Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
260 detections
Filters
Last updated
All Time
Detection languages
141
60
21
18
11
Contributors
80
44
20
19
11
Categories
77
69
67
49
37
Platforms
203
56
30
13
11
Products / Services
10,683
9,759
6,695
4,421
3,750
260
MITRE Techniques
75
73
53
41
40
CVEs
8
4
4
3
3
IDS Classtypes
4
1
1
1
IDS Protocols
4
1
Detects files referencing the cloned qassam-315/PAN-OS-User-ID-Buffer-Overflow-PoC GitHub repository for CVE-2026-0300, requiring co-occurrence of the full repo path, repo name, and CVE identifier
This rule monitors Palo Alto Networks firewalls for files identified as malicious by the WildFire or ML-based threat detection engines. It filters out known allowed file hashes and aggregates events to provide a high-confidence alert. The rule categorizes threats based on the verdict ('malicious', 'malware', 'phishing', 'suspicious', 'c2') and distinguishes whether the threat was blocked or delivered to the network, providing context for incident response.
Detects SSH traffic originating from designated edge appliances (e.g., firewalls, NAS devices) directed towards internal network destinations. This pattern is often indicative of network pivoting, lateral movement, or unauthorized remote access from exposed or compromised perimeter assets.
This rule computes a composite risk score for user authentication events across multiple identity and network providers (Okta, Azure AD, VPNs, etc.). It flags suspicious patterns including impossible travel, credential stuffing indicators (multiple failures followed by success), new or unknown devices, and lateral movement attempts (concurrent access to both VPN and SSO services). Only users meeting a risk score threshold of 40 or higher with at least one successful authentication are reported.
Detects evidence of exploitation attempts related to the hypothetical CVE-2026-55200 vulnerability. The rule monitors for known exploit file names, command-line arguments, and specific network patterns targeting SSH ports (22, 2222) that indicate potential LibSSH2 exploitation.
Detects anomalous outbound network traffic from Firefox processes indicative of CVE-2025-15661 exploitation. The rule monitors for communication with a specific non-standard port (8765) associated with a SmartWindow PoC server, or requests containing suspicious URI query parameters ('u0', 'u1', 'u2', 'source=history') frequently used for exfiltrating private browsing tabs and history via the '/leak' endpoint.
This rule detects network communication (DNS, connection, or traffic) with known infrastructure and specific URI paths associated with the SpectralViper threat actor. It enriches these findings by identifying if the communication originated from processes commonly associated with SpectralViper, such as OneDrive Sync services or specific Intel audio service files.
Detects Python processes spawned by development tools (VSCode, Cursor) or Node.js that are engaged in suspicious activities, specifically communicating with the Telegram Bot API or uploading large amounts of data, often accompanied by file staging (ZIP archive creation). This behavior is characteristic of the InvisibleFerret information stealer.
This rule identifies potential BRICKSTORM command and control (C2) activity by detecting sustained beaconing patterns to known public DNS-over-HTTPS (DoH) providers (8.8.8.8, 1.1.1.1) over port 443. It buckets network connections into 10-minute windows and flags source devices exhibiting frequent, repetitive traffic patterns, enriched with asset context to prioritize alerts from edge appliances such as firewalls, NAS devices, or storage sync services.
Detects the presence and execution of RustDesk remote access software by monitoring process names, command-line arguments, specific destination network ports, and DNS queries associated with the application. This rule identifies potential unauthorized remote access attempts or the use of non-approved remote support tools.
Detects outbound proxy traffic originating from identified AI and automation agent user-agents (e.g., ChatGPT, Perplexity, Claude, etc.) attempting to access sensitive URI paths, such as credential files, SSH keys, configuration files, or cloud-hosted secrets. This may indicate an attempt to exfiltrate sensitive data to or through AI platforms.
This rule monitors network logs (from proxies and firewalls) for web traffic (HTTP GET requests) containing suspicious JavaScript code injection patterns in the request context or message fields. It specifically looks for common XSS indicators such as <script tags, eval(), document.write(), fromCharCode(), and atob().
This rule detects outbound HTTP requests to Bing's 'searchbyimage' functionality that contain an image URL pointing to a domain outside of Microsoft or Bing. This behavior is associated with a potential data exfiltration technique (referenced as CVE-2026-42824) where internal information is encoded into an image URL and passed to an external, attacker-controlled, or third-party web service via the Bing search engine to bypass traditional network egress controls.
Detects suspicious processes commonly associated with malicious activity establishing outbound network connections to Azure Web Apps domains. The rule aggregates connections to identify potential beaconing behavior by monitoring connection frequency and duration, flagging activity initiated by binaries frequently used for living-off-the-land attacks such as powershell.exe, rundll32.exe, and others.
This rule detects high-volume or repeated outbound connections to Mega cloud storage domains (mega.nz, mega.co.nz, etc.) by processes that are not identified as standard web browsers. Such activity may indicate data exfiltration, automated file staging, or unauthorized use of cloud storage services.
Detects anomalous, high-volume API requests to ServiceNow endpoints originating from the same source IP that lack valid authorization headers or session tokens, potentially indicating automated scanning, enumeration, or exploitation attempts.
Identifies network connections to known ShinyHunters IP addresses or domains associated with the recent Oracle PeopleSoft exploitation campaign. This rule monitors both endpoint network events and perimeter firewall/proxy logs for outbound communication to the specified malicious infrastructure.
Detects network communication associated with BeepRAT malware, specifically targeting hardcoded TCP connections to destination port 18848 and ICMP packets containing the string 'BEEP', which are used for C2 communication and keepalive signals.
This rule identifies suspicious outbound network beaconing by analyzing flow statistics for consistency in interval and payload size. It flags processes that demonstrate periodic communication (heartbeats) to external endpoints on non-standard ports. The detection logic calculates a 'beacon_score' based on the consistency of the communication interval, small average payload sizes, low variance in payload sizes, and the number of connections. Additional weighting is applied to connections made by typically non-network processes (e.g., svchost, explorer, wscript) or those connecting to ports associated with known malware like SystemBC.
Detects bulk file downloads of executable or script content from web proxies using automated or headless client agents (e.g., curl, wget) where no browser-like Referer is present. The rule aggregates these downloads within a 10-minute window and correlates them with endpoint Sysmon file creation events in non-standard directories, indicating potential tool staging or malware delivery.
This rule detects network connections to phishing pages hosted on the workers.dev domain that are attempting to interact with EvilTokens device code API endpoints. These pages are known to implement developer hotkey prevention (F12 blocked, context menu disabled) to hinder in-browser analysis, indicating malicious intent.
Page 3 of 13