Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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This rule monitors for modifications to the PostgreSQL configuration file 'pg_hba.conf', specifically looking for changes that would enable 'trust' authentication, followed by successful database logins using that method within one hour. This behavior is indicative of an attacker modifying authentication rules to gain unauthorized, passwordless access to a database.
Detects instances where common web server applications (e.g., Apache, Tomcat, IIS, Nginx) or Java runtime environments spawn suspicious child processes like command shells (cmd.exe, powershell.exe, sh, bash) or network utilities (wget, curl). This behavior often indicates exploitation of a web vulnerability, such as Remote Code Execution (RCE), allowing an adversary to execute commands or download further malicious payloads.
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 network requests to unpkg.com or npmmirror for 'index.html' files directly under a package path. This behavior deviates from standard package management usage (which typically fetches tarballs or specific JavaScript modules) and is often associated with adversaries using npm packages as hosting infrastructure for phishing landing pages.
Detects Server-Side Request Forgery (SSRF) attempts targeting vulnerable versions of Grafana MCP Server (versions < 1.1.0). The rule identifies requests attempting to access sensitive internal or cloud-metadata endpoints (e.g., 169.254.169.254, localhost, internal subnets) via the X-Grafana-URL header.
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 DragonForce ransomware note (readme.txt) referencing known DragonForce Tor negotiation/blog onion addresses
This rule monitors for file and process events involving 'pubspec.yaml' configuration files or specific Flutter-related components such as 'universal_file_viewer' and 'surveyjs_flutter'. This detection logic is designed to track development activities or the inclusion of specific software packages within a Flutter project environment.
Detects network connection attempts or established connections to a specific remote IP (103.141.13.26) on UDP port 3479. This pattern is often associated with command and control infrastructure or unauthorized data communication.
Detects potential remote code execution (RCE) attempts targeting vulnerable Sub-Store instances (versions < 2.38.2). The detection identifies web browsers making network connections to local ports (38324) associated with Sub-Store, followed by the spawning of shell processes (cmd, powershell, bash, sh) by the Node.js process hosting the application.
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
This rule detects potentially unauthorized access or attempts to copy sensitive SSH keys (e.g., id_rsa, id_ed25519) by command-line utilities (such as cat, cp, tar, scp, or curl) or when triggered by automation-related process names like 'terraform'. It monitors both process command lines and file events to identify staging or exfiltration activities.
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 potential Server-Side Request Forgery (SSRF) activity where web application or runtime processes attempt to access the Cloud Instance Metadata Service (IMDS) or container task metadata endpoints. By monitoring network connections and application logs, the rule filters out known legitimate metadata clients and identifies suspicious processes frequently associated with web-based vulnerabilities that are repeatedly querying sensitive metadata paths.
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.
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