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Secure Software Development Practices for Generative Artificial Intelligence and Dual-Use Foundation Models

Artificial Intelligence model and system development is still much more of an art than an exact science, requiring developers to interact with model code, training data, and other parameters over multiple iterations. Training datasets may be acquired from unknown, untrusted sources. Model weights and other training parameters can be susceptible to malicious tampering.

  • Author(s):
  • Martin Stanley
  • Harold Booth
  • Murugiah Souppaya
  • Apostol Vassilev
  • Michael Ogata
  • Karen Scarfone
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Secure Software Development Practices for Generative Artificial Intelligence and Dual-Use Foundation Models
Format:
  • White Paper
Topics:
Website:Visit Publisher Website
Publisher:National Institute of Standards and Technology (NIST)
Published:July 1, 2024
License:Public Domain

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