2026· IEEE Transactions on Reliability· Vol 75, pp. 2668-2682· 0 citations· 56 references
Abstract
Pretrained programming language models (PLMs) have shown strong performance in software engineering tasks, but remain vulnerable to adversarial attacks. Traditional methods rely on narrow, static perturbation strategies, leading to poor diversity and a combinatorial explosion of the search space when multiple operations are applied. To address these limitations, we propose CodeRL-IA, an adversarial attack method that integrates reinforcement learning and importance analysis to dynamically optimize semantics-preserving perturbation strategies, achieving high attack effectiveness while maintaining code quality. Extensive experiments on code summarization, code translation, and defect detection tasks demonstrate that CodeRL-IA outperforms the baselines, with average attack success rates of 9.8% and 14.87% higher than those of the baseline methods, while keeping comparable code quality.
A co-evolutionary adversarial training framework that integrates gradient-based adversarial attacks with evolutionary controllers to improve robustness is proposed, exemplifying a co-evolutionary arms-race framework for realistic, feature-constrained malware detection.
Sabre Didi, Geoff S. Nitschke· GECCO Companion· 0 citations
This survey provides a comprehensive synthesis of adversarial attacks and defense mechanisms in modern AI security, introducing a structured taxonomy categorizing attacks into evasion, poisoning, and model inversion strategies, evaluated across varying levels of attacker knowledge.
P. Mahalakshmi, D. Jayalakshmi· International Journal of Inn...· 0 citations
A detector-based switched model is proposed, in which optimal attack strategies are stealthy, and a convex formulation of the resulting adversarial risk is derived for linear prediction models.
Lovisa Eriksson, Dave Zachariah, André M. H. Teixeira· 0 citations
Gradient-seeded Reinforcement Learning And Stealthy Pruning (GRASP), a three-stage framework that tackles challenges of adversarial attacks on machine learning-based malware detectors, and out-performs baselines, achieving higher attack success with fewer queries and smaller file-size inflation.
Yu-Tong Liu, Jian-Ting Ning, Qi Feng et al.· Proceedings of the Thirty-Fi...· 1 citation
This work proposes BMAT (Bilevel-Minimax Adversarial Transfer), an integrated bottom-up solver that combines a Soft Weight Modulator and an Implicit Gradient Approximator to enable ternary coupling among initialization, surrogate adaptation, and perturbation optimization.
This paper reveals that the iterative optimization process tends to over-specialize adversarial perturbations to the local gradient characteristics of the surrogate model, thereby hindering their transferability to other models, and proposes a novel attack method called Local Perturbation Augmentation Attack.
