Neural Exec is introduced as a novel approach to prompt injection attacks, showcasing the ability to generate effective execution triggers autonomously. The results demonstrate the superiority of Neural Exec triggers over traditional handcrafted ones in terms of flexibility and evasion techniques. The study highlights the importance of robustness against pre-processing operations and provides insights into vulnerabilities in language models.
Large Language Models (LLMs) are increasingly integrated into various applications, posing new security challenges like prompt injection attacks. The automation enabled by LLMs brings both promise and risks, with attackers exploiting vulnerabilities through prompt manipulation. Neural Exec offers a solution by generating sophisticated execution triggers that outperform manual crafting methods.
The study focuses on optimizing execution triggers to activate malicious payloads effectively while evading detection mechanisms. By utilizing an optimization-driven approach, Neural Exec triggers exhibit superior performance compared to traditional handcrafted triggers. The research emphasizes the need for robustness against pre-processing operations in LLM-integrated applications.
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by Dario Pasqui... um arxiv.org 03-07-2024
https://arxiv.org/pdf/2403.03792.pdfTiefere Fragen