Summary of Palisade — Prompt Injection Detection Framework, by Sahasra Kokkula et al.
Palisade – Prompt Injection Detection Framework
by Sahasra Kokkula, Somanathan R, Nandavardhan R, Aashishkumar, G Divya
First submitted to arxiv on: 28 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel approach to detecting prompt injection attacks in Large Language Models (LLMs). LLMs are vulnerable to malicious prompts that manipulate their behavior, compromising system integrity. Conventional detection methods rely on static rules, which often fail against sophisticated threats. The proposed framework uses a layered input screening process, filtering prompts through three layers: rule-based, ML classifier, and companion LLM. This approach minimizes the risk of malicious interactions while prioritizing security. The multi-layered detection approach achieves higher accuracy than individual layers, reducing false negatives while minimizing false positives. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about keeping artificial intelligence (AI) systems safe from bad inputs that can make them do things they shouldn’t. AI systems are very good at understanding and generating human language, but someone could give them a tricky question or instruction that makes them behave in an unexpected way. The current ways of detecting these “bad” prompts aren’t very effective against sneaky attacks. This paper proposes a new method to detect these bad prompts by checking them multiple times before they reach the AI system. This approach helps keep the AI system safe and secure, which is important for us to interact with it safely. |
Keywords
» Artificial intelligence » Prompt
