# HogVul: Black-box Adversarial Code Generation Framework Against LM-based Vulnerability Detectors

arXiv: https://arxiv.org/abs/2601.05587 · HTML: https://mlsanity.com/paper/2601.05587/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 40 references that are in the map.
- First posted on arXiv 9 Jan 2026.

## Builds on (most cited first, 19 of 19)

- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [GraphCodeBERT: Pre-training Code Representations with Data Flow](https://mlsanity.com/paper/2009.08366/) (2020)
- [CodeBLEU: a Method for Automatic Evaluation of Code Synthesis](https://mlsanity.com/paper/2009.10297/) (2020)
- [Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks](https://mlsanity.com/paper/1909.03496/) (2019)
- [Survey of Vulnerabilities in Large Language Models Revealed by Adversarial Attacks](https://mlsanity.com/paper/2310.10844/) (2023)
- [DiverseVul: A New Vulnerable Source Code Dataset for Deep Learning Based Vulnerability Detection](https://mlsanity.com/paper/2304.00409/) (2023)
- [SynCoBERT: Syntax-Guided Multi-Modal Contrastive Pre-Training for Code Representation](https://mlsanity.com/paper/2108.04556/) (2021)
- [CodeAttack: Code-Based Adversarial Attacks for Pre-trained Programming Language Models](https://mlsanity.com/paper/2206.00052/) (2022)
- [D2A: A Dataset Built for AI-Based Vulnerability Detection Methods Using Differential Analysis](https://mlsanity.com/paper/2102.07995/) (2021)
- [An Empirical Study of Deep Learning Models for Vulnerability Detection](https://mlsanity.com/paper/2212.08109/) (2022)
- [Composite Adversarial Attacks](https://mlsanity.com/paper/2012.05434/) (2020)
- [Dynamic Parametric Retrieval Augmented Generation for Test-time Knowledge Enhancement](https://mlsanity.com/paper/2503.23895/) (2025)
- [Bottom-up Policy Optimization: Your Language Model Policy Secretly Contains Internal Policies](https://mlsanity.com/paper/2512.19673/) (2025)
- [Challenging Machine Learning-based Clone Detectors via Semantic-preserving Code Transformations](https://mlsanity.com/paper/2111.10793/) (2021)
- [Multi-granular Adversarial Attacks against Black-box Neural Ranking Models](https://mlsanity.com/paper/2404.01574/) (2024)
- [Adversarial Attacks on Large Language Models Using Regularized Relaxation](https://mlsanity.com/paper/2410.19160/) (2024)
- [Code Difference Guided Adversarial Example Generation for Deep Code Models](https://mlsanity.com/paper/2301.02412/) (2023)
- [Adversarial Attacks on Code Models with Discriminative Graph Patterns](https://mlsanity.com/paper/2308.11161/) (2023)

## Built on by (most cited first, 3 of 3)

- [SARE: Sample-wise Adaptive Reasoning for Training-free Fine-grained Visual Recognition](https://mlsanity.com/paper/2603.17729/) (2026)
- [Syntax- and Compilation-Preserving Evasion of LLM Vulnerability Detectors](https://mlsanity.com/paper/2602.00305/) (2026)
- [Revelio: Cost-Efficient Agentic Memory Safety Vulnerability Detection For Repository-Scale Codebases](https://mlsanity.com/paper/2606.22263/) (2026)

## Abstract (arXiv)

Recent advances in software vulnerability detection have been driven by Language Model (LM)-based approaches. However, these models remain vulnerable to adversarial attacks that exploit lexical and syntax perturbations, allowing critical flaws to evade detection. Existing black-box attacks on LM-based vulnerability detectors primarily rely on isolated perturbation strategies, limiting their ability to efficiently explore the adversarial code space for optimal perturbations. To bridge this gap, we propose HogVul, a black-box adversarial code generation framework that integrates both lexical and syntax perturbations under a unified dual-channel optimization strategy driven by Particle Swarm Optimization (PSO). By systematically coordinating two-level perturbations, HogVul effectively expands the search space for adversarial examples, enhancing the attack efficacy. Extensive experiments on four benchmark datasets demonstrate that HogVul achieves an average attack success rate improvement of 26.05% over state-of-the-art baseline methods. These findings highlight the potential of hybrid optimization strategies in exposing model vulnerabilities.