.d/ that are associated with known miner file names (e.g., redis-miner, xminer). It explicitly looks for instances where the initiating process or its parent process is associated with Redis, suggesting a compromise or exploitation of a Redis service to establish persistence for a crypto miner.
This rule detects the creation of cron jobs in /etc/cron.d/ that are associated with known miner file names (e.g., redis-miner, xminer). It explicitly looks for instances where the initiating process or its parent process is associated with Redis, suggesting a compromise or exploitation of a Redis service to establish persistence for a crypto miner.
This rule detects the creation of cron jobs in /etc/cron.d/ that are associated with known miner file names (e.g., redis-miner, xminer). It explicitly looks for instances where the initiating process or its parent process is associated with Redis, suggesting a compromise or exploitation of a Redis service to establish persistence for a crypto miner.
Detects malicious activities associated with the SilkParasite campaign targeting Redis servers. The rules identify the initiation of a 'SLAVEOF' command to a rogue replication host, subsequent handshake (PSYNC/REPLCONF) with an external attacker-controlled node, and the modification of server configuration ('CONFIG SET dbfilename') to potentially facilitate unauthorized file persistence or data exfiltration through the Redis replication mechanism.
Detects malicious activities associated with the SilkParasite campaign targeting Redis servers. The rules identify the initiation of a 'SLAVEOF' command to a rogue replication host, subsequent handshake (PSYNC/REPLCONF) with an external attacker-controlled node, and the modification of server configuration ('CONFIG SET dbfilename') to potentially facilitate unauthorized file persistence or data exfiltration through the Redis replication mechanism.
BigBear 2.0 IOC HUNT (Cortex XDR)
Cortex XDR
CloudSEK researchers uncovered BigBear 2.0, a global Microsoft 365 phishing-as-a-service operation targeting hundreds of organizations across 40+ countries. The investigation exposed the attacker’s admin panel, affiliate network, phishing infrastructure, and thousands of stolen credentials and session cookies, revealing how modern AiTM attacks can hijack authenticated sessions even after MFA.
CloudSEK researchers uncovered BigBear 2.0, a global Microsoft 365 phishing-as-a-service operation targeting hundreds of organizations across 40+ countries. The investigation exposed the attacker’s admin panel, affiliate network, phishing infrastructure, and thousands of stolen credentials and session cookies, revealing how modern AiTM attacks can hijack authenticated sessions even after MFA.
Detects browser extension manifest.json files requesting declarativeNetRequest and content_scripts/host_permissions covering AI assistant vendor domains (BragJack attack class)
Detects browser extension manifest.json files requesting declarativeNetRequest and content_scripts/host_permissions covering AI assistant vendor domains (BragJack attack class)
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