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SqlStealthRogue is a command-line utility the project presents for extracting data through a known SQL or NoSQL injection point. Its “zero-probe” approach means it is intended to skip discovery traffic: the operator supplies the relevant database and query details, and the project says each request is an extraction request. It is not presented as a scanner for finding injection vulnerabilities.
Use it only for authorized security testing. The repository warns that testing requires written permission from the target owner.
What “zero-probe” means in SqlStealthRogue
Many injection-testing workflows first send requests to determine whether a parameter is injectable and which techniques may work. SqlStealthRogue takes a different stated approach: it assumes the injection point and relevant database, table, or column context are already known. The project describes its premise as “every single request it sends is a data-extraction request.” That is the project’s characterization, not an independently verified traffic analysis.
This makes the tool a focused extraction utility rather than a general-purpose discovery scanner. If you do not already have a validated injection point and enough query context to configure the extraction, the project’s intended workflow is not a substitute for finding them.
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Which extraction methods and engines does the project list?
The README lists five technique categories. These are feature claims made by the project; their presence in the documentation does not establish that each technique works in every listed environment.
- Union-based: retrieves data through a query result that can be combined with the application’s result.
- Error-based: uses database error behavior to expose data.
- Boolean-blind: infers data from differences in true and false responses.
- Time-based: infers results from response delays.
- Prefix extraction: uses regular-expression conditions for NoSQL-style extraction, as described by the project.
The README calls its compatibility table a “12-Engine Real-Machine Verification Matrix.” It names the following targets. This is the project’s own matrix and qualification, not an external certification:
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- MySQL 8, PostgreSQL 14, MSSQL 2022, and SQLite
- Redis and MongoDB 7
- openGauss 5, OceanBase CE, and Oracle 23ai
- Elasticsearch 8, Milvus 2.4, and pgvector
The matrix also qualifies some cells: certain techniques are marked disabled based on what the project calls real-machine evidence, while Redis and Elasticsearch time templates are described as shipped but not lab-verified. The README says Oracle 23ai XMLType errors no longer echo data and notes that older versions may behave differently. Treat compatibility as technique- and version-specific rather than assuming every method works across every engine.
What setup and controls are documented?
The project describes SqlStealthRogue as a single-entry Python program using the standard library, with no dependencies. The README also documents configurable templates, tamper plugins, HTTP keep-alive, and parallelism controls. These features are intended to let an authorized tester adapt requests to a known target context; this overview does not provide target-specific extraction instructions.
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Because the approach omits discovery, configuration accuracy matters. The project warns that incorrect settings can simply produce no extracted rows unless the error-mark option is used. A lack of output therefore does not, by itself, establish that a target contains no data or that an injection point is absent.
What are the extraction limits?
- Blind extraction is byte-wise: the README cautions that blind modes can mangle multibyte characters.
- Bit-parallel extraction has an edge case: the project says an all-zero byte is treated as end-of-string, which can truncate or otherwise limit recovery of values containing that byte.
- Time-based extraction is serial: the project says it avoids stacking delays on the target, so this mode does not gain the same parallelism as other approaches.
These constraints affect data fidelity and completion time. They are reasons to validate recovered values against an authorized source of truth rather than treating partial or garbled output as conclusive.
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How should its speed claims be interpreted?
The README reports bit-parallel blind extraction as 5.35× faster than serial binary search while using the same request count. It also reports HTTP keep-alive as 5.6× faster. For large-chunk extraction, it gives an example of reducing requests from 22 to 6 for a 600-character value under its stated PostgreSQL/MSSQL conditions. These are project-reported figures, not independently reproduced benchmarks; performance on another target may differ with response latency, configuration, data, and server behavior.
How it differs from discovery-oriented tools
Sqlmap’s official usage documentation covers testing with union, error, boolean-blind, and time-based techniques, and documents separate options for non-SQL injection classes such as NoSQL. It also exposes detection level and risk settings, with a warning that higher-risk tests can have unwanted effects in some query contexts. SqlStealthRogue instead emphasizes starting with a known injection point and supplied database/query details. These are different stated workflows, not evidence that one tool universally replaces or outperforms the other.
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NoSQLMap is another adjacent project: its repository describes an auditing and attack-automation utility for NoSQL injection and default-configuration weaknesses, with documented focus on MongoDB and CouchDB. That description provides context, but does not independently validate SqlStealthRogue’s capabilities or performance.
Authorization and responsible use
The SqlStealthRogue README states: “For authorized security testing only. Using this tool against systems you do not have written permission to test is illegal. You are solely responsible for your actions.” This is the project’s warning, not jurisdiction-specific legal advice. Before testing, obtain written authorization from the system owner and follow the agreed scope and limits.
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