mlsanityData to 14 Oct 2026

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

arXiv 2508.08583 ↗First posted 12 Aug 2025cs.CR cs.AIcited by 0

Not on the map. Why?

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.

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

28 of 60 references.

References · 28

  1. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,415
  2. 2013Intriguing properties of neural networkscited by 5,155
  3. 2016Deep Learning with Differential Privacycited by 2,728
  4. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,281
  5. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,839
  6. 2023Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injectioncited by 1,443
  7. 2019CNN-generated images are surprisingly easy to spot... for nowcited by 847
  8. 2016Stealing Machine Learning Models via Prediction APIscited by 619
  9. 2020Federated Learning with Matched Averagingcited by 564
  10. 2021Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarkscited by 395
  11. 2023Poisoning Web-Scale Training Datasets is Practicalcited by 290
  12. 2021Machine Unlearning of Features and Labelscited by 229
16 more
  1. 2019Detecting AI Trojans Using Meta Neural Analysiscited by 199
  2. 2018Gazelle: A Low Latency Framework for Secure Neural Network Inferencecited by 193
  3. 2017No Classification without Representation: Assessing Geodiversity Issues in Open Data Sets for the Developing Worldcited by 153
  4. 2020A Survey of Privacy Attacks in Machine Learningcited by 146
  5. 2024PLeak: Prompt Leaking Attacks against Large Language Model Applicationscited by 127
  6. 2021A Comprehensive Taxonomy for Explainable Artificial Intelligence: A Systematic Survey of Surveys on Methods and Conceptscited by 91
  7. 2021Detecting Adversarial Examples Is (Nearly) As Hard As Classifying Themcited by 48
  8. 2023Deepfakes, Phrenology, Surveillance, and More! A Taxonomy of AI Privacy Riskscited by 31
  9. 2023Digital Deception: Generative Artificial Intelligence in Social Engineering and Phishingcited by 27
  10. 2022SoK: On the Semantic AI Security in Autonomous Drivingcited by 22
  11. 2024Cost-Effective Hallucination Detection for LLMscited by 18
  12. 2024AutoJailbreak: Exploring Jailbreak Attacks and Defenses through a Dependency Lenscited by 14
  13. 2024The Pursuit of Fairness in Artificial Intelligence Models: A Surveycited by 13
  14. 2021k-Anonymity in Practice: How Generalisation and Suppression Affect Machine Learning Classifierscited by 12
  15. 2024Offensive AI: Enhancing Directory Brute-forcing Attack with the Use of Language Modelscited by 2
  16. 2025Securing AI Systems: A Guide to Known Attacks and Impactscited by 1

Built on by

Not cited yet.