AI companies should publish security assessments

2024-06-17 · Source: Redwood Research blog · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy · Depth: Intermediate, medium

Summary

AI companies should engage third-party security experts to assess and red-team their systems against critical threat models, subsequently publishing high-level findings. These assessments should cover model weight exfiltration, theft of algorithmic secrets and IP, model tampering (e.g., backdoors), unauthorized compute access, and persistent attacker presence. The goal is to increase transparency regarding security posture, especially given the current perception of poor security within AI companies. While defending against state-level actors may be intractable for competitive AI companies, improved security against other threats and actors (including the AIs themselves) is crucial. Publicizing these findings, along with the assessors' identities, aims to foster better security practices and inform the broader AI community about the evolving security landscape.

Key takeaway

For CTOs and VPs of Engineering evaluating AI security strategies, prioritize engaging third-party experts for security assessments and red-teaming against defined threat models. Publicly sharing high-level findings, even if challenging, will drive industry-wide security improvements and inform stakeholders, ultimately strengthening the collective defense against evolving AI-specific threats. Your transparency can set a new industry standard.

Key insights

AI companies should publicly disclose third-party security assessment findings against defined threat models to improve collective security.

Principles

• Transparency improves security.
• Security is a collective action problem.
• Assessments should cover specific threat models.

Method

Commission third-party security experts to assess systems against threat models (exfiltration, IP theft, tampering, unauthorized access, persistent presence), then publish high-level findings and assessor identities.

In practice

• Define specific threat models for assessment.
• Publish high-level security robustness claims.
• Consider multi-company simultaneous assessments.

Topics

• AI Security Assessments
• Threat Models
• Model Weight Exfiltration
• Algorithmic IP Theft
• Model Tampering

Best for: CTO, VP of Engineering/Data, AI Architect, AI Security Engineer, Director of AI/ML, Policy Maker

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Editorial summary, takeaway, and curation by AIssential. Original article published by Redwood Research blog.