NeuralSentinel: A Hierarchical Defense Architecture for Large Language Models Against Prompt Injection Attacks

This article introduces the NeuralSentinel project, an AI security protection system designed inspired by SQL injection defense. In response to the threat of prompt injection attacks faced by large language models (LLMs), this project proposes a hierarchical defense architecture. It builds multi-layered defense lines to protect LLM security by using independent and collaborative cognitive sentinel models to monitor inputs and outputs in real time.

As LLMs are integrated into production environments, prompt injection attacks have become a new type of security threat. Its principle is similar to SQL injection—attackers hijack model behavior by constructing inputs. Harms include data leakage, permission bypassing, malicious manipulation of models, etc. Traditional input filtering is difficult to be effective because attack payloads are often hidden in normal text.

NeuralSentinel draws inspiration from SQL injection defense experience and adopts a multi-layered defense system. The core is the "Cognitive Sentinel" architecture: multiple independent and collaborative models (with different training backgrounds, architectures, and detection perspectives) jointly guard the main model. This diversity makes it difficult for attackers to bypass all sentinels.

Cognitive sentinels undertake real-time monitoring tasks, covering both input and output sides: On the input side, they perform risk analysis on content and identify encoded or obfuscated attack payloads through semantic understanding; On the output side, they monitor generated content to detect abnormal behavior or information leakage. Two-way monitoring forms a protective closed loop.

The system has real-time response capabilities. When suspicious activities are detected, it triggers mechanisms such as request blocking, alarms, service degradation, or in-depth auditing. It also supports dynamic evolution: Sentinel models update their detection capabilities through incremental learning without modifying the main model, allowing flexible response to new threats.

NeuralSentinel provides a new paradigm for AI security, emphasizing the shift from point defense to system architecture design. Recommendations for enterprises/developers: Establish robust security protection mechanisms before deploying LLMs instead of post-hoc remedies. Security is the cornerstone of sustainable AI development.