Artificial Intelligence Safety and Cybersecurity: a Timeline of AI Failures
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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.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Oct 2016.
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Citing papers per year: 2022: 2; 2023: 3; 2024: 3; 2025: 1.
9 papers.
- 2024AI Risk Management Should Incorporate Both Safety and Securitycited by 17
- 2022A taxonomic system for failure cause analysis of open source AI incidentscited by 9
- 2023Scope Compliance Uncertainty Estimatecited by 3
- 2024Hybrid Convolutional Neural Networks with Reliability Guaranteecited by 2
- 2024Decentralized Intelligence Network (DIN)cited by 2
- 2023Exploring Winograd Convolution for Cost-effective Neural Network Fault Tolerancecited by 1
- 2022Generating 3D Bio-Printable Patches Using Wound Segmentation and Reconstruction to Treat Diabetic Foot Ulcerscited by 0
- 2023The race to robustness: exploiting fragile models for urban camouflage and the imperative for machine learning securitycited by 0
- 2025Dimensional Characterization and Pathway Modeling for Catastrophic AI Riskscited by 0
- 2025Dimensional Characterization and Pathway Modeling for Catastrophic AI Risks8 Aug 2025
- 2024Decentralized Intelligence Network (DIN)2 Jul 2024
- 2024AI Risk Management Should Incorporate Both Safety and Security29 May 2024
- 2024Hybrid Convolutional Neural Networks with Reliability Guarantee8 May 2024
- 2023Scope Compliance Uncertainty Estimate17 Dec 2023
- 2023Exploring Winograd Convolution for Cost-effective Neural Network Fault Tolerance16 Aug 2023
- 2023The race to robustness: exploiting fragile models for urban camouflage and the imperative for machine learning security26 Jun 2023
- 2022A taxonomic system for failure cause analysis of open source AI incidents14 Nov 2022
- 2022Generating 3D Bio-Printable Patches Using Wound Segmentation and Reconstruction to Treat Diabetic Foot Ulcers8 Mar 2022