Carnegie Mellon University
Browse
- No file added yet -

Lessons Learned in Coordinated Disclosure for Artificial Intelligence and Machine Learning Systems

Download (738.94 kB)
Version 2 2024-08-29, 15:25
Version 1 2024-08-29, 01:30
report
posted on 2024-08-29, 15:25 authored by Allen HouseholderAllen Householder, Vijay SarvepalliVijay Sarvepalli, Jeffrey HavrillaJeffrey Havrilla, Matthew ChurillaMatthew Churilla, Lena PonsLena Pons, Shing-hon LauShing-hon Lau, Nathan VanhoudnosNathan Vanhoudnos, Andrew KompanekAndrew Kompanek, Lauren McIlvennyLauren McIlvenny
In this paper, SEI researchers incorporate several lessons learned from the coordination of artificial intelligence (AI) and machine learning (ML) vulnerabilities at the SEI’s CERT Coordination Center (CERT/CC). They also include their observations of public discussions of AI vulnerability coordination cases. Risk management within the context of AI systems is a rapidly evolving and substantial space. Even when restricted to cybersecurity risk management, AI systems require comprehensive security, such as what the National Institute of Standards and Technology (NIST) describes in The NIST Cybersecurity Framework (CSF). In this paper, the authors focus on one part of cybersecurity risk management for AI systems: the CERT/CC’s lessons learned from applying the Coordinated Vulnerability Disclosure (CVD) process to reported “vulnerabilities” in AI and ML systems.

History

Publisher Statement

This material is based upon work funded and supported by the Department of Defense under Contract No. FA8702-15-D-0002 with Carnegie Mellon University for the operation of the Software Engineering Institute, a federally funded research and development center. The view, opinions, and/or findings contained in this material are those of the author(s) and should not be construed as an official Government position, policy, or decision, unless designated by other documentation. References herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by Carnegie Mellon University or its Software Engineering Institute. This report was prepared for the SEI Administrative Agent AFLCMC/AZS 5 Eglin Street Hanscom AFB, MA 01731-2100. NO WARRANTY. THIS CARNEGIE MELLON UNIVERSITY AND SOFTWARE ENGINEERING INSTITUTE MATERIAL IS FURNISHED ON AN "AS-IS" BASIS. CARNEGIE MELLON UNIVERSITY MAKES NO WARRANTIES OF ANY KIND, EITHER EXPRESSED OR IMPLIED, AS TO ANY MATTER INCLUDING, BUT NOT LIMITED TO, WARRANTY OF FITNESS FOR PURPOSE OR MERCHANTABILITY, EXCLUSIVITY, OR RESULTS OBTAINED FROM USE OF THE MATERIAL. CARNEGIE MELLON UNIVERSITY DOES NOT MAKE ANY WARRANTY OF ANY KIND WITH RESPECT TO FREEDOM FROM PATENT, TRADEMARK, OR COPYRIGHT INFRINGEMENT. [DISTRIBUTION STATEMENT A] This material has been approved for public release and unlimited distribution. Please see Copyright notice for non-US Government use and distribution

Copyright Statement

Copyright 2024 Carnegie Mellon University.

Usage metrics

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC