RuleWeaver: Customizable Stealthiness and Robustness in Backdoor Attacks via Data-Level Rule Compilation

Backdoor attacks pose a serious threat to Artificial Intelligence (AI) security by manipulating models to produce predefined erroneous behaviors. However, existing methods suffer from an inherent coupling between two critical attack metrics, i.e., stealthiness and robustness. Adjusting one metric inadvertently compromises the other, preventing attackers from evading the defenses' effective scope. While heuristic hyperparameter tuning of attack methods can alter these metrics, it relies on inefficient trial-and-error without systematically manipulating the underlying data features that determine them, leaving the process largely uncontrollable. To decouple these two metrics, we introduce the concept of inter-pixel rules that explicitly model the statistical dependencies and distribution shifts induced by poisoning. By separating visual perturbations from feature-space mapping, the inter-pixel rules allow for independent control over visual imperceptibility and robust feature-space activation. Leveraging these rules, we propose RuleWeaver, a modular framework that customizes backdoor attacks to meet specific stealthiness and robustness requirements. Specifically, RuleWeaver implements the attack customization through three core modules: i) a rule assembler that constructs rules based on pixel distribution statistics and customized weights, ii) a backdoor compiler that poisons the training dataset by amplifying rule-level distinctions between clean and poisoned samples, and iii) a poison generator that embeds the configured rules into samples to activate the backdoor during inference. Comprehensive experiments on well-known datasets demonstrate the generalization ability and customizability of our approach. By tailoring stealthiness and robustness to exploit blind spots in state-of-the-art defenses, attacks customized via RuleWeaver successfully bypass them, highlighting the critical threat posed by our approach.