TamperBench: Systematically Stress-Testing LLM Safety Under Fine-Tuning and Tampering

TL;DR

TamperBench is a comprehensive framework designed to systematically evaluate the tamper resistance of large language models against various attacks, providing standardized metrics, reproducibility, and insights into defense effectiveness.

Contribution

It introduces the first unified platform for evaluating LLM tamper resistance, including attack repositories, hyperparameter sweeps, and safety-utility assessments, enabling consistent comparison across models.

Findings

Jailbreak-tuning is the most severe attack.

Post-training improves tamper resistance.

Triplet is a leading defense method.

Abstract

As increasingly capable open-weight large language models (LLMs) are deployed, improving their tamper resistance against unsafe modifications, whether accidental or intentional, becomes critical to minimize risks. However, there is no standard approach to evaluate tamper resistance. Varied data sets, metrics, and tampering configurations make it difficult to compare safety, utility, and robustness across different models and defenses. To this end, we introduce TamperBench, the first unified framework to systematically evaluate the tamper resistance of LLMs. TamperBench (i) curates a repository of state-of-the-art weight-space fine-tuning attacks and latent-space representation attacks; (ii) enables realistic adversarial evaluation through systematic hyperparameter sweeps per attack-model pair; and (iii) provides both safety and utility evaluations. TamperBench requires minimal additional code to specify any fine-tuning configuration, alignment-stage defense method, and metric suite while ensuring end-to-end reproducibility. We use TamperBench to evaluate 21 open-weight LLMs, including defense-augmented variants, across nine tampering threats using standardized safety and capability metrics with hyperparameter sweeps per model-attack pair. This yields novel insights, including effects of post-training on tamper resistance, that jailbreak-tuning is typically the most severe attack, and that Triplet emerges as a leading alignment-stage defense. Code is available at: https://github.com/criticalml-uw/TamperBench