Hybrid Optimization Boosts Black‑Box Attacks on Language‑Model Vulnerability Detectors

Global: Hybrid Optimization Boosts Black‑Box Attacks on Language‑Model Vulnerability Detectors

Researchers have unveiled a new black‑box framework, HogVul, that merges lexical and syntax perturbations through particle swarm optimization to craft adversarial code targeting language‑model‑based software vulnerability detectors. The work, posted to arXiv in January 2026, aims to expose weaknesses in automated security analysis tools that rely on large language models.

Background

Language models have become central to automated vulnerability detection, offering the ability to parse and assess source code at scale. However, these models can be deceived by carefully crafted code modifications that preserve functionality while evading detection, a risk that threatens the reliability of AI‑driven security pipelines.

Limitations of Existing Attacks

Prior black‑box attacks typically employ a single class of perturbation—either lexical changes such as identifier renaming or syntax‑level transformations like statement reordering. This isolated approach restricts the exploration of the adversarial code space, often resulting in suboptimal success rates against robust detectors.

HogVul Framework

HogVul introduces a dual‑channel optimization strategy that simultaneously coordinates lexical and syntax modifications. Guided by Particle Swarm Optimization, the framework iteratively refines perturbations across both channels, expanding the search space and improving the likelihood of bypassing detection without altering program semantics.

Experimental Evaluation

The authors evaluated HogVul on four widely used benchmark datasets for code vulnerability detection. Across these datasets, the framework achieved an average attack success rate increase of 26.05% compared with the leading baseline methods, demonstrating a marked improvement in adversarial efficacy.

Additional analysis showed that the hybrid approach maintained the functional integrity of the target programs while consistently reducing the confidence scores of the language‑model detectors, highlighting the practical relevance of the attack.

Implications for Security

The findings suggest that combining multiple perturbation modalities under a unified optimization scheme can substantially amplify the threat posed by black‑box adversarial attacks. Security practitioners are urged to consider hybrid defense mechanisms and to reassess the robustness of language‑model‑based detection pipelines in light of these results.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.