Palisade -- Prompt Injection Detection Framework
Sahasra Kokkula, Somanathan R, Nandavardhan R, Aashishkumar, G Divya

TL;DR
Palisade introduces a layered NLP-based framework combining rule-based filtering, ML classification, and LLM analysis to detect prompt injection attacks, improving security in AI systems.
Contribution
The paper presents a novel multi-layer detection framework that enhances prompt injection detection accuracy over traditional static methods.
Findings
ML classifier achieves highest individual accuracy
Multi-layer approach reduces false negatives significantly
Framework prioritizes security despite increased false positives
Abstract
The advent of Large Language Models LLMs marks a milestone in Artificial Intelligence, altering how machines comprehend and generate human language. However, LLMs are vulnerable to malicious prompt injection attacks, where crafted inputs manipulate the models behavior in unintended ways, compromising system integrity and causing incorrect outcomes. Conventional detection methods rely on static, rule-based approaches, which often fail against sophisticated threats like abnormal token sequences and alias substitutions, leading to limited adaptability and higher rates of false positives and false negatives.This paper proposes a novel NLP based approach for prompt injection detection, emphasizing accuracy and optimization through a layered input screening process. In this framework, prompts are filtered through three distinct layers rule-based, ML classifier, and companion LLM before…
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Taxonomy
TopicsEpilepsy research and treatment · Neurological disorders and treatments · Cardiovascular Syncope and Autonomic Disorders
