# Artificial Intelligence Safety and Cybersecurity: a Timeline of AI Failures

arXiv: https://arxiv.org/abs/1610.07997 · HTML: https://mlsanity.com/paper/1610.07997/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Oct 2016.

## Built on by (most cited first, 9 of 9)

- [AI Risk Management Should Incorporate Both Safety and Security](https://mlsanity.com/paper/2405.19524/) (2024)
- [A taxonomic system for failure cause analysis of open source AI incidents](https://mlsanity.com/paper/2211.07280/) (2022)
- [Scope Compliance Uncertainty Estimate](https://mlsanity.com/paper/2312.10801/) (2023)
- [Hybrid Convolutional Neural Networks with Reliability Guarantee](https://mlsanity.com/paper/2405.05146/) (2024)
- [Decentralized Intelligence Network (DIN)](https://mlsanity.com/paper/2407.02461/) (2024)
- [Exploring Winograd Convolution for Cost-effective Neural Network Fault Tolerance](https://mlsanity.com/paper/2308.08230/) (2023)
- [Dimensional Characterization and Pathway Modeling for Catastrophic AI Risks](https://mlsanity.com/paper/2508.06411/) (2025)
- [Generating 3D Bio-Printable Patches Using Wound Segmentation and Reconstruction to Treat Diabetic Foot Ulcers](https://mlsanity.com/paper/2203.03814/) (2022)
- [The race to robustness: exploiting fragile models for urban camouflage and the imperative for machine learning security](https://mlsanity.com/paper/2306.14609/) (2023)

## Abstract (arXiv)

In this work, we present and analyze reported failures of artificially intelligent systems and extrapolate our analysis to future AIs. We suggest that both the frequency and the seriousness of future AI failures will steadily increase. AI Safety can be improved based on ideas developed by cybersecurity experts. For narrow AIs safety failures are at the same, moderate, level of criticality as in cybersecurity, however for general AI, failures have a fundamentally different impact. A single failure of a superintelligent system may cause a catastrophic event without a chance for recovery. The goal of cybersecurity is to reduce the number of successful attacks on the system; the goal of AI Safety is to make sure zero attacks succeed in bypassing the safety mechanisms. Unfortunately, such a level of performance is unachievable. Every security system will eventually fail; there is no such thing as a 100% secure system.