Blocking live failures with synchronous monitors

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

Summary

AI control schemes frequently incorporate monitoring to review actions by untrusted models, serving two primary goals: detection and prevention. Detection aims to identify misbehavior for future understanding and prevention, exemplified by current AI company practices in identifying misuse requests, reward hacking, and hallucination. Prevention, conversely, focuses on mitigating immediate harmful effects, as seen with Anthropic's constitutional classifiers and Claude Code auto mode, which prevent jailbreaks and destructive mistakes in real time. The required latency for monitoring varies significantly; detection allows for substantial latency, while prevention, especially against "live failures" (rapid, hard-to-recover harm), necessitates quick, often synchronous, monitoring. Synchronous monitors run after an LLM generates an action but before execution, effectively blocking harmful actions. The effectiveness of asynchronous monitors depends on factors like attack latency, rate of damage, and the nature of accumulating damage (irreversible damage vs. recovery costs).

Key takeaway

For AI/ML Directors evaluating AI safety and control mechanisms, understanding the distinction between detection and prevention monitoring is critical. Your teams should prioritize synchronous, blocking monitors for AI systems with access to sensitive data or critical infrastructure, especially where "live failures" like weight exfiltration or rogue internal deployments pose a high, rapid, and irreversible risk. This proactive approach minimizes immediate damage, even if it incurs higher latency and inference costs, by preventing harmful actions before they execute.

Key insights

AI monitoring systems must align latency and blocking capabilities with the specific goals of detection versus real-time prevention of "live failures."

Principles

• Prevention requires low-latency monitoring.
• Synchronous monitors block actions pre-execution.
• Asynchronous monitors suit threats with attack latency.

Method

Implement synchronous, blocking monitors for AI agents with permissions that could lead to "live failures," considering options like human review, action replacement by trusted models, or re-sampling from the same model.

In practice

• Use constitutional classifiers for jailbreak prevention.
• Employ Claude Code auto mode for real-time error blocking.
• Prioritize blocking monitors for sensitive AI deployments.

Topics

• AI Monitoring
• Synchronous Monitors
• Live Failures
• Attack Latency
• Damage Prevention

Best for: CTO, VP of Engineering/Data, Director of AI/ML, AI Security Engineer, AI Scientist, MLOps Engineer

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