# Security-Aware Sensor Fusion with MATE: the Multi-Agent Trust Estimator

arXiv: https://arxiv.org/abs/2503.04954 · HTML: https://mlsanity.com/paper/2503.04954/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Mar 2025.

## Built on by (most cited first, 4 of 4)

- [All Vehicles Can Lie: Efficient Adversarial Defense in Fully Untrusted-Vehicle Collaborative Perception via Pseudo-Random Bayesian Inference](https://mlsanity.com/paper/2603.08498/) (2026)
- [Adversarial Trust Poisoning in Vehicular Collaborative Perception](https://mlsanity.com/paper/2605.22122/) (2026)
- [Scaling Datasets for Multi-Sensor, Multi-Agent, and Multi-Domain Learning in Autonomous Systems](https://mlsanity.com/paper/2606.04444/) (2026)
- [Who to Trust in Multi-Robot Systems: Joint Robot and Object Hypothesis Trust Inference via Neural-Symbolic Cross-Validation](https://mlsanity.com/paper/2610.14530/) (2026)

## Abstract (arXiv)

Lacking security awareness, sensor fusion in systems with multi-agent networks such as smart cities is vulnerable to attacks. To guard against recent threats, we design security-aware sensor fusion that is based on the estimates of distributions over trust. Trust estimation can be cast as a hidden Markov model, and we solve it by mapping sensor data to trust pseudomeasurements (PSMs) that recursively update trust posteriors in a Bayesian context. Trust then feeds sensor fusion to facilitate trust-weighted updates to situational awareness. Essential to security-awareness are a novel field of view estimator, logic to map sensor data into PSMs, and the derivation of efficient Bayesian updates. We evaluate security-aware fusion under attacks on agents using case studies and Monte Carlo simulation in the physics-based Unreal Engine simulator, CARLA. A mix of novel and classical security-relevant metrics show that our security-aware fusion enables building trustworthy situational awareness even in hostile conditions.