HYDRA: A Hybrid Heuristic-Guided Deep Representation Architecture for Predicting Latent Zero-Day Vulnerabilities in Patched Functions

TL;DR

HYDRA tackles latent zero-day vulnerabilities in patched functions by merging symbolic heuristics with deep semantic representations from GraphCodeBERT and latent-space learning via a Variational Autoencoder. The unsupervised HYDRA pipeline learns a fused feature representation from a five-bit heuristic vector and a 768-dimensional code embedding, then clusters functions to reveal latent risk patterns without requiring ground-truth labels. Across patched code from Chrome, Android, and ImageMagick, HYDRA surfaces latent risks in 13–24% of functions and demonstrates superior clustering quality compared with baselines. This hybrid approach enables more proactive post-patch vulnerability auditing and supports triage of unseen or emerging risk patterns.

Abstract

Software security testing, particularly when enhanced with deep learning models, has become a powerful approach for improving software quality, enabling faster detection of known flaws in source code. However, many approaches miss post-fix latent vulnerabilities that remain even after patches typically due to incomplete fixes or overlooked issues may later lead to zero-day exploits. In this paper, we propose $HYDRA$, a $Hy$brid heuristic-guided $D$eep $R$epresentation $A$rchitecture for predicting latent zero-day vulnerabilities in patched functions that combines rule-based heuristics with deep representation learning to detect latent risky code patterns that may persist after patches. It integrates static vulnerability rules, GraphCodeBERT embeddings, and a Variational Autoencoder (VAE) to uncover anomalies often missed by symbolic or neural models alone. We evaluate HYDRA in an unsupervised setting on patched functions from three diverse real-world software projects: Chrome, Android, and ImageMagick. Our results show HYDRA predicts 13.7%, 20.6%, and 24% of functions from Chrome, Android, and ImageMagick respectively as containing latent risks, including both heuristic matches and cases without heuristic matches ($None$) that may lead to zero-day vulnerabilities. It outperforms baseline models that rely solely on regex-derived features or their combination with embeddings, uncovering truly risky code variants that largely align with known heuristic patterns. These results demonstrate HYDRA's capability to surface hidden, previously undetected risks, advancing software security validation and supporting proactive zero-day vulnerabilities discovery.

Figures (6)

• Figure 1: Illustrative Examples of Common Risky Heuristic Patterns Utilized in HYDRA.
• Figure 2: The proposed architecture of HYDRA.
• Figure 3: Distribution of None labeled patched functions across semantic embedding clusters for each project by HYDRA. Although None cases dominate each cluster, they tend to align with a specific heuristic type H (annotated), indicating latent similarity to known vulnerability patterns within that semantic cluster.
• Figure 4: VAE reconstruction loss during HYDRA training. Both training and validation losses converge smoothly, with best validation performance at epoch 186.
• Figure 5: Example from ImageMagick: HYDRA flags the ClipImage wrapper function using heuristic H1 (missing null check).