Sparse Neuron Ablation Triggers Catastrophic Collapse of the Language Core in Large Vision-Language Models

Researchers identified critical vulnerabilities in Large Vision-Language Models by discovering that catastrophic system collapse can be triggered by ablating just 4-5,000 neurons—a minuscule fraction of model parameters. The study reveals that these vulnerabilities are concentrated in the language backbone rather than vision components, exposing structural dependencies that challenge assumptions about model robustness.

This research exposes a fundamental fragility in large vision-language models that contradicts widespread assumptions about distributed neural redundancy. The discovery that LLaVA-1.5-7b can fail catastrophically from ablating merely four neurons suggests that despite containing billions of parameters, these models rely on sparse, critical pathways rather than distributed processing. This pattern mirrors findings in biological neural networks but challenges the robustness narrative often presented in AI deployment contexts.

The concentration of vulnerabilities in down-projection layers of language backbones indicates an architectural bottleneck where visual information integrates into linguistic processing. This mechanistic insight matters because it reveals that current LVLM designs haven't achieved true redundancy—a concerning implication for production systems where robustness is essential. The two-stage collapse pattern (degradation followed by sudden failure) suggests a tipping point phenomenon where cascading failures activate once critical thresholds are crossed.

For practitioners deploying LVLMs in safety-critical applications, this research signals potential risks from adversarial attacks targeting these sparse neurons or from natural model degradation in resource-constrained environments. The findings suggest that current model evaluation metrics may miss hidden brittleness, as systems can appear functional while operating near critical failure points. Developers should consider adversarial training specifically targeting language backbone neurons and explore architectural redesigns that distribute critical functions across broader parameter sets to achieve genuine robustness.

• →LLaVA-1.5-7b exhibits catastrophic collapse when just 4 neurons are ablated, revealing extreme sparsity in critical functionality.
• →Vision-language model vulnerabilities concentrate in language component down-projection layers rather than vision encoders.
• →A consistent two-stage collapse pattern indicates tipping-point dynamics where systems degrade progressively before sudden complete failure.
• →Current LVLM architectures lack true redundancy despite billions of parameters, suggesting single points of failure in production systems.
• →These findings have implications for adversarial robustness and model safety in safety-critical applications.