Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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Detects instances where AI coding and agent tools (e.g., Claude Code, Cursor, Copilot CLI, Ollama, LangGraph) create archive or database files containing potentially sensitive data and subsequently initiate network connections to non-AI-provider or non-developer services (exfiltration services or non-standard port connections).
Detects common web application and application server processes (such as java, node, nginx, or apache) spawning suspicious shell or utility processes. This behavior is often indicative of exploitation of a web vulnerability (e.g., Remote Code Execution) where an adversary attempts to gain a reverse shell or execute post-exploitation commands.
Detects the GHAPPIER loader pattern: a top-level require('https').get() call to the primevector-app924560.vercel.app C2 that evals the response, disguised inside a large benign-looking JavaScript benchmark file
Detects the PolinRider/GHAPPIER campaign signature of a malicious loader payload silently appended to the end of a legitimate project configuration file
Detects the PolinRider/GHAPPIER campaign signature of a malicious loader payload silently appended to the end of a legitimate project configuration file
Detects the PolinRider/GHAPPIER campaign signature of a malicious loader payload silently appended to the end of a legitimate project configuration file
Detects the PolinRider/GHAPPIER campaign signature of a malicious loader payload silently appended to the end of a legitimate project configuration file
Detects the GHAPPIER loader payload concealed as a single line deep inside a large (~99KB) legitimate-looking JavaScript file, fetching and eval'ing remote code
Detects the GHAPPIER loader payload concealed as a single line deep inside a large (~99KB) legitimate-looking JavaScript file, fetching and eval'ing remote code
Detects the GHAPPIER loader payload concealed as a single line deep inside a large (~99KB) legitimate-looking JavaScript file, fetching and eval'ing remote code
Detects the GHAPPIER loader payload concealed as a single line deep inside a large (~99KB) legitimate-looking JavaScript file, fetching and eval'ing remote code
Detects the GHAPPIER loader identified across 65 public npm/GitHub repositories and 22 developer accounts, based on its embedded remote-fetch/eval pattern and campaign markers
Detects the GHAPPIER loader identified across 65 public npm/GitHub repositories and 22 developer accounts, based on its embedded remote-fetch/eval pattern and campaign markers
Detects the GHAPPIER loader identified across 65 public npm/GitHub repositories and 22 developer accounts, based on its embedded remote-fetch/eval pattern and campaign markers
This rule detects the installation or presence of ten identified malicious npm packages associated with the indexed-btree/sorted-btree typosquatting campaign. It monitors command-line activity for 'npm install' commands, package name resolution, and entries within 'package.json' files.
This rule monitors for suspicious Node.js process activity, specifically focusing on the loading of scripts associated with obfuscated or malicious packages (e.g., sharedLoad.min.js, indexed-btree) and the use of child_process spawn patterns that indicate detached, hidden, or unreferenced child processes, which are common tactics for bypassing detection or maintaining stealthy execution.
Detects instances where a Gradle or Java build process invokes a shell (sh -c) containing suspicious build-related keywords such as 'preBuild', 'doLast', or 'tasks.all'. This pattern is commonly observed in supply chain compromises where malicious code is injected into build scripts to execute hidden payloads during the compilation phase.
Detects file artifacts referencing OtterCandy malware, which combines OtterCookie and RATatouille RAT capabilities, associated with WaterPlum/Contagious Interview campaign
Detects file artifacts referencing OtterCandy malware, which combines OtterCookie and RATatouille RAT capabilities, associated with WaterPlum/Contagious Interview campaign
Detects OtterCookie JavaScript-based RAT/infostealer malware artifacts associated with WaterPlum/Contagious Interview campaign
Detects file artifacts referencing InvisibleFerret, a Python-based backdoor used by WaterPlum/North Korean IT worker actors for persistent access to victim networks
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