Cyber Risk Research & Applied Studies

1. Observe What changed? →

2. Understand Why does it matter? →

3. Track How does risk evolve? →

4. Predict What happens next? →

5. Decide What to do? →

6. Verify What happened? →

7. Remember Persistent memory

🔬 Core Research Engines

🔄 Historical Replay Reconstructs discrete threat observability at Day 0, Day 1, Day 3, Day 7, Day 14, Day 30, and Day 90 intervals.

🔬 Forensic Autopsies Exhaustive post-mortem investigations of landmark security failures dissecting root causes and missed signals.

🧬 Vulnerability Genome Structural taxonomic dissection across 8 orthogonal chromosomes to isolate true root causes.

⏳ Cyber Risk Time Machine Discrete temporal snapshot diff engine navigating historical threat states.

Explore in-depth technical investigations into advanced threat mechanics and agentic AI vulnerabilities:

Adversarial Agentic Workflows & Multi-Step Hijacking Empirical analysis of indirect prompt injection and tool execution hijacking in LangChain and MCP architectures.

GPU Memory & KV-Cache Forensics in LLM Systems Techniques for recovering residual prompt artifacts and conversation histories directly from accelerator VRAM.

The AF_ALG Race Condition (CVE-2025-39964) Forensic deconstruction of the Linux kernel cryptographic socket use-after-free and delayed weaponization trajectory.

ActiveMQ Artemis Pre-Auth Queue Destruction (CVE-2026-67593) Technical dissection of unauthenticated state wiping across Openwire protocol boundaries.

All research adheres to the published Hermes Epistemic Standard:

Zero Hallucination: Reconstructed data is explicitly labeled RETROSPECTIVE or REPLAY.
Peer-Verifiable Telemetry: Exploits and artifacts cross-referenced with public git commits and packet traces.
Falsifiable Claims: Theoretical assumptions separated from observable facts.