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Future-Back Threat Modeling: A Foresight-Driven Security Framework

Vu Van Than

TL;DR

The paper addresses the mismatch between reactive threat modeling and the need to anticipate emergent future threats (e.g., AI-enabled fraud, information warfare, supply-chain disruptions). It introduces Future-Back Threat Modeling (FBTM), a foresight-driven framework that starts with envisioned future states and works backward to test and falsify current safety assumptions, converting insights into actionable governance artifacts. The methodology combines five epistemic principles, a four-phase reflexive workflow, and a three-perspective hypothesis framing, with deception-based validation and a Foresight-to-Evidence Matrix to quantify confidence. A case study applying FBTM to CVE-2025-64446 using a honeypot demonstrates end-to-end integration from foresight to governance, illustrating how such a framework can reveal unknown unknowns and support proactive resilience.

Abstract

Traditional threat modeling remains reactive-focused on known TTPs and past incident data, while threat prediction and forecasting frameworks are often disconnected from operational or architectural artifacts. This creates a fundamental weakness: the most serious cyber threats often do not arise from what is known, but from what is assumed, overlooked, or not yet conceived, and frequently originate from the future, such as artificial intelligence, information warfare, and supply chain attacks, where adversaries continuously develop new exploits that can bypass defenses built on current knowledge. To address this mental gap, this paper introduces the theory and methodology of Future-Back Threat Modeling (FBTM). This predictive approach begins with envisioned future threat states and works backward to identify assumptions, gaps, blind spots, and vulnerabilities in the current defense architecture, providing a clearer and more accurate view of impending threats so that we can anticipate their emergence and shape the future we want through actions taken now. The proposed methodology further aims to reveal known unknowns and unknown unknowns, including tactics, techniques, and procedures that are emerging, anticipated, and plausible. This enhances the predictability of adversary behavior, particularly under future uncertainty, helping security leaders make informed decisions today that shape more resilient security postures for the future.

Future-Back Threat Modeling: A Foresight-Driven Security Framework

TL;DR

The paper addresses the mismatch between reactive threat modeling and the need to anticipate emergent future threats (e.g., AI-enabled fraud, information warfare, supply-chain disruptions). It introduces Future-Back Threat Modeling (FBTM), a foresight-driven framework that starts with envisioned future states and works backward to test and falsify current safety assumptions, converting insights into actionable governance artifacts. The methodology combines five epistemic principles, a four-phase reflexive workflow, and a three-perspective hypothesis framing, with deception-based validation and a Foresight-to-Evidence Matrix to quantify confidence. A case study applying FBTM to CVE-2025-64446 using a honeypot demonstrates end-to-end integration from foresight to governance, illustrating how such a framework can reveal unknown unknowns and support proactive resilience.

Abstract

Traditional threat modeling remains reactive-focused on known TTPs and past incident data, while threat prediction and forecasting frameworks are often disconnected from operational or architectural artifacts. This creates a fundamental weakness: the most serious cyber threats often do not arise from what is known, but from what is assumed, overlooked, or not yet conceived, and frequently originate from the future, such as artificial intelligence, information warfare, and supply chain attacks, where adversaries continuously develop new exploits that can bypass defenses built on current knowledge. To address this mental gap, this paper introduces the theory and methodology of Future-Back Threat Modeling (FBTM). This predictive approach begins with envisioned future threat states and works backward to identify assumptions, gaps, blind spots, and vulnerabilities in the current defense architecture, providing a clearer and more accurate view of impending threats so that we can anticipate their emergence and shape the future we want through actions taken now. The proposed methodology further aims to reveal known unknowns and unknown unknowns, including tactics, techniques, and procedures that are emerging, anticipated, and plausible. This enhances the predictability of adversary behavior, particularly under future uncertainty, helping security leaders make informed decisions today that shape more resilient security postures for the future.

Paper Structure

This paper contains 28 sections, 3 figures, 3 tables.

Table of Contents

  1. Introduction -- The Limits of Past-Forward Security
  2. Problem Context
  3. Theoretical Gap
  4. Purpose and Contribution
  5. Related Work -- Where FBTM Extends Existing Thinking
  6. Foundational Principles of Future-Back Threat Modeling
  7. Core Principles of Future-Back Threat Modeling (FBTM)
  8. Method Overview – The Future-Back Reflexive Workflow
  9. Phase I – Foresight and Assumption Mapping
  10. Step 1 – Framing Intelligence Inputs
  11. Step 2 – Future-State Assumption Mapping
  12. Phase II – Epistemic Testing and Validation
  13. Purpose and Rationale
  14. Step 3 – Hypothesis Framing and Threat Perspective Modeling
  15. Step 4 – Validation and Experiment Design
  16. ...and 13 more sections

Figures (3)

  • Figure 1: Conceptual framework of Future-Back Threat Modeling (FBTM). The framework illustrates the cyclic relationship between foresight generation, epistemic testing, organizational learning, and strategic decision inputs. Each step (1--6) represents an iterative layer of hypothesis formulation, validation, and feedback integration.
  • Figure 2: Simplified honeypot research model used for the CVE-2025-64446 case study.
  • Figure 3: Web Honeypot Logs for 2025-11-19 showing probing of '/cgi-bin/fwbcgi'.