Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
2,588 detections
Filters
Last updated
All Time
Detection languages
1,194
459
310
204
161
Contributors
487
455
323
171
123
Categories
1,884
937
415
400
360
Platforms
1,349
560
343
329
257
Products / Services
601
314
306
286
271
MITRE Techniques
18,116
15,636
12,583
8,393
6,221
2,588
CVEs
9
5
5
5
2
IDS Classtypes
142
96
49
11
6
IDS Protocols
177
49
44
17
4
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 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 HTTP PUT requests that follow a specific pattern associated with MacSync Stealer's exfiltration mechanism. The rule identifies multipart data transmission by looking for specific URI parameters ('upload_id', 'chunk_index', 'total_chunks') used to piece together stolen ZIP files over an established HTTP connection.
Detects a non-browser process initiating network connections to four distinct commercial LLM API providers (DeepSeek, OpenRouter, Mistral, and Google Gemini) within a 5-minute interval. This behavior is indicative of a C2 pattern where an adversary uses multiple LLM backends to perform plurality-vote or redundant queries, bypassing typical browser-based AI aggregation services.
Detects a non-browser process initiating network connections to four distinct commercial LLM API providers (DeepSeek, OpenRouter, Mistral, and Google Gemini) within a 5-minute interval. This behavior is indicative of a C2 pattern where an adversary uses multiple LLM backends to perform plurality-vote or redundant queries, bypassing typical browser-based AI aggregation services.
This rule detects network traffic indicative of the SCKIT MemTensor worm establishing a command-and-control (C2) beacon. The detection identifies TLS connections where the Server Name Indication (SNI) matches a specific pattern of a 12-character hexadecimal string followed by .skyleen.fr.
Detects the use of legitimate data transfer utilities (s5cmd, rclone) configured to exfiltrate sensitive document types (.doc, .pdf, .sql, etc.) to S3-compatible cloud storage. The rule correlates this activity with recent access to files containing sensitive credentials or configuration data, a pattern observed in double-extortion campaigns.
Detects outbound network traffic to Microsoft Graph API originating from a user-agent string identified as 'python-httpx'. This behavior is characteristic of automated scripts or malicious tools using Python libraries to interact with Microsoft cloud services, potentially for data exfiltration or unauthorized API interaction.
Detects execution of xcopy with specific flags intended to perform recursive copying of files, including hidden and system files, into a staging directory located within C:\Users\Public\. This behavior matches the known exfiltration collection routine of the NarwhalRAT malware, often invoked by processes such as python.exe or userscreen.exe.
Detects HTTP and TLS traffic patterns indicative of data exfiltration to Slack, specifically targeting hardcoded Slack API tokens, Slack channel IDs in HTTP request bodies, or traffic directed to hooks.slack.com, commonly associated with malicious npm packages.
Detects high-volume file access or download operations in SharePoint or OneDrive performed by common scripting libraries and client user agents, which may indicate automated data exfiltration.
Detects Node.js processes executing system-level reconnaissance (e.g., CPU, memory, hostname, uptime enumeration) followed by network connections to known exfiltration endpoints (Slack, Telegram API). This behavior aligns with identified npm malware campaigns using host fingerprinting for target selection and data exfiltration.
Detects a suspicious Node.js process pattern characterized by the execution of 'sharedLoad.min.js' or detached child processes, followed by rapid outbound network communication to known messaging platforms (Slack, Telegram). This behavior is indicative of the indexed-btree malware family, which exfiltrates host fingerprint data including architecture, hostname, and system metrics.
Detects HTTP POST requests containing potential credential exfiltration indicators, specifically targeting the '/google.php' endpoint with 'do=login&', 'user=', and 'pass=' parameters. This pattern is associated with FlowerStorm (Storm-1167) AiTM phishing kits.
Detects a Node.js server process (e.g. "next-server", "node.exe") spawning
cmd.exe with the "/d /s /c" flag combination, which is the pattern
produced by Node's child_process.execFileSync("cmd.exe", ["/d","/s","/c",
...]) — the exact command shape used by the CVE-2026-75604 exploit chain
to run attacker-supplied commands after a Server Action deserialization
bypass. Node applications almost never legitimately spawn cmd.exe.
cmd.exe with the "/d /s /c" flag combination, which is the pattern
produced by Node's child_process.execFileSync("cmd.exe", ["/d","/s","/c",
...]) — the exact command shape used by the CVE-2026-75604 exploit chain
to run attacker-supplied commands after a Server Action deserialization
bypass. Node applications almost never legitimately spawn cmd.exe.
Detects the creation or modification of a file named '.Outlook' within the AppData or AppData\Roaming folders, followed by network communication to known malicious domains or IP addresses by a process other than outlook.exe within a 30-minute window. This behavior is indicative of potential malware staging or command-and-control communication.
Detects large and sustained outbound TCP data transfers to external networks, which may indicate mass data exfiltration. The rule monitors established sessions and uses a threshold to identify unusual outbound traffic volume over an extended period.
Detects a suspicious pattern where a user browser visits a Google Document and subsequently initiates a connection to the Telegram Bot API within a short timeframe (10 minutes). This sequence is indicative of malicious Google Apps Scripts embedded in documents attempting to exfiltrate victim information (IP, geolocation, device details) as part of a reconnaissance phase before a ClickFix-style payload delivery.
Detects a process accessing browser credential files (Login Data) followed by an outbound network connection to a suspicious exfiltration endpoint commonly associated with credential-stealing malware.
Detects outbound network requests to Google Gemini AI API endpoints from processes that are not common web browsers or known development tools. This behavior is indicative of unauthorized use of LLM services by non-browser applications, such as the RatHat UI-automation module, for processing sensitive data or automation tasks.
Detects outbound network connections to known JWR phishing framework endpoints. The rule identifies suspicious interaction sequences, including initial beaconing via 'addClick' or 'getSyncSettings' followed by exfiltration of sensitive data (payment card info, OTPs) through POST requests to 'the_final_interface' or 'addCvv', or via a specific WebSocket channel pattern.
Page 18 of 124