Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
3,647 detections
Filters
Last updated
All Time
Detection languages
1,768
374
351
273
255
Contributors
908
676
593
359
164
Categories
1,502
1,169
782
528
521
Platforms
39,936
7,266
6,537
3,866
3,647
Products / Services
807
512
432
340
300
MITRE Techniques
1,180
582
528
506
438
CVEs
17
16
9
8
7
IDS Classtypes
41
15
10
6
5
IDS Protocols
47
24
3
2
1
This rule detects a single device making a high volume of connections to known machine learning model and dataset hosting repositories (e.g., Hugging Face, Kaggle, TensorFlow) within a short timeframe. Such activity may indicate bulk exfiltration of sensitive internal research data, proprietary models, or large datasets.
Detects instances where a Claude process accesses a 'SKILL.md' file, followed closely by a suspicious command execution on the same device. The rule specifically targets command-line activity that involves encoding, download-and-execute patterns, staging in sensitive directories, or communication with suspicious domains/IPs, which is indicative of a supply-chain or poisoned agent-skill file attack.
This rule monitors for network communication with a specific known malicious IP address and URL path, as well as the execution or presence of associated malicious file hashes. Additionally, it flags emails involving specific indicators identified as potentially associated with malicious activity.
This rule detects network communication with known malicious infrastructure (C2 IPs and URLs), presence of malicious files identified by SHA256 hashes on disk, and execution of known malicious files. It also correlates these activities with specific operator email addresses involved in the malicious campaign.
Detects a specific multi-stage exfiltration pattern characterized by initial reconnaissance/fingerprinting probes against sensitive service endpoints (/health, /docs, /openapi.json) followed by unauthorized access to artifact storage endpoints (/files or /file?name=) from the same device against the same host within a short timeframe.
Detects instances where common scripting or development language runtimes (Go, Node.js, WScript, CScript) access sensitive configuration or credential files, such as AWS credentials, Kubernetes configs, SSH keys, or environment files. This activity is often indicative of credential harvesting or unauthorized access to sensitive secrets by potentially malicious scripts or processes.
Detects execution and network activity related to the PamStealer / Wavel malware campaign, including the JXA dropper and associated C2 infrastructure. The rule monitors for known malicious file hashes (e.g., JXA dropper, pkgunpack utility) and network connections to identified lure, distribution, and C2 domains and URLs used by the campaign.
This analytic detects the macOS osascript utility being used with an interactive Bash invocation, identified by the presence
of "bash -i" in the command line.
Adversaries may abuse osascript and AppleScript's shell execution capabilities to launch
interactive shells, establish remote access, or execute post-exploitation commands.
of "bash -i" in the command line.
Adversaries may abuse osascript and AppleScript's shell execution capabilities to launch
interactive shells, establish remote access, or execute post-exploitation commands.
Detects instances where the 'curl' utility is used to download files while simultaneously piping the output directly into a shell interpreter (bash/sh) or using download flags ('-o', '-O') in a way that suggests staging or immediate execution of remote scripts, often associated with force-flag bypass patterns.
Detects suspicious command line patterns on macOS where base64 encoded strings are decoded and piped directly into a shell interpreter (bash/zsh/sh). This pattern is commonly used by adversaries to execute obfuscated payloads or scripts directly in memory.
Detects HTTP beaconing activity associated with the MacSync stealer malware on macOS platforms. The rule monitors for specific URI patterns ('curl', 'dynamic', 'gate') combined with macOS-specific User-Agent strings and potential credential exfiltration headers, indicating established communication with a Command and Control (C2) server.
This rule detects network connections from internal devices to a specific remote IP address (134.122.155.135) over port 443. This is characteristic of communication with a known or suspicious Command and Control (C2) server.
IOC hunt across DNS, network, and file-hash telemetry for the Ledger Google Ads phishing campaign. Vercel redirect domains are matched by exact hostname (DNS query name / parsed URL host) rather than substring, eliminating false positives from unrelated strings that merely contain a look-alike domain fragment. GCS bucket and Google Sites path IOCs remain substring-matched since the bucket IDs and page slugs are already highly specific.
This rule identifies potential infections associated with the MacSync malware family by matching process, file, and network indicators (hashes, malicious domains, and specific payload URLs) against endpoint telemetry. It monitors for execution of known malicious binaries, file creation events, and communication with identified command-and-control infrastructure.
Detects attempts to bypass Vite development server file access restrictions by appending query parameters such as ?raw, ?import&raw, or ?import&url&inline to request paths for sensitive files like .env, cloud credentials, and terraform state files.
Detects the execution of bash commands that contain base64 decoding flags, typically used to obfuscate command-line arguments to evade detection, initiated from a terminal application.
This rule detects outgoing HTTP POST requests to the '/api/credentials' endpoint, which is characteristic of the AMOS (Atomic macOS Stealer) malware family communicating with its command-and-control server to exfiltrate stolen sensitive information.
Detects suspicious execution patterns on macOS where the Terminal application launches a bash process executing base64-encoded commands. This behavior is indicative of the ClickFix campaign, where users are prompted to copy and paste malicious obfuscated scripts into the terminal, often under the guise of a reCAPTCHA verification.
Detects the modification of AI agent configuration files (e.g., mcp.json, config.json) followed shortly by the agent spawning a child process that matches typical Model Context Protocol (MCP) server execution patterns. This behavior may indicate an attacker has modified the configuration to inject malicious server commands or tools that the AI agent will execute upon next initialization, effectively achieving persistent code execution via the agent's legitimate functionality.
Detects instances where a Claude process accesses a 'SKILL.md' file, followed closely by a suspicious command execution on the same device. The rule specifically targets command-line activity that involves encoding, download-and-execute patterns, staging in sensitive directories, or communication with suspicious domains/IPs, which is indicative of a supply-chain or poisoned agent-skill file attack.
This rule correlates endpoint-based alerts related to credential theft or infostealer malware with subsequent successful cloud identity authentication events. It identifies situations where a user, who has recently triggered a credential theft alert on an endpoint, logs into a cloud environment from a country or IP address that has not been historically associated with that user within a 24-hour window.
Page 12 of 174