Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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This rule detects unauthorized or suspicious access to sensitive configuration and credential files (e.g., .aws, .azure, .ssh) by specific DLP (Data Loss Prevention) or automation scripts, and identifies subsequent attempts to expose environment variables or sensitive tokens within process command lines.
This rule detects unauthorized or suspicious access to sensitive configuration and credential files (e.g., .aws, .azure, .ssh) by specific DLP (Data Loss Prevention) or automation scripts, and identifies subsequent attempts to expose environment variables or sensitive tokens within process command lines.
Matches known malicious dlp.sh/dlp-docker.sh Terraform module scripts used in Coder registry supply chain attack (common, aider, rstudio-server, windows-rdp, zed variants)
Detects cached malicious dlp.sh/dlp-docker.sh Terraform module artifacts served from the compromised registry.coder.com infrastructure
Detects the creation of tar archives within the Coder database during a specific window associated with rogue IP activity, where the file creation was performed by a system-level process.
Detects the presence of a specific sentinel string 'data.external.telemetry' within Terraform provisioner job logs, which indicates the execution of unauthorized data-collection logic within a compromised or backdoored Terraform module.
Detects unexpected child process execution (php, bash, sh) by web server processes (php-fpm, apache, nginx) while accessing specific Magento application directories (var/report, var/log/system.log) or parameters often associated with the StyleSmuggler exploitation technique, indicating potential Remote Code Execution.
Detects instances where common web server processes (e.g., PHP, Apache, Nginx) spawn child processes associated with command-line utilities (e.g., curl, bash, python). The rule specifically monitors for command-line arguments referencing log files or traces, which is indicative of potential exploitation attempts or post-exploitation activities such as log poisoning or remote code execution.
Detects the presence of PHP code execution patterns (eval, base64_decode, system, shell_exec) injected into Magento log or report files (var/report/, var/log/system.log), indicative of log poisoning used for web shell staging.
This rule detects the execution of specific DLP-related shell scripts (dlp.sh, dlp-docker.sh) being invoked by Terraform processes. This pattern may indicate suspicious automated infrastructure configuration or an attempt to bypass security controls by leveraging IaC tools to execute scripts.
This rule detects the execution of specific DLP-related shell scripts (dlp.sh, dlp-docker.sh) being invoked by Terraform processes. This pattern may indicate suspicious automated infrastructure configuration or an attempt to bypass security controls by leveraging IaC tools to execute scripts.
Detects text/report artifacts (markdown, JSON, plain text) produced by an autonomous AI-driven vulnerability research pipeline that decompiles binaries, traces cross-references, hypothesizes memory-safety flaws, and generates/debugs proof-of-concept exploits
Detects anomalous activity patterns consistent with automated firmware reverse engineering pipelines, specifically correlating the high-frequency execution of firmware analysis tools (e.g., binwalk, Ghidra) with the creation of vulnerability research knowledge base artifacts. This behavioral heuristic aims to identify potential AI-driven or automated zero-day discovery workflows by tracking tool usage density and output characteristics.
Detects the initialization of containerized LLM-based gateway services (e.g., litellm, ollama) followed closely in time by high-volume network traffic containing suspicious web injection payloads (e.g., SQLi, XSS, SSRF). The rule correlates process execution events for container launches with network activity originating from the same device to identify potential unauthorized or unsupervised automated exploitation.
Detects the initialization of containerized LLM-based gateway services (e.g., litellm, ollama) followed closely in time by high-volume network traffic containing suspicious web injection payloads (e.g., SQLi, XSS, SSRF). The rule correlates process execution events for container launches with network activity originating from the same device to identify potential unauthorized or unsupervised automated exploitation.
This rule detects potential command and control (C2) activity associated with the CurlRAT malware. It monitors for both the execution of 'curl' or 'curl.exe' processes with command lines containing known C2 domains, and network traffic originating from internal devices directed toward those same domains.
Detects the 'ted' backdoor or similar malicious activity attempting to manipulate HAProxy's internal connection counters by accessing or writing to the HAProxy process memory (/proc/[pid]/mem or /proc/self/mem). This behavior is characteristic of an attempt to evade backend logging and statistics tracking by directly modifying process memory structures.
This rule detects modifications or creation of files named 'crond' in standard system paths ('/usr/bin' or '/usr/sbin') that occur within a 4-second window of an 'ssh' binary file event. This pattern is indicative of potential persistence establishment where an adversary replaces or modifies a system daemon while simultaneously interacting with SSH services.
Detects network connections from common Linux system binaries (agetty, atd, polkitd, crond) to identified suspicious external domains. These binaries typically do not communicate with external internet services, suggesting potential process masquerading or compromise.
This rule identifies a discrepancy between actual network connections processed by HAProxy and the corresponding entries in its server-side logs. Such inconsistencies can indicate the presence of a malicious backdoor compiled into or injected into the HAProxy process, designed to facilitate C2 traffic while evading standard monitoring by suppressing or omitting logging for those connections.
Detects attempts to modify internal PostgreSQL system catalog tables (pg_authid or pg_proc) to grant superuser privileges or alter authorization records. This activity is commonly associated with exploiting logical decoding plugins to achieve persistence or privilege escalation in PostgreSQL environments.
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