Mobile Application Reverse Engineering and Security QA Dataset

#security question answering #decompiled code analysis #evidence-based vulnerability reasoning #mobile application security #reverse engineering analysis #mobile vulnerability assessment

3960
0.4GB
JSON
CATL
MOBIUSI INC

Updated:2026-10-24

AI Analysis & Value Prop

This dataset is designed for mobile application reverse engineering and security analysis. Each sample pairs an analysis question with evidence such as textual decompiled code snippets, application manifest content, and runtime observations, followed by an answer, functionality inference, data flow analysis, security assessment, and explanations of evidence and uncertainty. It covers application behavior analysis, sensitive data flow tracing, and security review, with an emphasis on distinguishing observed facts from reasoned conclusions. It supports domain-specific supervised fine-tuning, long-form question answering, and analytical capability evaluation.

Dataset Insights

Sample Examples

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Technical Specifications

Field	Type	Description
file_name	string	File name
answer	string	A direct, coherent answer to the analysis question, distinguishing observed facts from reasoned inferences.
question	string	The question to answer about the application's functionality, data flows, or security-related behavior.
data_flow_analysis	string	A description of relevant data sources, transformations, destinations, and boundaries, including sensitive data flows when supported by the evidence.
decompiled_snippets	string	Textual excerpts from decompiled application code, with relevant class, method, or location context when available.
security_assessment	string	An assessment of security-relevant behavior, potential risks, and their conditions or impact, avoiding claims unsupported by the evidence.
application_manifest	string	The application manifest content, such as AndroidManifest.xml, including relevant permissions, components, and configuration.
runtime_observations	string	Textual observations collected while the application is running, such as logs, network behavior, or user-interface behavior.
evidence_and_reasoning	string	The supporting evidence and reasoning that connect specific source snippets, manifest entries, or runtime observations to the conclusions.
functionality_inference	string	An explanation of the relevant application feature or behavior inferred from the supplied evidence.
uncertainties_and_limits	string	Unresolved questions, alternative explanations, missing evidence, and limitations that affect confidence in the analysis.

Compliance Statement

Authorization Type	Proprietary - Commercial AI Training License (No Redistribution)
Commercial Use	Requires exclusive subscription or authorization contract (monthly or per-invocation charging)
Privacy and Anonymization	No PII, no real company names, simulated scenarios follow industry standards
Compliance System	Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs

Frequently Asked Questions

What does the dataset contain?: Samples are organized around mobile application analysis questions and include evidence such as textual decompiled code snippets, application manifests, and runtime observations. They also provide an analysis answer, functionality inference, data flow analysis, security assessment, and explanations of evidence and uncertainty. Refer to the dataset schema for the exact fields.

Which capabilities can the dataset support?: It can support training models to combine code, configuration, and runtime clues when answering mobile application security questions. Potential tasks include functionality inference, data flow tracing, risk analysis, evidence-based explanations, and uncertainty communication; it can also be used to evaluate related QA capabilities.

What is the dataset size and quality?: The supplied information does not specify a sample count or a standardized quality metric. The data is organized into structured fields for questions, evidence, and answers. Before use, check the delivered version for sample volume, field completeness, and suitability for your needs.

How can I access and use the dataset?: Check the relevant dataset product page or delivery channel for available formats, download instructions, and licensing terms. Before using it for training, evaluation, or commercial projects, confirm the applicable license and comply with relevant privacy, security, and software requirements.

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Cite this Work

@dataset{Mobiusi2026,
  title={Mobile Application Reverse Engineering and Security QA Dataset},
  author={MOBIUSI INC},
  year={2026},
  url={https://www.mobiusi.com/datasets/4d1dc13c4e5613c4af9c5d01f0811dc2},
  urldate={2026-10-21},
  keywords={mobile application security dataset,app reverse engineering,mobile security QA,decompiled code analysis,Android security analysis,data flow tracing,manifest analysis,security risk assessment},
  version={1.0}
}

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