Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
2,686 detections
Filters
Last updated
All Time
Detection languages
1,342
389
264
219
197
Contributors
620
506
358
209
102
Categories
20,443
11,831
5,875
5,151
4,864
2,686
Platforms
1,168
638
452
382
359
Products / Services
593
445
403
264
229
MITRE Techniques
1,884
787
525
521
510
CVEs
11
5
5
5
3
IDS Classtypes
189
133
34
10
7
IDS Protocols
243
58
33
22
12
Detects a Node.js process accessing sensitive files such as environment configuration files (.env) or browser credential/session stores. This behavior was observed in a developer-targeting campaign where attackers exfiltrated secrets from compromised developer machines.
Detects data exfiltration patterns used by HoneyMyte scripts. The rule monitors for rar.exe compressing files with a specific password or curl.exe uploading data to the Pixeldrain file-sharing service.
Detects network connections to the 10xprofit backend infrastructure. Connections to these domains indicate the presence of a malicious browser extension and potential exfiltration of scraped e-commerce data or tracking activity.
This rule detects a suspicious sequence of events: a file named 'host.exe' being created in the Windows Startup folder, a 'runtime.zip' file being created in the local temporary directory, and subsequent network communication to the Telegram API (api.telegram.org) from the same device. This pattern is indicative of malware establishing persistence, staging data, and exfiltrating it via Telegram, potentially associated with malware like Small Sieve.
Detects periodic network traffic (beaconing) from browser processes to external domains, which may indicate automated data exfiltration by a malicious extension like the ChatGPT Stealer. This threat is known to exfiltrate data at approximately 30-minute intervals.
This rule detects the presence of hardcoded tokens associated with the dYdX supply chain attack. These tokens were used for C2 authentication and as an API key for data exfiltration. The rule searches across process command lines, network URLs, network headers, and common security logs for these specific tokens.
This rule detects outbound network connections to known Command and Control (C2) and exfiltration domains associated with the SANDWORM_MODE campaign. These domains are used to exfiltrate stolen credentials and other sensitive data.
This rule detects network connections from common web browsers to known command and control (C2) domains associated with the ChatGPT Stealer campaign. These domains are used to exfiltrate stolen AI session tokens and conversation data.
Detects execution of curl.exe uploading data to temp.sh using custom user-agent parameters, consistent with publicly reported malicious Notepad++ updater activity.
Detects programmatic GitHub data exfiltration by identifying non-interactive repository exports, clones, artifact downloads, or gist staging performed using tokens, OAuth applications, or GitHub Apps. The detection focuses on bursty or multi-repository activity within a short time window, which can indicate automated source code collection, misuse of programmatic credentials, or post-compromise data extraction.
This rule detects network connections to malicious infrastructure associated with a supply chain attack targeting dYdX npm and PyPI packages. The infrastructure is used for credential exfiltration and as a C2 for a RAT.
Detects a suspicious sequence where a new credential (PAT, SSH key, or OAuth authorization) is created and then used to establish durable persistence via webhooks or deploy keys. This behavior typically occurs after an account compromise as an attacker attempts to "lock in" access that survives credential rotation and provides real-time exfiltration of repository data.
Detects network requests to specific URI endpoints associated with the DOMOAuth2_ and IRAOAuth2.0 malicious NuGet packages. These packages exfiltrate ASP.NET Identity data to a local C2 proxy. The rule specifically looks for traffic to these URIs on localhost via port 7152.
Detects DNS queries to known OAST (Out-of-Band Application Security Testing) domains like oastify.com, oast.pro, and oast.fun where the subdomain is unusually long (over 50 characters). Long subdomains may indicate encoded data exfiltration, a technique observed in the exploitation of vulnerabilities like CVE-2026-1731 to bypass egress filtering.
This detection identifies network connections from non-browser processes to known Large Language Model (LLM) API endpoints. While some connections may be legitimate developer or application traffic, unexpected processes connecting to these services could indicate unauthorized data exfiltration, corporate espionage, or misuse of AI services.
This rule detects suspicious activity related to a malicious Notepad++ updater, specifically looking for command strings that indicate data exfiltration to 'temp.sh' using 'curl.exe'. It identifies the presence of 'temp.sh/upload', '--user-agent', 'temp.sh/', '/list', and 'curl.exe' within the analyzed content, suggesting an attempt to upload data or list contents to a temporary sharing service.
This rule detects HTTP network connections to the Telegram bot API (api.telegram.org/bot). This pattern is often associated with infostealers exfiltrating data to attacker-controlled Telegram bots. The rule specifically looks for the 'NETWORK_HTTP' event type and a URL containing '/api.telegram.org/bot' (case-insensitive).
Detects the creation or modification of repository webhooks in GitHub. Attackers can use webhooks for real-time data exfiltration of code pushes, pull request comments, and workflow data. Once a webhook is established, it operates independently of the creating user; revoking the creator's credentials will NOT stop the data flow. Only deleting the webhook itself terminates the exfiltration.
This rule detects suspicious enumeration of Azure Blob, File, Table, Queue, or DFS storage accounts by internal users. It identifies devices making network connections to public Azure storage URLs using potentially suspicious processes (e.g., PowerShell, Python, AzCopy, curl, wget, WinSCP, FileZilla, Storage Explorer). The rule then analyzes the distinct storage accounts accessed and uses a Jaccard index to identify unusual naming conventions among the accessed storage accounts, potentially indicating an insider attempting to discover or exfiltrate data from unfamiliar storage resources.
Detects suspicious network connections to 'outlook-one.vercel.app' or 'api.telegram.org' when initiated by common web browsers (msedge.exe, chrome.exe, firefox.exe) or Outlook (outlook.exe). This pattern can indicate phishing attempts, credential harvesting, or data exfiltration via Telegram bots.
Detects files being written to USB-mounted removable drives. The rule enriches these alerts with USB device identifiers such as VID_PID, InstancePathId, and SerialNumberId, gathered from fleet-wide Plug and Play (PnP) events. This enrichment allows security analysts to cross-reference the detected USB devices against an organization's approved device exclusion list, potentially managed by solutions like Microsoft Intune, to differentiate legitimate data transfers from potential data exfiltration attempts.
Page 103 of 128