mlsanityData to 14 Oct 2026

Attacks, Defenses, And Tools: A Framework To Facilitate Robust AI/ML Systems

arXiv 2202.09465 ↗First posted 18 Feb 2022cs.CR cs.LGcited by 0

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Abstract (arXiv)

Software systems are increasingly relying on Artificial Intelligence (AI) and Machine Learning (ML) components. The emerging popularity of AI techniques in various application domains attracts malicious actors and adversaries. Therefore, the developers of AI-enabled software systems need to take into account various novel cyber-attacks and vulnerabilities that these systems may be susceptible to. This paper presents a framework to characterize attacks and weaknesses associated with AI-enabled systems and provide mitigation techniques and defense strategies. This framework aims to support software designers in taking proactive measures in developing AI-enabled software, understanding the attack surface of such systems, and developing products that are resilient to various emerging attacks associated with ML. The developed framework covers a broad spectrum of attacks, mitigation techniques, and defensive and offensive tools. In this paper, we demonstrate the framework architecture and its major components, describe their attributes, and discuss the long-term goals of this research.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 12 of its 18 references that are in the map. First posted on arXiv 18 Feb 2022.

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References · 12

  1. 2014Explaining and Harnessing Adversarial Examplescited by 7,364
  2. 2013Intriguing properties of neural networkscited by 5,155
  3. 2016Towards Evaluating the Robustness of Neural Networkscited by 2,998
  4. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,281
  5. 2015DeepFool: a simple and accurate method to fool deep neural networkscited by 1,318
  6. 2015The Limitations of Deep Learning in Adversarial Settingscited by 790
  7. 2015Distillation as a Defense to Adversarial Perturbations against Deep Neural Networkscited by 678
  8. 2016Stealing Machine Learning Models via Prediction APIscited by 619
  9. 2017Adversarial Examples: Attacks and Defenses for Deep Learningcited by 351
  10. 2019Adversarial Attacks and Defenses in Images, Graphs and Text: A Reviewcited by 237
  11. 2017Towards Poisoning of Deep Learning Algorithms with Back-gradient Optimizationcited by 197
  12. 2017On the (Statistical) Detection of Adversarial Examplescited by 180

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