arXiv Notify me• Jan 31, 2025paper

Constitutional Classifiers: Defending against Universal Jailbreaks across Thousands of Hours of Red Teaming

Jared Kaplan(guest)

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Executive Summary

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Adversarial Robustness in LLMs Constitutional AI as a Safety Mechanism

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Concerns Raised

The inherent vulnerability of LLMs to universal jailbreaks.
The 23.7% inference overhead may be a performance concern for latency-sensitive applications.

Opportunities Identified

Deploying Constitutional Classifiers to significantly enhance the safety of production LLMs.
Using the constitutional approach to create scalable and maintainable AI safety systems.
Establishing large-scale red teaming as a standard for validating AI security measures.

Key Themes

Research Findings3

During over 3,000 estimated hours of red teaming, no universal jailbreak was found that could extract information from an early Constitutional Classifier-guarded LLM at a similar level of detail to an unguarded model.

Enhanced Constitutional Classifiers demonstrated robust defense against held-out, domain-specific jailbreaks in automated evaluations.

Constitutional Classifiers cause an absolute increase of 0.38% in production-traffic refusals.

Topics

AI Safety Large Language Models (LLMs)Jailbreaking Universal Jailbreaks Adversarial Attacks Red Teaming Constitutional AI Model Safeguards AI Security Synthetic Data Generation Inference Overhead Model Robustness AI Alignment Prompt Hacking

Processed Jul 28, 2026Daily intelligence brief → yt-dlp + mlx-whisper + Gemini

Constitutional Classifiers: Defending against Universal Jailbreaks across Thousands of Hours of Red Teaming

Continue your research

Concerns Raised

Opportunities Identified

Key Themes

Adversarial Robustness in LLMs

Constitutional AI as a Safety Mechanism

Scalability and Deployment Viability

The Role of Red Teaming in AI Safety

Research Findings3

Topics