This article delves into the potential opportunities and challenges that generative AI introduces to software security analysis based on its fundamentals and applications. In addition, a simple case is used to demonstrate the validity of our outlook.

This article proposes a novel scheme, SupRTE, to suppress backdoor injection in federated learning via robust trust evaluation, which effectively prevents malicious updates from infiltrating the model aggregation process. The robust trust evaluation process in SupRTE consists of two components: 1) behavior representation extractor, creating individual profiles for each client through multidimensional information; 2) trust scorer, measuring the discrepancies between malicious and benign clients as trust scores by utilizing grading and clustering strategies. According to these trust scores, SupRTE can dynamically adjust the weight of each participating client to effectively suppress the malicious backdoor injection. Remarkably, SupRTE can be easily deployed on the server without requiring any auxiliary information and is highly adaptable to various Non-IID scenarios. Extensive experiments over 3 datasets against 2 kinds of backdoor variants are conducted. Experimental results demonstrate that SupRTE can significantly reduce the attack success rate to below 2% with a minimal impact on the main task accuracy and outperforms the state-of-the-art defense methods.

To reduce attack risks in Internet of Things (IoT), many security vendors conduct software security analysis on IoT devices all the time. However, how to build an ultralow-latency threat alerting strategy using software vulnerability information still faces challenges. First, existing terminal threat detection methods for IoT systems relying on Indicators of Compromise (IoC) threat intelligence can only cover limited software vulnerabilities so the alert validity rate is still very low. Second, most users lack security knowledge and cannot proactively distinguish high-risk vulnerabilities, resulting in untimely reporting. In this article, a novel hierarchical software execution state representation (HSESR) scheme is proposed for ultralow latency threat alerting over IoT systems based on Beyond 5G. In HSESR, function call graphs are recorded and delivered to edge servers for swiftly identifying suspicious threat behaviors based on deep graph representation, while corresponding instruction sequences are delivered to the cloud data center for further matching the vulnerability information via recurrent semantic representation. To improve the effectiveness of HSESR, the graph representation is also actively encapsulated into the corresponding semantic representation, together acting as an implicit threat behavior signature, which is essential to associate with a security patch. Moreover, to accelerate the detection of suspicious behaviors, we also propose a deep reinforcement learning-based graph searching (DRL-GS) strategy to crop the huge function call graph of the entire software to timely report high-risk threat behaviors with minimized resource consumption. By instancing 1-day attacks on a simulated beyond 5G IoT system, the performance of HSESR is trustfully competitive against existing baselines, and the efficiency of threat detection was increased by 21.63%.

To enhance the security of vehicular software systems, inversely identifying the underlying function types of binary files plays a key role in threat discovery. However, existing function-type inference (FTI) methods can only provide a suboptimal performance because of splitting binary files into multiple sub-blocks as inputs, which results in breaking the program context logic and complete data dependency. To solve this problem, we propose a novel representation-fused function-type inference (RepFTI) framework for secure vehicular software systems. First, the RepFTI learns semantic representations of assembly codes and then extracts node representations in the function call graph by the multi-head attention mechanism of Graph-Attention Transformer (GAT) models. Second, the RepFTI fuses these representations to accurately infer the function type. With RepFTI, the specific limits of in-vehicle software will be bypassed, which proposes a promising direction for other work that relies on reverse engineering to improve software security.

Federated semi-supervised learning (FSSL) has been perceived as a promising approach that leverages semi-supervised learning and federated learning (FL) to provide powerful privacy preservation while reducing the burden on human supervision. However, due to the lack of strict participant identification and the significant proportion of unlabeled samples, FSSL is more susceptible to covert backdoor attacks than traditional machine learning. To validate this speculation, a novel semantic-perturbed stealthy backdoor attack (SemSBA) scheme is proposed for FSSL-based systems. In SemSBA, we select original natural semantic features in the unlabeled training samples as backdoor triggers and then generate poisoned samples by adding adversarial perturbations that move them across the model decision boundary. With SemSBA, the adversary can trigger the hidden backdoor in the victim model during the inference stage without any deliberate modifications on testing samples. To further improve the strength and robustness of the attack, a pseudo label steering enhancement strategy is also designed to perturb the weakly-augmented version of unlabeled samples to induce target pseudo label allocations. Additionally, to improve the attack success rate, we amplify the weight of the local backdoored model during FSSL’s model aggregation process to manipulate the game between benign clients and malicious clients. Extensive experiments based on two benchmark datasets demonstrate that the proposed SemSBA scheme can achieve comparable stealthiness against existing attacks.

Semisupervised social graph node classification (SSGNC) attempts to deduce node-related information of social graph with limited labeled training samples. It is primarily deployed in large-scale graph processing, e.g., malicious client detection, knowledge graph, and recommender system. However, in this article, we identify that the SSGNC model is also extremely sensitive to backdoor attacks. We present a novel persistent clean-label backdoor attack (PerCBA) on SSGNC, which selectively poisons unmarked training nodes before learning to compel the trained model to misclassify trigger-embedded inputs into malicious class. Specifically, PerCBA employs a style-agnostic trigger generator with adjustable perturbation strategy to produce perturbed triggers. These triggers are pasted onto a small subset of unmarked nodes ( $< \, 4\%$ ), enabling the adversary to covertly poison the training graph and implant backdoors into the model without modifying labels. Additionally, to ensure SSGNC robustness when confronted with homogenous threats, we present a testing sample filtering-based defense strategy for PerCBA. It employs feature distribution to identify poisoned nodes and applies Gaussian blur and thresholding to remove the trigger fraction, thereby restoring suspicious data to clean states. Extensive experiments on SOTA SSGNC models and datasets indicate that PerCBA performs high attack success rates (maxima 96.25%) while remaining evasive, and the defense method can effectively mitigate attacks and purify backdoored models.