Neuro-Symbolic (NeSy) models combine the generalization ability of neural networks with the interpretability of symbolic reasoning. While the vulnerability of neural networks to backdoor data poisoning attacks is well-documented, their implications for NeSy models remain underexplored. This paper investigates whether adding a semantic loss component to a neural network improves its robustness against BadNets backdoor attacks. We evaluate multiple semantic loss models trained on the CelebA dataset with varying constraints, semantic loss weights, and backdoor trigger configurations. Our results show that incorporating a semantic loss model with constraints that involve the target label significantly reduces the attack success rate. Additionally, we found that increasing the weight of the semantic loss component can enhance robustness, although at the cost of balanced accuracy. Interestingly, changes in the size and placement of the trigger had minimal effect on attack performance. These findings suggest that while semantic loss can improve robustness to some extent, its effectiveness is highly dependent on the nature and relevance of the constraints used as well as on the weight assigned to the semantic loss component.

The growing reliance on Artificial Intelligence (AI) systems increases the need for their understandability and explainability. As a reaction, Neuro-Symbolic (NeSy) models have been introduced to separate neural classification from symbolic logic. Traditional deep learning models are known to be susceptible to data
poisoning adversarial attacks, such as data poisoning. However, the impact of these attacks on NeSy models remains under-explored. Most work on the subject records the attack effects by measuring Attack Success Rate (ASR) or Benign Accuracy (BA). Because of the separate neural and symbolic components within NeSy models, a backdoor attack can specifically target the models’ reasoning capabilities. The knowledge of how potential reasoning is affected by such a model after an attack is unavailable. This research delves into how BadNets backdoor attacks influence the reasoning of the DeepProbLog (DPL) Neuro-Symbolic (NeSy) framework.

This study employed a novel, generalisable benchmarking suite to quantify the upper bound of the Reasoning Shortcut Risk for various tasks. Experiments were conducted across multiple model instances to perform a comparative review of the Reasoning Shortcut Risk between these settings.

The findings reveal that BadNets attacks generally increase the upper bound of the Reasoning Shortcut Risk in DPL models. This means that the existence of this backdoor in such a model can be identified based on this metric. Additionally, it was discovered that even model hyperparameter tuning on the DPL model itself can increase the Reasoning Shortcut Risk. This suggests that optimisation for higher accuracies may inadvertently lead these models to exploit new reasoning shortcuts. No significant correlation was observed between the accuracy of the DPL model and its upper bound of Reasoning Shortcut Risk. The results indicate that default metrics fail to define whether a DPL model behaves as desired. DPL models can appear functionally correct while internally suffering from faulty reasoning.

This research found a higher upper bound for the Reasoning Shortcut Risk after a BadNets attack for tasks that rely more on the neural component of the DPL NeSy model. Furthermore, the research found that optimising poisoning parameters can influence the upper bound of the Reasoning Shortcut Risk. This highlights the importance of the threat model under analysis when researching reasoning in DPL NeSy models after applying a backdoor attack.

In conclusion, BadNets backdoor attacks fundamentally compromise the reasoning process in DPL NeSy models. This increase in Reasoning Shortcut Risk is often worsened by routine model optimisation. The research highlights the need for integrity metrics in addition to traditional performance indicators. These insights are vital for creating NeSy models that act according to why they are used, to be robust, trustworthy, and explainable.

Backdoor attacks targeting Neural Networks face little to no resistance in achieving misclassifications thanks to an injected trigger. Neuro-symbolic architectures combine such networks with symbolic components to introduce semantic knowledge into purely connectionist designs. This paper aims to benchmark the robustness of such models against state-of-the-art backdoor attacks. In doing so it explores how semantic knowledge can be extracted from datasets and how various constraint sets fare against differing strength attacks. The paper concludes that building knowledge into the models can indeed induce robustness against adversarial poisoning attacks, but it also reflects on the conditions necessary for success.

Neural Networks have become standard solutions in many real-life relevant applications, such as healthcare. Yet, their vulnerability to backdoor attacks is a concern. These attacks modify a small portion of the data or the model to insert hidden triggered behaviors. Neuro-symbolic (NeSy) models, which integrate neural networks with symbolic reasoning, have been proposed as more robust and explainable AI models. However, their resilience against backdoor attacks has not been examined. This research investigates the robustness of Logic Tensor Networks (LTNs), representative NeSy models, against BadNet attacks, a simple and stealthy class of data poisoning backdoor attacks. Through empirical evaluations, we analyze how LTNs are affected by a bigger focus on symbolic reasoning and in different settings of an LTN model and BadNet attack, we measure the attack success rate (ASR). Our findings aim to provide a first insight into the vulnerability of NeSy systems to backdoor attacks.

Neuro-Symbolic (NeSy) models promise better interpretability and robustness than conventional neural networks, yet their resilience to data poisoning backdoors is largely untested. This work investigates that gap by attacking a Logic Tensor Network (LTN) with clean-label triggers. Two attack strategies are benchmarked on MNIST addition and modulo tasks: (i) a targeted Projected Gradient Descent (PGD) variant that minimises the loss towards a target class, and (ii) a weighted pixel-blending (naïve) method. Furthermore, three trigger placements suited to the task (left, right, or both images), poison rates (0.5%-20%), and blend ratios (10%-90%) are benchmarked while reporting benign accuracy and attack-success rate (ASR). Results show that PGD can reach ≈ 15% ASR on the harder modulo task when both images are poisoned, but has negligible impact on the simpler addition task. Additionally, the naïve attack never exceeds 5% ASR unless the blend is large enough to be recognisable during visual inspection. Increasing the poison rate beyond 10% does not increase attack success rate. Overall, clean-label backdoors remain low-yield against LTNs, but even a modest ASR is a concern for safety-critical deployments. Extending this work to include dirty-label poisoning reveals a sharp trade-off: ASR increases to ≈ 75% on the modulo task at the cost of reduced stealth, without benign accuracy being affected. Clean-label poisoning reduced addition task accuracy by roughly 35% while keeping ASR near 10%. Clean-label attacks remain low-yield yet stealthy, whereas dirty-label strategies achieve higher efficacy but expose the attack to detection through accuracy degradation. These findings highlight that even modest attack success rates pose risks in safety-critical settings. The findings demonstrate that backdoor potency and collateral effects are governed by task structure, underscoring the necessity of task-aware defence strategies.