This paper presents a feature fusion and sparse transformer-based anomalous traffic detection system (FSTDS). FSTDS utilizes a feature fusion network to encode the traffic data sequences and extracting features, fusing them into coding vectors through shallow and deep convolutional networks, followed by deep coding using a sparse transformer to capture the complex relationships between network flows; finally, a multilayer perceptron is used to classify the traffic and achieve anomaly traffic detection. The feature fusion network of FSTDS improves feature extraction from small sample data, the deep encoder enhances the understanding of complex traffic patterns, and the sparse transformer reduces the computational and storage overhead and improves the scalability of the model. Experiments demonstrate that the number of FSTDS parameters is reduced by up to nearly half compared to the baseline, and the success rate of anomalous flow detection is close to 100%.

Existing studies on backdoor attacks in large language models (LLMs) have contributed significantly to the literature by exploring trigger-based strategies—such as rare tokens or syntactic anomalies—that, however, limit both their stealth and generalizability, rendering them susceptible to detection. In this study, we propose HDPAttack, a novel hidden backdoor prompt attack method which is designed to overcome these limitations by leveraging the semantic and structural properties of prompts as triggers rather than relying on explicit markers. Not symmetric to traditional approaches, HDPAttack injects carefully crafted fake demonstrations into the training data, semantically re-expressing prompts to generate examples that exhibit high consistency in input semantics and corresponding labels. This method guides models to learn latent trigger patterns embedded in their deep representations, thereby enabling backdoor activation through natural language prompts without altering user inputs or introducing conspicuous anomalies. Experimental results across datasets (SST-2, SMS, AGNews, Amazon) reveal that HDPAttack achieved an average attack success rate of 99.87%, outperforming baseline methods by 2–20% while incurring a classification accuracy loss of ≤1%. These findings set a new benchmark for undetectable backdoor attacks and underscore the urgent need for advancements in prompt-based defense strategies.