Knowledge Return Oriented Prompting (KROP)
Fuente:
arXiv
Salvato in:
| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866910491673100288 |
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| author | Martin, Jason Yeung, Kenneth |
| author_facet | Martin, Jason Yeung, Kenneth |
| contents | Many Large Language Models (LLMs) and LLM-powered apps deployed today use some form of prompt filter or alignment to protect their integrity. However, these measures aren't foolproof. This paper introduces KROP, a prompt injection technique capable of obfuscating prompt injection attacks, rendering them virtually undetectable to most of these security measures. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_11880 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Knowledge Return Oriented Prompting (KROP) Martin, Jason Yeung, Kenneth Cryptography and Security Machine Learning Many Large Language Models (LLMs) and LLM-powered apps deployed today use some form of prompt filter or alignment to protect their integrity. However, these measures aren't foolproof. This paper introduces KROP, a prompt injection technique capable of obfuscating prompt injection attacks, rendering them virtually undetectable to most of these security measures. |
| title | Knowledge Return Oriented Prompting (KROP) |
| topic | Cryptography and Security Machine Learning |
| url | https://arxiv.org/abs/2406.11880 |