# AI Security Map: Holistic Organization of AI Security Technologies and Impacts on Stakeholders

arXiv: https://arxiv.org/abs/2508.08583 · HTML: https://mlsanity.com/paper/2508.08583/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 28 of its 60 references that are in the map.
- First posted on arXiv 12 Aug 2025.

## Builds on (most cited first, 28 of 28)

- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Membership Inference Attacks against Machine Learning Models](https://mlsanity.com/paper/1610.05820/) (2016)
- [Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection](https://mlsanity.com/paper/2310.11511/) (2023)
- [Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection](https://mlsanity.com/paper/2302.12173/) (2023)
- [CNN-generated images are surprisingly easy to spot... for now](https://mlsanity.com/paper/1912.11035/) (2019)
- [Stealing Machine Learning Models via Prediction APIs](https://mlsanity.com/paper/1609.02943/) (2016)
- [Federated Learning with Matched Averaging](https://mlsanity.com/paper/2002.06440/) (2020)
- [Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks](https://mlsanity.com/paper/2103.14749/) (2021)
- [Poisoning Web-Scale Training Datasets is Practical](https://mlsanity.com/paper/2302.10149/) (2023)
- [Machine Unlearning of Features and Labels](https://mlsanity.com/paper/2108.11577/) (2021)
- [Detecting AI Trojans Using Meta Neural Analysis](https://mlsanity.com/paper/1910.03137/) (2019)
- [Gazelle: A Low Latency Framework for Secure Neural Network Inference](https://mlsanity.com/paper/1801.05507/) (2018)
- [No Classification without Representation: Assessing Geodiversity Issues in Open Data Sets for the Developing World](https://mlsanity.com/paper/1711.08536/) (2017)
- [A Survey of Privacy Attacks in Machine Learning](https://mlsanity.com/paper/2007.07646/) (2020)
- [PLeak: Prompt Leaking Attacks against Large Language Model Applications](https://mlsanity.com/paper/2405.06823/) (2024)
- [A Comprehensive Taxonomy for Explainable Artificial Intelligence: A Systematic Survey of Surveys on Methods and Concepts](https://mlsanity.com/paper/2105.07190/) (2021)
- [Detecting Adversarial Examples Is (Nearly) As Hard As Classifying Them](https://mlsanity.com/paper/2107.11630/) (2021)
- [Deepfakes, Phrenology, Surveillance, and More! A Taxonomy of AI Privacy Risks](https://mlsanity.com/paper/2310.07879/) (2023)
- [Digital Deception: Generative Artificial Intelligence in Social Engineering and Phishing](https://mlsanity.com/paper/2310.13715/) (2023)
- [SoK: On the Semantic AI Security in Autonomous Driving](https://mlsanity.com/paper/2203.05314/) (2022)
- [Cost-Effective Hallucination Detection for LLMs](https://mlsanity.com/paper/2407.21424/) (2024)
- [AutoJailbreak: Exploring Jailbreak Attacks and Defenses through a Dependency Lens](https://mlsanity.com/paper/2406.03805/) (2024)
- [The Pursuit of Fairness in Artificial Intelligence Models: A Survey](https://mlsanity.com/paper/2403.17333/) (2024)
- [k-Anonymity in Practice: How Generalisation and Suppression Affect Machine Learning Classifiers](https://mlsanity.com/paper/2102.04763/) (2021)
- [Offensive AI: Enhancing Directory Brute-forcing Attack with the Use of Language Models](https://mlsanity.com/paper/2404.14138/) (2024)
- [Securing AI Systems: A Guide to Known Attacks and Impacts](https://mlsanity.com/paper/2506.23296/) (2025)

## Abstract (arXiv)

As the social implementation of AI has been steadily progressing, research and development related to AI security has also been increasing. However, existing studies have been limited to organizing related techniques, attacks, defenses, and risks in terms of specific domains or AI elements. Thus, it extremely difficult to understand the relationships among them and how negative impacts on stakeholders are brought about. In this paper, we argue that the knowledge, technologies, and social impacts related to AI security should be holistically organized to help understand relationships among them. To this end, we first develop an AI security map that holistically organizes interrelationships among elements related to AI security as well as negative impacts on information systems and stakeholders. This map consists of the two aspects, namely the information system aspect (ISA) and the external influence aspect (EIA). The elements that AI should fulfill within information systems are classified under the ISA. The EIA includes elements that affect stakeholders as a result of AI being attacked or misused. For each element, corresponding negative impacts are identified. By referring to the AI security map, one can understand the potential negative impacts, along with their causes and countermeasures. Additionally, our map helps clarify how the negative impacts on AI-based systems relate to those on stakeholders. We show some findings newly obtained by referring to our map. We also provide several recommendations and open problems to guide future AI security communities.