Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
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This rule detects unauthorized access, creation, or modification of Git credential files (.git-credentials, .netrc) by processes other than standard Git credential management utilities. It also monitors command-line activity that references these credential stores to identify potential attempts to exfiltrate or manipulate stored credentials.
Detects architecture-specific Mach-O 64-bit AMOS Stealer binaries dropped by the Macfinger ClickFix first-stage shell script, requiring multiple distinctive C2 endpoint strings alongside Mach-O magic and size constraints to reduce false positives
This rule detects artifacts and behaviors associated with the WaterPlum/Contagious Interview (also known as DeceptiveDevelopment) malware suite, including BeaverTail, InvisibleFerret, and related payloads. The rule specifically targets the presence of embedded family-name strings, as well as the execution of suspicious npm package installations triggered by VS Code task automation (tasks.json) or trust prompts, common in fake technical interview attack scenarios.
This rule monitors for the installation of specific known-malicious or suspicious NPM packages that are commonly used in supply chain attacks. It identifies the execution of npm (or node) commands to install or add packages explicitly listed as malicious by security research, which may indicate a supply chain compromise or an attempt to introduce unauthorized code into the development environment.
Detects the execution of cloud CLI tools (aws, az, gcloud) using Unix shells (bash, sh) to perform sensitive operations such as listing secrets, keys, or metadata, which may indicate credential discovery or exfiltration.
Detects common runtime processes (Node.js, npx, Python) often used for Model Context Protocol (MCP) servers accessing sensitive local credential files such as AWS credentials, SSH private keys, or environment files. This behavior is indicative of potential credential harvesting by unauthorized MCP servers in developer environments.
Detects the presence of specific Go build path metadata associated with the sckit Go implant, including modules and internal package structures, within an executable or file.
Detects the installation of known malicious npm or PyPI packages followed within 30 minutes by the execution of a package manager (node or python) performing network activity. This pattern matches the behavior of downloader stages in the 'Graphalgo' campaign, which uses coding-task lures to trick users into installing malicious packages.
Detects potential exfiltration of sensitive DevOps credentials or configuration files (such as AWS credentials, SSH keys, Kubernetes configs, or cryptocurrency wallets) by monitoring for file access events followed by outbound network connections to known exfiltration channels like Slack or specific C2 infrastructure within a 30-minute window.
Detects instances where a non-browser process initiates connections to commercial LLM provider APIs (DeepSeek, OpenRouter, Mistral) followed by a connection to Discord CDN/Webhook endpoints within a 15-minute window. This behavior is indicative of a process acting as an autonomous C2 agent that exfiltrates data after receiving instructions or processing information via an LLM.
Detects the execution of the 'sckit' Go implant, a malicious binary associated with trojanized '@memtensor/memos-cloud-openclaw-plugin' or 'MemoryOS' packages. The rule identifies instances where this binary is spawned as a child process of a language runtime (Node.js or Python) when the execution originates from specific directory paths associated with these packages, or when the parent process command line indicates these packages are being initialized.
Detects the Sckit Go implant accessing sensitive local configuration and credential files, including SSH keys, cloud CLI tokens, and package manager credentials. The rule monitors for file read events initiated by processes identified as 'sckit' or processes with specific command line arguments.
Detects the installation of AI-related packages (e.g., mcp, tiktoken_mcp) via common package managers (pip, npm, docker) followed by an outbound network connection from the same host within 10 minutes. This behavior is consistent with supply chain attacks where trojanized AI/MCP components are used to initiate command-and-control communication shortly after deployment.
This rule detects potentially malicious network activity by monitoring for specific query parameters ('bmodule', 'smodule', 'lmodule', 'task', 'upload.php') in web requests or communication with a specific known-malicious IP address ('62.60.226.50'). This pattern is characteristic of C2 (Command and Control) traffic or unauthorized file staging/upload operations.
This rule detects potentially malicious command execution initiated by web browsers (Chrome, Edge, Firefox, Safari, Opera). It identifies scenarios where browsers spawn PowerShell on Windows with encoded command arguments or spawn shells (zsh, bash, sh) on macOS while piping commands retrieved via curl or wget, which is a common pattern for dropper and fileless malware delivery.
This rule detects specific JavaScript helper functions and state machine constants commonly used in 'ClickFix' phishing campaigns (e.g., Exvicy and ErrTraffic). These scripts typically prompt users to copy and paste malicious code snippets, often disguised as error fixes or CAPTCHA resolution, to facilitate initial access.
Matches archived SHA-256 hashes of known malicious CRX builds from the 'VPN for X' proxy farm extension family
Detects the presence of a specific, hardcoded Ed25519 public key associated with malicious cross-platform payloads (npm, Go modules, Terraform providers) that use Slack for C2 communication.
Detects the execution of the Graphalgo RAT second-stage payload. The rule identifies the spawning of a detached 'go run .' process, which performs an ephemeral key exchange using a hardcoded public key before C2 establishment. It also correlates this activity with parent processes common in software build environments (npm, terraform, go) to differentiate malicious staging from developer activity.
Detects an account takeover chain where a device loads a malicious JavaScript component ('load-addon.js') in a browser session, followed by repeated attempts to bypass bot detection mechanisms (targeting Google's 'errors/robot.png') as the payload attempts to force authentication challenges or password resets.
Detects network communication to specific Gusercontent endpoints consistent with malicious browser extensions exfiltrating captured session tokens or cookies. The rule monitors for POST requests containing parameters like leadId, email, and data, often used to transmit stolen authentication material.
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