Detections
Explore public detection logic contributed by the community across SIEM and rule languages.
1,310 detections
Filters
Last updated
All Time
Detection languages
682
264
155
72
49
Contributors
237
195
187
166
110
Categories
345
308
229
217
143
Platforms
472
424
224
173
144
Products / Services
264
262
200
176
104
MITRE Techniques
18,116
15,636
12,583
8,393
6,223
1,310
CVEs
5
4
3
3
3
IDS Classtypes
103
67
24
23
12
IDS Protocols
167
51
16
14
7
Detects network connections from server or workload identities (excluding SYSTEM) to known public Generative-AI inference API domains. This may indicate the use of AI services for data exfiltration, automated content generation, or C2 communication via legitimate web services.
Detection of common web server reconnaissance, enumeration, and brute-force patterns targeting IIS environments. This includes detection of short-name (~1) probes, WebDAV method usage, NTFS alternate data stream requests, and brute-force attempts on login endpoints originating from PowerShell user agents.
Detects repeated attempts to enumerate available model endpoints on a LiteLLM server, a behavior often associated with reconnaissance targeting AI gateway services to identify exposed models.
Detects a sequence of events where a single user receives an unusually high volume of inbound emails (potentially vishing-related), followed shortly thereafter by the user executing common remote assistance or remote access tools (e.g., Quick Assist, AnyDesk). This pattern is consistent with social engineering tactics used in IT-support impersonation scams to facilitate the delivery of malware such as Sauron Loader.
Detects network activity related to the exploitation of the CVE-2025-31702 vulnerability in Dahua P2P Relay services. The rules monitor for probe attempts, device parameter fetching (randsalt), serial number enumeration bursts, and successful no-auth channel responses, indicating a bypass of authentication mechanisms on vulnerable Dahua devices.
Detects anomalous external traffic patterns indicative of automated API reconnaissance. The rule flags high-frequency requests from a single IP targeting a diverse set of API endpoints, particularly when characterized by a mixture of successful (200), unauthorized (403), or not found (404) status codes, or repeated attempts to access sensitive management/administrative API paths.
This rule monitors network device logs for patterns indicative of reconnaissance or vulnerability scanning against management and VPN interfaces of common firewall and network appliances (FortiGate, Meraki, WatchGuard, Sophos). It flags sources that either request multiple sensitive paths related to system status and configuration or employ known scanning tools (Nmap, masscan, Nuclei, zgrab, curl) in the user-agent string.
Detects inbound emails containing malicious QR codes (quishing) or HTML smuggling payloads designed to facilitate credential harvesting (AiTM). The rule identifies suspicious attachments, HTML-based obfuscation (e.g., atob, Blob) in email bodies, and QR-code-themed image attachments, filtering out internal communications.
Detects the execution of multiple distinct host reconnaissance commands (e.g., systeminfo, whoami, ipconfig, AV/EDR checks, and domain enumeration) within a short window (10 minutes) originating from the same parent process. This pattern is characteristic of automated reconnaissance collection, often performed during the initial phases of an attack or as part of a C2 workflow. The rule includes exclusions for common administrative, monitoring, and RMM tools to reduce false positives.
Detects accounts showing a spike in requests to public generative-AI services compared to their historical baseline, combined with anomalous indicators like multiple source IPs, regions, or off-hours activity.
Detects instances where a single user account exhibits an anomalously high frequency of requests to public generative-AI platforms within a one-hour window, potentially indicating automated reconnaissance or data staging activities using AI tools.
This rule detects potential quishing (QR code phishing) attempts by identifying emails with one or more image attachments and minimal URL content, followed by a risky user authentication event within a 3-hour window. This behavior is indicative of an attacker attempting to harvest credentials via a malicious QR code that redirects the user to a phishing site.
Detects anomalous, high-frequency, multi-channel probing of AI agent input surfaces. The rule identifies external actors sending instruction-injection style keywords across different communication channels (e.g., email, Teams, calendar) within a short window, which may indicate an attempt to identify and manipulate an organization's autonomous agents. Covers T1595.002
Detects high-volume account enumeration activity associated with the TeamFiltration tool, characterized by the use of legacy Microsoft Teams desktop client User-Agent strings (specific versions of Teams, Electron, and Chrome) within a short time window.
Detects high-volume account enumeration activity associated with the TeamFiltration tool, characterized by the use of legacy Microsoft Teams desktop client User-Agent strings (specific versions of Teams, Electron, and Chrome) within a short time window.
Detects suspicious mobile-device HTTP traffic patterns indicative of 'quishing' (QR code phishing). The rule identifies mobile HTTP GET requests (iPhone, Android, Mobile Safari, iPad) that lack a Referer header, followed by HTTP POST requests to common credential entry URI patterns (e.g., /login, /signin) over the same protocol, which is a behavioral indicator of a user being redirected from a scanned QR code to a malicious phishing landing page.
Detects automated API exploitation attempts, specifically Broken Object Level Authorization (BOLA) scanning. The rule triggers when a single source IP or API token exhibits high-frequency request patterns (5+ requests per second) against resource endpoints with sequentially incrementing IDs. It specifically correlates a spike in 401/403 authorization failures followed by a small count of 200 successes, indicating a potential successful bypass of authorization controls.
Detects a suspicious pattern of activity where a single source IP performs network reconnaissance followed by rapid-fire failed authentication attempts against multiple distinct hosts within a short time window. This behavior is indicative of automated, agentic AI-driven offensive tooling rather than manual activity.
Detects the Sauron Loader vishing chain: a mailbox hit by a high-volume, high-distinct-sender email bombing burst (raised from 20 to 50 emails/hour and now requiring 15+ distinct senders, to exclude single-sender retry loops or broken automation), followed within 2 hours by the same user launching Quick Assist or AnyDesk — consistent with an attacker posing as IT support after the flood. Also fixes a schema bug where EmailEvents was queried with TimeGenerated instead of Timestamp, and a post-join column reference bug.
Detects a suspicious sequence of events where a host performs multiple reconnaissance-style probes (health checks, documentation, or OpenAPI definitions) followed within 10 minutes by access to sensitive artifact-related URLs on the same remote host. This pattern is indicative of an attacker profiling a target server for metadata and then proceeding to exfiltrate files or sensitive artifacts.
Detects a suspicious sequence of events where a host performs multiple reconnaissance-style probes (health checks, documentation, or OpenAPI definitions) followed within 10 minutes by access to sensitive artifact-related URLs on the same remote host. This pattern is indicative of an attacker profiling a target server for metadata and then proceeding to exfiltrate files or sensitive artifacts.
Page 6 of 63