# Adversarial Machine Learning Threats to Spacecraft

arXiv: https://arxiv.org/abs/2405.08834 · HTML: https://mlsanity.com/paper/2405.08834/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 22 references that are in the map.
- First posted on arXiv 14 May 2024.

## Builds on (most cited first, 4 of 4)

- [Physical Adversarial Attacks on an Aerial Imagery Object Detector](https://mlsanity.com/paper/2108.11765/) (2021)
- [Federated Learning in Satellite Constellations](https://mlsanity.com/paper/2206.00307/) (2022)
- [Robust Adversarial Attacks Detection based on Explainable Deep Reinforcement Learning For UAV Guidance and Planning](https://mlsanity.com/paper/2206.02670/) (2022)
- [Adversarial Attacks against a Satellite-borne Multispectral Cloud Detector](https://mlsanity.com/paper/2112.01723/) (2021)

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

- [Dynamic Parameter Optimization for Highly Transferable Transformation-Based Attacks](https://mlsanity.com/paper/2511.11993/) (2025)

## Abstract (arXiv)

Spacecraft are among the earliest autonomous systems. Their ability to function without a human in the loop have afforded some of humanity's grandest achievements. As reliance on autonomy grows, space vehicles will become increasingly vulnerable to attacks designed to disrupt autonomous processes-especially probabilistic ones based on machine learning. This paper aims to elucidate and demonstrate the threats that adversarial machine learning (AML) capabilities pose to spacecraft. First, an AML threat taxonomy for spacecraft is introduced. Next, we demonstrate the execution of AML attacks against spacecraft through experimental simulations using NASA's Core Flight System (cFS) and NASA's On-board Artificial Intelligence Research (OnAIR) Platform. Our findings highlight the imperative for incorporating AML-focused security measures in spacecraft that engage autonomy.