# DASH: Deception-Augmented Shared Mental Model for a Human-Machine Teaming System

arXiv: https://arxiv.org/abs/2512.18616 · HTML: https://mlsanity.com/paper/2512.18616/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 62 references that are in the map.
- First posted on arXiv 21 Dec 2025.

## Builds on (most cited first, 7 of 7)

- [Blind Backdoors in Deep Learning Models](https://mlsanity.com/paper/2005.03823/) (2020)
- [A Game-Theoretic Taxonomy and Survey of Defensive Deception for Cybersecurity and Privacy](https://mlsanity.com/paper/1712.05441/) (2017)
- [Toward Proactive, Adaptive Defense: A Survey on Moving Target Defense](https://mlsanity.com/paper/1909.08092/) (2019)
- [Social Engineering in Cybersecurity: A Domain Ontology and Knowledge Graph Application Examples](https://mlsanity.com/paper/2106.01157/) (2021)
- [AI Potentiality and Awareness: A Position Paper from the Perspective of Human-AI Teaming in Cybersecurity](https://mlsanity.com/paper/2310.12162/) (2023)
- [Toward Mutual Trust Modeling in Human-Robot Collaboration](https://mlsanity.com/paper/2011.01056/) (2020)
- [A Hierarchical Variable Autonomy Mixed-Initiative Framework for Human-Robot Teaming in Mobile Robotics](https://mlsanity.com/paper/2211.14095/) (2022)

## Abstract (arXiv)

We present DASH (Deception-Augmented Shared mental model for Human-machine teaming), a novel framework that enhances mission resilience by embedding proactive deception into Shared Mental Models (SMM). Designed for mission-critical applications such as surveillance and rescue, DASH introduces "bait tasks" to detect insider threats, e.g., compromised Unmanned Ground Vehicles (UGVs), AI agents, or human analysts, before they degrade team performance. Upon detection, tailored recovery mechanisms are activated, including UGV system reinstallation, AI model retraining, or human analyst replacement. In contrast to existing SMM approaches that neglect insider risks, DASH improves both coordination and security. Empirical evaluations across four schemes (DASH, SMM-only, no-SMM, and baseline) show that DASH sustains approximately 80% mission success under high attack rates, eight times higher than the baseline. This work contributes a practical human-AI teaming framework grounded in shared mental models, a deception-based strategy for insider threat detection, and empirical evidence of enhanced robustness under adversarial conditions. DASH establishes a foundation for secure, adaptive human-machine teaming in contested environments.