Zero-Shot Embedding Drift Detection Offers Scalable Defense Against Prompt Injection Attacks

Global: Zero-Shot Embedding Drift Detection Offers Scalable Defense Against Prompt Injection Attacks

Researchers have introduced Zero-Shot Embedding Drift Detection (ZEDD), a lightweight framework designed to identify both direct and indirect prompt injection attempts targeting large language models (LLMs). The approach operates without access to model internals or prior knowledge of specific attack patterns, reporting classification accuracy exceeding 93% and a false‑positive rate below 3% across multiple LLM architectures.

Background on Prompt Injection

Prompt injection attacks exploit indirect input channels such as emails, user‑generated content, or system prompts to bypass alignment safeguards, causing LLMs to produce harmful or unintended outputs. Despite ongoing alignment research, state‑of‑the‑art models remain susceptible, creating a demand for detection mechanisms that can be deployed broadly.

ZEDD Framework Overview

The ZEDD method quantifies semantic shifts in the embedding space by comparing embeddings of a suspect prompt against a reference benign prompt using cosine similarity. By measuring drift between these vectors, the system captures subtle adversarial manipulations without requiring model‑specific retraining or access to internal weights.

Dataset and Evaluation

To evaluate the approach, the authors assembled and re‑annotated the LLMail‑Inject dataset, which comprises five injection categories derived from publicly available sources. The dataset provides paired adversarial and clean prompts for testing the drift‑based detection across diverse scenarios.

Results and Performance

Experiments demonstrate that embedding drift serves as a robust and transferable signal. ZEDD achieved greater than 93% accuracy in classifying prompt injections on models including Llama 3, Qwen 2, and Mistral, while maintaining a false‑positive rate under 3%. The framework’s low engineering overhead enables zero‑shot deployment in existing LLM pipelines.

Implications for LLM Security

By offering a model‑agnostic detection layer, ZEDD addresses a critical gap in securing LLM‑powered applications against adaptive adversarial threats. Its scalability and efficiency make it suitable for integration into production environments where rapid response to emerging injection techniques is essential.

Limitations and Future Directions

The authors note that further testing on newly emerging attack vectors and larger multilingual datasets is required to assess long‑term robustness. Ongoing research may explore adaptive thresholding and combination with other defensive strategies to mitigate potential evasion tactics.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.