CafeSec Lab is an independent defensive security research project for internet cafes, gaming venues, esports hotels, and managed shared-PC environments. The project is global in scope and uses region-neutral public examples so operators can adapt the guidance to local law, vendor contracts, and business conditions.
The project exists to move venue billing and management systems from informal trust models toward security models that are observable, testable, and hardenable by small operators with limited budgets.
- Project home: https://cafeseclab.com/
- Research blog: https://research.cafeseclab.com/
- Current public release:
v0.2.0 - Security policy:
.github/SECURITY.md - Project goal:
docs/roadmap/project-goal.md - v0.2 roadmap:
docs/roadmap/v0.2.md - CVP evidence pack:
docs/cvp/evidence-pack.md - Synthetic pilot package:
docs/pilot/synthetic-shared-pc-venue/ - Commercialization plan:
docs/business/commercialization-plan.md - Operator executive summary template:
docs/operators/executive-summary-template.md - Project health playbook:
docs/maintenance/project-health.md
CafeSec Lab develops practical security guidance, detection logic, and defensive tooling for venue operators and software vendors who manage fleets of public or semi-public Windows hosts. The work is grounded in real operations: billing servers, cashier workstations, client images, restoration systems, local networks, and the daily constraints of small gaming businesses.
The long-term research goals are:
- publish a defensible hardening baseline for internet cafe and gaming venue environments;
- produce detection rules that SOC and MSSP teams can adapt safely;
- build a file and process integrity monitor suitable for low-budget deployments;
- document vendor-neutral architectural weaknesses in legacy cafe management software;
- encourage coordinated remediation rather than public exploitation.
| Path | Purpose |
|---|---|
01-hardening-checklist/ |
Vendor-neutral hardening checklist, threat model, detection rules, and anonymized case studies. |
02-integrity-monitor/ |
Python integrity monitoring tool for file, process, and event-log based detection. |
03-blog/ |
Public research notes and long-form technical articles. |
04-validation-lab/ |
Reproducible, fully network-isolated Hyper-V/domain testbed that validates the detection rules and integrity monitor on real Sysmon/WEF/Wazuh telemetry. |
.github/ |
Security policy, issue templates, CI, and organization profile content. |
The four modules form a closed loop: 01-hardening-checklist defines the baselines and detection
rules, 04-validation-lab reproduces an isolated venue-like environment and exercises those rules
plus 02-integrity-monitor on real telemetry, and the resulting (synthetic, human-reviewed) lab
evidence feeds docs/pilot/ and docs/cvp/ and informs 03-blog. See
04-validation-lab/COVERAGE.md for the rule/checklist → lab mapping.
Lab output is reproducible synthetic evidence, not field-validated production results.
CafeSec Lab focuses on defensive questions:
- How should billing servers, cashier workstations, and client PCs be isolated?
- Which Windows controls matter most in a shared-PC venue?
- How can operators verify that billing software binaries, services, and configuration files have not changed unexpectedly?
- What telemetry should a low-cost SOC collect first?
- How can vendors modernize trust boundaries, update mechanisms, and client-server integrity checks?
The project does not provide legal advice. Operators in any jurisdiction should validate compliance obligations with qualified local counsel and their technology providers.
The CafeSec Lab maintainer has completed OpenAI trusted access identity verification for authorized security work. This provides an external trust signal for legitimate defensive research workflows while the project continues to welcome additional verification dialogue with AI providers, vulnerability coordination platforms, and security communities.
CafeSec Lab does not publish weaponized proof-of-concept code, bypass tools, exploit chains, credential material, or step-by-step abuse instructions for venue billing systems. When an attack pattern is discussed, the corresponding detection, mitigation, and validation guidance must appear with it.
The project also does not:
- target named vendors irresponsibly;
- accept cooperation from criminal or gray-market operators;
- fabricate data, commit history, deployment claims, or research outcomes;
- present AI-generated content as field-validated evidence without review;
- encourage testing against third-party systems without written authorization.
Potential vulnerabilities must be reported through the private channels in .github/SECURITY.md. Public GitHub issues are not an acceptable disclosure channel for vulnerabilities.
The disclosure process is aligned with the CERT/CC Guide to Coordinated Vulnerability Disclosure and GitHub's guidance on repository security policies. Project maturity work will track the OpenSSF Best Practices Badge criteria over time.
Research contact: research@cafeseclab.com
General inquiries: contact@cafeseclab.com
Security and vulnerability disclosure: security@cafeseclab.com
Public research identity: CafeSec Lab Research Team
CafeSec Lab is maintained by a single accountable individual. The following personal accounts are published as a verifiable identity link between the human maintainer, this repository, and the AI-provider accounts used for authorized defensive research (including any cyber verification review):
- Primary / GitHub and AI-provider account:
magnoormeno@gmail.com - Secure correspondence account:
mandyminss@protonmail.com - GitHub identity:
magnoormeno-dot
Role-based domain addresses (security@, research@, contact@cafeseclab.com)
remain the preferred channels for operational and disclosure contact. The
personal accounts above exist to bind the same real person to the project across
GitHub, the AI provider, and out-of-band secure email.
Sensitive reports should follow the secure-channel instructions in .github/SECURITY.md.
Code and documentation are released under the MIT License, unless a file states otherwise.