# Towards Formal Fault Injection for Safety Assessment of Automated Systems

arXiv: https://arxiv.org/abs/2311.09810 · HTML: https://mlsanity.com/paper/2311.09810/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 40 references that are in the map.
- First posted on arXiv 16 Nov 2023.

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

- [Scenic: A Language for Scenario Specification and Data Generation](https://mlsanity.com/paper/2010.06580/) (2020)
- [ML-based Fault Injection for Autonomous Vehicles: A Case for Bayesian Fault Injection](https://mlsanity.com/paper/1907.01051/) (2019)
- [Supervisor Synthesis to Thwart Cyber Attack with Bounded Sensor Reading Alterations](https://mlsanity.com/paper/1608.04103/) (2016)
- [Synthesis of Sensor Deception Attacks at the Supervisory Layer of Cyber-Physical Systems](https://mlsanity.com/paper/2008.01497/) (2020)

## Abstract (arXiv)

Reasoning about safety, security, and other dependability attributes of autonomous systems is a challenge that needs to be addressed before the adoption of such systems in day-to-day life. Formal methods is a class of methods that mathematically reason about a system's behavior. Thus, a correctness proof is sufficient to conclude the system's dependability. However, these methods are usually applied to abstract models of the system, which might not fully represent the actual system. Fault injection, on the other hand, is a testing method to evaluate the dependability of systems. However, the amount of testing required to evaluate the system is rather large and often a problem. This vision paper introduces formal fault injection, a fusion of these two techniques throughout the development lifecycle to enhance the dependability of autonomous systems. We advocate for a more cohesive approach by identifying five areas of mutual support between formal methods and fault injection. By forging stronger ties between the two fields, we pave the way for developing safe and dependable autonomous systems. This paper delves into the integration's potential and outlines future research avenues, addressing open challenges along the way.