GenAI Model Security

Ken Huang (), Ben Goertzel (), Daniel Wu () and Anita Xie
Additional contact information
Ken Huang: DistributedApps.ai
Ben Goertzel: SingularityNET Foundation
Daniel Wu: JPMorgan Chase & Co.
Anita Xie: Black Cloud Technology

Chapter Chapter 6 in Generative AI Security, 2024, pp 163-198 from Springer

Abstract: Abstract Safeguarding GenAI models against threats and aligning them with security requirements is imperative yet challenging. This chapter provides an overview of the security landscape for generative models. It begins by elucidating common vulnerabilities and attack vectors, including adversarial attacks, model inversion, backdoors, data extraction, and algorithmic bias. The practical implications of these threats are discussed, spanning domains like finance, healthcare, and content creation. The narrative then shifts to exploring mitigation strategies and innovative security paradigms. Differential privacy, blockchain-based provenance, quantum-resistant algorithms, and human-guided reinforcement learning are analyzed as potential techniques to harden generative models. Broader ethical concerns surrounding transparency, accountability, deepfakes, and model interpretability are also addressed. The chapter aims to establish a conceptual foundation encompassing both the technical and ethical dimensions of security for generative AI. It highlights open challenges and lays the groundwork for developing robust, trustworthy, and human-centric solutions. The multifaceted perspective spanning vulnerabilities, implications, and solutions is intended to further discourse on securing society’s growing reliance on generative models. Frontier model security is discussed using Anthropic proposed approach.

Keywords: Generative models; Adversarial attacks; Model inversion; Data extraction; Backdoors; Bias; Fairness; Interpretability; Transparency; Accountability; Deepfakes; Differential privacy; Regularization; Blockchain; Quantum computing; Post-quantum cryptography; Reinforcement learning; Human feedback; Alignment; Ethics; Threat modeling; Risk assessment; Mitigation strategies; Access control; Auditing; Governance; Regulation; Responsible AI; Trust (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/978-3-031-54252-7_6

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