Mohammad Farhad

Mohammad Farhad, Shuvalaxmi Dass

Software security relies on effective vulnerability detection and patching, yet determining whether a patch fully eliminates risk remains an underexplored challenge. Existing vulnerability benchmarks often treat patched functions as inherently benign, overlooking the possibility of residual security risks. In this work, we analyze vulnerable-benign function pairs from the PrimeVul, a benchmark dataset using multiple code language models (Code LMs) to capture semantic similarity, complemented by Tree-sitter-based abstract syntax tree (AST) analysis for structural similarity. Building on these signals, we propose Residual Risk Scoring (RRS), a unified framework that integrates embedding-based semantic similarity, localized AST-based structural similarity, and cross-model agreement to estimate residual risk in code. Our analysis shows that benign functions often remain highly similar to their vulnerable counterparts both semantically and structurally, indicating potential persistence of residual risk. We further find that approximately $61\%$ of high-RRS code pairs exhibit $13$ distinct categories of residual issues (e.g., null pointer dereferences, unsafe memory allocation), validated using state-of-the-art static analysis tools including Cppcheck, Clang-Tidy, and Facebook-Infer. These results demonstrate that code-level similarity provides a practical signal for prioritizing post-patch inspection, enabling more reliable and scalable security assessment in real-world open-source software pipelines.

Mohammad Farhad, Sabbir Rahman, Shuvalaxmi Dass

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.