Methodology

This page describes how Malwlab approaches research, content creation, and editorial standards. Every published post follows these principles.

Research Approach

Malwlab content is grounded in one or more of:

• Firsthand lab experience — Reproducible experiments, documented configurations, actual lab observations from a real home lab environment
• Sourced external analysis — Clearly attributed reports, advisory-style writeups, and industry research with identifiable sources
• Synthesis — Combining multiple sources to provide practical context and analysis that goes beyond any single report

AI-Assisted Workflow

Malwlab uses AI assistance for drafting, research support, and editing. This is done transparently:

• AI does not invent lab results, testing data, or personal observations
• Technical claims are verified against source material
• All AI-assisted content is reviewed before publication
• The final voice and editorial decisions come from the author
• Content tagged with ai-llm-generated indicates substantial AI assistance in drafting

Sources and Attribution

• External sources are cited with links where possible
• Claims not based on firsthand experience are attributed to sources
• Uncertainty and assumptions are stated explicitly
• When information comes from multiple sources, each is named

Handling of IOCs and Technical Details

• IOCs are presented with context, not as standalone lists
• Exploit code is not published in full — only sufficient detail to understand the mechanism
• Configuration examples are safe and production-aware
• Logs and screenshots are sanitized of sensitive data

Corrections

If errors are identified in published content, they are corrected with clear notation. Corrections are tracked and visible to readers. Major corrections are also communicated via the RSS feed and newsletter.