Eliciting sandbagged LLM capability via structural interventions on attention-head functional networks

Reliably evaluating the capabilities of advanced AI systems is critical for safe development and deployment, yet such evaluations may be undermined by sandbagging, in which a model strategically suppresses capabilities it possesses. Existing work has developed controlled sandbagging settings and methods for detecting or eliciting hidden capabilities, but comparatively little is known about how a model’s internal computation changes when its capabilities are suppressed. In this work, we study sandbagging through the lens of internal functional organization. Inspired by network neuroscience, we construct functional graphs from attention-head activations and compare their organization under honest and sandbagging conditions while holding the model architecture and weights fixed. Across model architectures and community detection methods, we find a consistent structural signature: sandbagging is associated with reduced functional connectivity within detected communities. We further show that this reorganization provides an actionable signal for capability elicitation. Ablating attention heads that exhibit the largest connectivity changes substantially recovers suppressed performance while largely preserving performance under honest behavior. Moreover, incorporating these structural signals further improves existing inference-level and weight-level elicitation methods. Together, our results show that sandbagging is accompanied by systematic reorganization of internal computation and demonstrate that functional network structure can provide both a mechanistic lens for understanding capability suppression and a practical signal for recovering hidden capabilities.