# Cyber Threat Intelligence for Artificial Intelligence Systems

arXiv: https://arxiv.org/abs/2603.05068 · HTML: https://mlsanity.com/paper/2603.05068/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 36 references that are in the map.
- First posted on arXiv 5 Mar 2026.

## Builds on (most cited first, 7 of 7)

- [Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection](https://mlsanity.com/paper/2302.12173/) (2023)
- [Detecting Backdoor Attacks on Deep Neural Networks by Activation Clustering](https://mlsanity.com/paper/1811.03728/) (2018)
- [From ChatGPT to ThreatGPT: Impact of Generative AI in Cybersecurity and Privacy](https://mlsanity.com/paper/2307.00691/) (2023)
- [Preventing Repeated Real World AI Failures by Cataloging Incidents: The AI Incident Database](https://mlsanity.com/paper/2011.08512/) (2020)
- [A Survey on Deep Hashing Methods](https://mlsanity.com/paper/2003.03369/) (2020)
- [Threat Assessment in Machine Learning based Systems](https://mlsanity.com/paper/2207.00091/) (2022)
- [A Survey on Explainable Artificial Intelligence for Cybersecurity](https://mlsanity.com/paper/2303.12942/) (2023)

## Abstract (arXiv)

As artificial intelligence (AI) becomes deeply embedded in critical services and everyday products, it is increasingly exposed to security threats which traditional cyber defenses were not designed to handle. In this paper, we investigate how cyber threat intelligence (CTI) may evolve to address attacks that target AI systems. We first analyze the assumptions and workflows of conventional threat intelligence with the needs of AI-focused defense, highlighting AI-specific assets and vulnerabilities. We then review and organize the current landscape of AI security knowledge. Based on this, we outline what an AI-oriented threat intelligence knowledge base should contain, describing concrete indicators of compromise (IoC) for different AI supply-chain phases and artifacts, and showing how such a knowledge base could support security tools. Finally, we discuss techniques for measuring similarity between collected indicators and newly observed AI artifacts. The review reveals gaps and quality issues in existing resources and identifies potential future research directions toward a practical threat intelligence framework tailored to AI.