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The AI Security Pyramid of Pain (2402.11082v1)

Published 16 Feb 2024 in cs.CR and cs.AI

Abstract: We introduce the AI Security Pyramid of Pain, a framework that adapts the cybersecurity Pyramid of Pain to categorize and prioritize AI-specific threats. This framework provides a structured approach to understanding and addressing various levels of AI threats. Starting at the base, the pyramid emphasizes Data Integrity, which is essential for the accuracy and reliability of datasets and AI models, including their weights and parameters. Ensuring data integrity is crucial, as it underpins the effectiveness of all AI-driven decisions and operations. The next level, AI System Performance, focuses on MLOps-driven metrics such as model drift, accuracy, and false positive rates. These metrics are crucial for detecting potential security breaches, allowing for early intervention and maintenance of AI system integrity. Advancing further, the pyramid addresses the threat posed by Adversarial Tools, identifying and neutralizing tools used by adversaries to target AI systems. This layer is key to staying ahead of evolving attack methodologies. At the Adversarial Input layer, the framework addresses the detection and mitigation of inputs designed to deceive or exploit AI models. This includes techniques like adversarial patterns and prompt injection attacks, which are increasingly used in sophisticated attacks on AI systems. Data Provenance is the next critical layer, ensuring the authenticity and lineage of data and models. This layer is pivotal in preventing the use of compromised or biased data in AI systems. At the apex is the tactics, techniques, and procedures (TTPs) layer, dealing with the most complex and challenging aspects of AI security. This involves a deep understanding and strategic approach to counter advanced AI-targeted attacks, requiring comprehensive knowledge and planning.

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References (25)
  1. Bianco, D., “The pyramid of pain.” https://detect-respond.blogspot.com/2013/03/the-pyramid-of-pain.html (2013). Accessed: 2024-01-04.
  2. Hutchins, E. M., Cloppert, M. J., and Amin, R. M., “Intelligence-driven computer network defense informed by analysis of adversary campaigns and intrusion kill chains,” Leading Issues in Information Warfare & Security Research 1(1), 80 (2011).
  3. Papernot, N., McDaniel, P., and Goodfellow, I., “Transferability in machine learning: from phenomena to black-box attacks using adversarial samples,” arXiv preprint arXiv:1605.07277 (2016).
  4. Liao, X., Yu, W., Li, B., Li, Z., and Wang, K., “A deep learning approach to real-time network security situational awareness,” IEEE Communications Letters 17(1), 180–183 (2013).
  5. Biggio, B. and Roli, F., “Wild patterns: Ten years after the rise of adversarial machine learning,” Pattern Recognition 84, 317–331 (2018).
  6. Daszczyszak, R., Ellis, D., Luke, S., and Whitley, S., “Ttp-based hunting,” MITRE CORP MCLEAN VA, Tech. Rep (2019).
  7. Kurakin, A., Goodfellow, I., and Bengio, S., “Adversarial examples in the physical world,” arXiv preprint arXiv:1607.02533 (2016).
  8. Akhtar, N. and Mian, A., “Threat of adversarial attacks on deep learning in computer vision: A survey,” IEEE Access 6, 14410–14430 (2018).
  9. Tramèr, F., Kurakin, A., Papernot, N., Goodfellow, I., Boneh, D., and McDaniel, P., “Ensemble adversarial training: Attacks and defenses,” arXiv preprint arXiv:1705.07204 (2017).
  10. Apruzzese, G., Colajanni, M., Ferretti, L., Guido, A., and Marchetti, M., “On the effectiveness of machine and deep learning for cybersecurity,” in [2018 International Conference on Cyber Security and Protection of Digital Services (Cyber Security) ], 1–8, IEEE (2018).
  11. Papernot, N., McDaniel, P., Jha, S., Fredrikson, M., Celik, Z. B., and Swami, A., “The limitations of deep learning in adversarial settings,” in [2016 IEEE European Symposium on Security and Privacy (EuroS&P) ], 372–387, IEEE (2016).
  12. Yuan, X., He, P., Zhu, Q., and Li, X., “Adversarial examples: Attacks and defenses for deep learning,” IEEE Transactions on Neural Networks and Learning Systems 30(9), 2805–2824 (2019).
  13. Konečný, J., McMahan, H. B., Ramage, D., and Richtárik, P., “Federated optimization: Distributed machine learning for on-device intelligence,” arXiv preprint arXiv:1610.02527 (2016).
  14. Mosca, M., Roetteler, M., and Takagi, T., “Cybersecurity in an era with quantum computers: will we be ready?,” IEEE Security & Privacy 17(5), 38–46 (2019).
  15. Oseni, A., Moustafa, N., Janicke, H., Liu, P., Tari, Z., and Vasilakos, A., “Security and privacy for artificial intelligence: Opportunities and challenges,” ar5iv (2020).
  16. Pakmehr, A., Aßmuth, A., Neumann, C. P., and Pirkl, G., “Security challenges for cloud or fog computing-based ai applications,” ar5iv (2020).
  17. Taddeo, M. and Floridi, L., “Regulate artificial intelligence to avert cyber arms race,” Nature 556(7701), 296–298 (2018).
  18. Parameswaran, S., Harguess, J., Barngrover, C., Shafer, S., and Reese, M., “Evaluation schemes for video and image anomaly detection algorithms,” in [Automatic Target Recognition XXVI ], 9844, 98–109, SPIE (2016).
  19. Nicolae, M.-I., Sinn, M., Tran, M. N., Buesser, B., Rawat, A., Wistuba, M., Zantedeschi, V., Baracaldo, N., Chen, B., Ludwig, H., et al., “Adversarial robustness toolbox v1. 0.0,” arXiv preprint arXiv:1807.01069 (2018).
  20. Bai, T., Luo, J., Zhao, J., Wen, B., and Wang, Q., “Recent advances in adversarial training for adversarial robustness,” arXiv preprint arXiv:2102.01356 (2020).
  21. Vassilev, A., Oprea, A., Fordyce, A., and Andersen, H., “Adversarial machine learning: A taxonomy and terminology of attacks and mitigations,” (2024).
  22. Holt, E., Malkastian, A., Smith, S., Ward, C., and Harguess, J., “Baseline evaluation methodology for adversarial patterns on object detection models,” in [2021 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) ], 1–6, IEEE (2021).
  23. Jutras, M., Liang, E., Leary, S., Ward, C., and Manville, K., “Detecting physical adversarial patch attacks with object detectors,” in [2022 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) ], 1–7, IEEE (2022).
  24. Braunegg, A., Chakraborty, A., Krumdick, M., Lape, N., Leary, S., Manville, K., Merkhofer, E., Strickhart, L., and Walmer, M., “Apricot: A dataset of physical adversarial attacks on object detection,” in [Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXI 16 ], 35–50, Springer (2020).
  25. MITRE), “Mitre atlas.” atlas.mitre.org (2020). Accessed: January 22, 2024.

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