Cold Coffee Game Security

Analyze the abuse path from trust boundary to observable behavior, then implement detection and hardening.

Start

1. Identify engine, platform, architecture, network model, authoritative state, anti-cheat components, and available artifacts.
2. Classify the technique: external memory, injected/internal, input automation, overlay/ESP, runtime patch, packet/protocol, asset/config, kernel/driver, DMA/hardware, or account/economy abuse.
3. Map required access, modified state, data sources, persistence/lifecycle, and observable artifacts.
4. Reproduce only the minimum behavior needed to validate detection or a defensive hypothesis.

Select references

• Cheat categories, data flows, and observables: read references/cheat-architecture.md.
• Anti-cheat architecture and control placement: read references/anti-cheat-design.md.
• Unity/IL2CPP and Unreal analysis: read references/engine-security.md.
• Investigation and evidence: read references/incident-analysis.md.

Tools

• Use scripts/integrity_manifest.py to create or verify signed-off file hash manifests.
• Use scripts/telemetry_analyze.py to summarize player/input telemetry and flag explainable anomaly indicators.
• Combine with $eni-reverse-deep, $eni-memory-forensics, and $eni-case-lab for binaries, runtime state, and evidence handling.

Deliver

Return the technique classification, trust-boundary failure, required capabilities, observables, reproduction harness, integrity/telemetry checks, false-positive considerations, mitigations, and retest plan. Separate a detection hypothesis from a confirmed cheat artifact.