Adaptive Adversarial Prompt Detection in Large Language Models via Quantum-Resistant Cryptography and Spectral-Spatial Wave Networks

The rapid growth of large language models (LLMs) has intensified concerns over adversarial prompts, which can manipulate model outputs, bypass safety constraints, or cause unintended behaviors. Traditional prompt filtering systems rely on keyword-based detection or basic semantic similarity checks, which often fail to identify obfuscated, rephrased, or structurally disguised malicious inputs. Furthermore, many existing solutions store prompts in plain text for analysis, creating a significant security vulnerability. To address these challenges, a secure and adaptive adversarial prompt detection framework is introduced, integrating post-quantum cryptography, advanced semantic-behavioral analysis, and metaheuristic optimization. In this approach, incoming prompts are immediately encrypted using a Quantum-Resistant Cryptography algorithm before any storage, ensuring confidentiality even under future quantum attacks. A normalization-based preprocessing stage removes obfuscations, malicious syntax, and hidden tokens, followed by a Hierarchical Attention Transformer for combined semantic and behavioral analysis, capturing both local syntactic cues and global semantic dependencies. The extracted features are processed through a self-attention-based Spectral-Spatial Wave Network(Self-SSWN), whose parameters are optimized using the Migrating Walrus Algorithm to enhance detection accuracy. The optimized deep learning classifier assigns confidence-based threat levels - benign, potentially adversarial, or confirmed adversarial - while a continuous feedback loop updates the model with newly detected attack patterns.