RedacBench: Can AI Erase Your Secrets?

2025-09-22 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy, Data Science & Analytics · Depth: Expert, extended

Summary

RedacBench is a new, comprehensive benchmark designed to evaluate policy-conditioned redaction capabilities of Large Language Models (LLMs) across various domains and strategies. Developed by researchers at KAIST, it addresses limitations of existing benchmarks by focusing on the selective removal of policy-violating information while preserving original text semantics. The benchmark comprises 514 human-authored texts from individual, corporate, and government sources, paired with 187 security policies. Performance is quantified using 8,053 annotated propositions, measuring both "security" (removal of sensitive propositions) and "utility" (preservation of non-sensitive propositions). Initial experiments with multiple redaction strategies and state-of-the-art LLMs, including GPT-5-mini and Claude-Sonnet-4, indicate that while advanced models can improve security, maintaining utility remains a significant challenge. The researchers have released RedacBench and an interactive web-based playground to foster further research.

Key takeaway

For AI Engineers and CTOs building or deploying LLM-based systems that handle sensitive information, RedacBench highlights the critical trade-off between data security and text utility. You should rigorously evaluate redaction solutions not just for explicit PII removal, but for their ability to prevent inference of sensitive information under specific policies while minimizing semantic degradation. Prioritize solutions that offer configurable policy adherence and consider iterative redaction or models like Claude-Sonnet-4 for better balance, recognizing that human oversight remains crucial for high-stakes applications.

Key insights

RedacBench evaluates LLM redaction by balancing security (sensitive data removal) and utility (non-sensitive data preservation) under policy constraints.

Principles

• Redaction must be policy-conditioned.
• Security and utility are often in trade-off.
• Iterative redaction can compensate for model scale.

Method

RedacBench uses a proposition-based evaluation framework: redact text per policy, analyze proposition inferability post-redaction, then calculate security and utility scores based on a confusion matrix.

In practice

• Use adversarial redaction for semantic removal.
• Consider iterative redaction for enhanced security.
• Prioritize models like Claude-Sonnet-4 for utility balance.

Topics

• LLM Redaction
• Data Privacy Benchmarks
• Policy-Conditioned Redaction
• Security-Utility Trade-off
• Proposition-based Evaluation

Best for: Research Scientist, AI Engineer, CTO, AI Researcher, AI Scientist, AI Security Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.