# Planning for Attacker Entrapment in Adversarial Settings

arXiv: https://arxiv.org/abs/2303.00822 · HTML: https://mlsanity.com/paper/2303.00822/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 37 references that are in the map.
- First posted on arXiv 1 Mar 2023.

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

- [Reward-rational (implicit) choice: A unifying formalism for reward learning](https://mlsanity.com/paper/2002.04833/) (2020)
- [Explicability? Legibility? Predictability? Transparency? Privacy? Security? The Emerging Landscape of Interpretable Agent Behavior](https://mlsanity.com/paper/1811.09722/) (2018)
- [A Unified Framework for Planning in Adversarial and Cooperative Environments](https://mlsanity.com/paper/1802.06137/) (2018)
- [Deceptive Reinforcement Learning for Privacy-Preserving Planning](https://mlsanity.com/paper/2102.03022/) (2021)
- [Deception in Optimal Control](https://mlsanity.com/paper/1805.03090/) (2018)
- [Signaling Friends and Head-Faking Enemies Simultaneously: Balancing Goal Obfuscation and Goal Legibility](https://mlsanity.com/paper/1905.10672/) (2019)
- [A Unifying Bayesian Formulation of Measures of Interpretability in Human-AI](https://mlsanity.com/paper/2104.10743/) (2021)

## Built on by (most cited first, 1 of 1)

- [Value of Information-based Deceptive Path Planning Under Adversarial Interventions](https://mlsanity.com/paper/2503.24284/) (2025)

## Abstract (arXiv)

In this paper, we propose a planning framework to generate a defense strategy against an attacker who is working in an environment where a defender can operate without the attacker's knowledge. The objective of the defender is to covertly guide the attacker to a trap state from which the attacker cannot achieve their goal. Further, the defender is constrained to achieve its goal within K number of steps, where K is calculated as a pessimistic lower bound within which the attacker is unlikely to suspect a threat in the environment. Such a defense strategy is highly useful in real world systems like honeypots or honeynets, where an unsuspecting attacker interacts with a simulated production system while assuming it is the actual production system. Typically, the interaction between an attacker and a defender is captured using game theoretic frameworks. Our problem formulation allows us to capture it as a much simpler infinite horizon discounted MDP, in which the optimal policy for the MDP gives the defender's strategy against the actions of the attacker. Through empirical evaluation, we show the merits of our problem formulation.