DeepGuard: Secure Code Generation via Multi-Layer Semantic Aggregation
Large Language Models (LLMs) for code generation can replicate insecure patterns from their training data. To mitigate this, a common strategy for security hardening is to fine-tune models using supervision derived from the final transformer layer. However, this design may suffer from a final-layer bottleneck: vulnerability-discriminative cues can be distributed across layers and become less detectable near the output representations optimized for next-token prediction. To diagnose this issue, we perform layer-wise linear probing. We observe that vulnerability-related signals are most detectable in a band of intermediate-to-upper layers yet attenuate toward the final layers. Motivated by ...
| Authors | Li Huang, Zhongxin Liu, Yifan Wu et al. |
|---|---|
| Primary category | cs.SE |
| Published | 10 April 2026 |
| Last updated at source | 10 April 2026 |
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