Self-Destructive Language Models

ICLR 2026 Conference Submission20359 Authors

Keywords: Self-destructive Model, Safety Alignment, Harmful Fine-tuning Attack

TL;DR: This work proposes SEAM, a novel alignment enhancing defense that transforms the LLM into a self-destructive model resistant to harmful finetuning attacks.

Abstract:

Harmful fine-tuning attacks represent a major threat to the security of large language models (LLMs), allowing adversaries to compromise safety guardrails with minimal harmful data. While existing defenses attempt to reinforce LLM alignment, they fail to address models' inherent `trainability' on harmful data, leaving them vulnerable to stronger attacks with increased learning rates or larger harmful datasets. To overcome this limitation, we introduce SEAM, a novel alignment-enhancing defense that transforms LLMs into self-destructive models with intrinsic resilience to misalignment attempts. Specifically, these models retain their capabilities for legitimate tasks while exhibiting substantial performance degradation when fine-tuned on harmful data. The protection is achieved through a novel loss function that couples the optimization trajectories of benign and harmful data, enhanced with adversarial gradient ascent to amplify the self-destructive effect. To enable practical training, we develop an efficient Hessian-free gradient estimate with theoretical error bounds. Extensive evaluation across LLMs and datasets demonstrates that SEAM creates a no-win situation for adversaries: the self-destructive models achieve state-of-the-art robustness against low-intensity attacks and undergo catastrophic performance collapse under high-intensity attacks, rendering them effectively unusable. The code
is available: https://anonymous.4open.science/r/seam-5C7E (warning: this paper contains potentially harmful content generated by LLMs.)

Primary Area: alignment, fairness, safety, privacy, and societal considerations

Submission Number: 20359