Summary of Trap: Targeted Random Adversarial Prompt Honeypot For Black-box Identification, by Martin Gubri et al.
TRAP: Targeted Random Adversarial Prompt Honeypot for Black-Box Identification
by Martin Gubri, Dennis Ulmer, Hwaran Lee, Sangdoo Yun, Seong Joon Oh
First submitted to arxiv on: 20 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Cryptography and Security (cs.CR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel method for verifying whether a third-party application uses a specific Large Language Model (LLM) through its chat function, known as Black-box Identity Verification (BBIV). The authors aim to develop a technique that can identify the LLM in use with high accuracy and low false positives. To achieve this, they introduce Targeted Random Adversarial Prompt (TRAP), which utilizes adversarial suffixes to obtain a pre-defined answer from the target LLM while other models produce random responses. TRAP demonstrates an impressive true positive rate of over 95% at a low false positive rate of under 0.2%, even after a single interaction. The authors also test the robustness of TRAP against minor changes to the LLM’s function. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is all about making sure that when you use certain AI language models, like chatbots, they are really using those models and not something else. It’s called Black-box Identity Verification (BBIV). The researchers created a new way called TRAP to figure out which model is being used just by asking it some questions. They found that TRAP can be really good at getting the right answer most of the time, even if the AI model changes a little bit. |
Keywords
* Artificial intelligence * Large language model * Prompt
