Phoenix Studio
Convert indexed Sigma rules into analyst-ready detections.
This studio is built around Phoenix's own rule corpus, not a blank editor. Search by title or rule id, choose a local pySigma backend, then reveal pipelines only when you actually need them.
Indexed Rules
3,731
Ready to search
Backends
17
Loaded from pysigma-node
pySigma Versions
10
Newest: 1.3.3
Translation Workspace
Shape the rule before it leaves Phoenix
Tune Translation
Active Rule
Suspicious SQL Query
Target Profile
Splunk
Splunk through pysigma-backend-splunk.
Format Mode
Default
Default pySigma output mode for this backend.
Conversion Output
Suspicious SQL Query
Using Splunk · Default · pySigma 1.3.3
Translation controls
Adjust the rule on the left, then regenerate when you want a fresh backend-native query.
BackendSplunkFormatDefaultVersion1.3.3
title: Suspicious SQL Query
id: d84c0ded-edd7-4123-80ed-348bb3ccc4d5
status: test
description: Detects suspicious SQL query keywrods that are often used during recon, exfiltration or destructive activities. Such as dropping tables and selecting wildcard fields
author: '@juju4'
date: 2022-12-27
references:
- https://github.com/sqlmapproject/sqlmap
tags:
- attack.exfiltration
- attack.initial-access
- attack.privilege-escalation
- attack.persistence
- attack.t1190
- attack.t1505.001
logsource:
category: database
definition: 'Requirements: Must be able to log the SQL queries'
detection:
keywords:
- 'drop'
- 'truncate'
- 'dump'
- 'select \*'
condition: keywords
falsepositives:
- Inventory and monitoring activity
- Vulnerability scanners
- Legitimate applications
level: medium
CLI command
Copy the exact command to reproduce this translation locally.
sigma convert --without-pipeline -t splunk -f default rules/category/database/db_anomalous_query.yml