Maximal Brain Damage Without Data or Optimization: Disrupting Neural Networks via Sign-Bit Flips

Overview

Deep neural networks are vulnerable to catastrophic failure from flipping just a few sign bits in model parameters. We present Deep Neural Lesion (DNL), a data-free method that identifies and exploits critical parameters across vision and language domains.

Our approach requires only write access to stored weights—no training data, no optimization, minimal computation. This makes it practical under realistic threat models where attackers compromise model storage through firmware exploits, rootkits, DMA attacks, or Rowhammer vulnerabilities.

• ResNet-50: 2 sign flips → 99.8% accuracy drop
• Mask R-CNN / YOLOv8-seg: 1–2 flips collapse detection and segmentation
• Qwen3-30B & Nemotron 8B: Few flips reduce reasoning and task accuracy to near-zero

Methodology

Attack Variants

Pass-Free DNL: Identifies critical parameters using magnitude-based heuristics and early-layer targeting with zero additional computation.

Enhanced 1-Pass DNL: Refines parameter selection with a single forward and backward pass on random inputs, achieving stronger attacks with minimal overhead.

Why Sign-Bit Flips Matter

• Clean disruption: Flipping the sign bit instantly negates weights, maximizing feature map corruption
• Hardware feasibility: Bit flips in fixed positions are more reliably achievable in physical attacks
• Early-layer criticality: High-magnitude weights in early layers have outsized impact on all downstream representations
• Universal vulnerability: Pattern holds across CNNs, Transformers, and MoE architectures

Image Classification Results

Accuracy reduction (AR) as a function of model scale across diverse architectures. All models remain highly vulnerable, with early-layer targeting dominating architecture choice in determining susceptibility.

Detection & Segmentation Results

Baseline: Mask R-CNN correctly detects and segments objects with high confidence.

After 1-2 flips: Detection and segmentation collapse to random outputs.

Language Models Results

Language Model Attack Patterns

Decoder-only models (Qwen, Nemotron): Sign-bit attacks are highly effective, especially when targeting the first 5 blocks. Two targeted flips can reduce Qwen3-30B accuracy from 78% to 0%.

MoE routing: Targeting different experts in Mixture-of-Experts models amplifies the attack impact, as each token's routing path becomes compromised.

Encoder models (BERT, RoBERTa): Early-layer sign-bit flips remain highly destructive across diverse architectures.

Generation behavior: Attacked models degenerate into repetitive, nonsensical text rather than near-miss errors—indicating catastrophic failure rather than graceful degradation.

Attack Strategy Comparison

Performance of different targeting heuristics across 48 ImageNet models. Magnitude-based selection combined with early-layer targeting significantly outperforms random flips and matches top-k magnitude selection while remaining data-free and computationally lightweight.

Defense & Implications

While the vulnerability is severe, we demonstrate that selective hardening of critical parameters provides practical defense. By protecting only the top 0.1-1% most vulnerable weights, models achieve substantial resilience without major performance overhead.

DNL easily bypasses common defenses such as weight quantization, pruning, and simple checksumming schemes. The data-free nature and reliance on magnitude-based targeting make it robust against defenses that assume adversaries lack threat model knowledge or computational resources.

Key Takeaways

• Critical parameters are universally identifiable across architectures and domains
• Defense cost scales better than attack identification for large models
• Once attackers gain parameter write access, minimal computation suffices for catastrophic failure
• Data-free nature makes detection and attribution exceptionally challenging

Citation