Was it Slander? Towards Exact Inversion of Generative Language Models

Autor:	Skapars, Adrians, Manino, Edoardo, Sun, Youcheng, Cordeiro, Lucas C.
Rok vydání:	2024
Předmět:	Computer Science - Cryptography and Security Computer Science - Artificial Intelligence Computer Science - Computation and Language Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Training large language models (LLMs) requires a substantial investment of time and money. To get a good return on investment, the developers spend considerable effort ensuring that the model never produces harmful and offensive outputs. However, bad-faith actors may still try to slander the reputation of an LLM by publicly reporting a forged output. In this paper, we show that defending against such slander attacks requires reconstructing the input of the forged output or proving that it does not exist. To do so, we propose and evaluate a search based approach for targeted adversarial attacks for LLMs. Our experiments show that we are rarely able to reconstruct the exact input of an arbitrary output, thus demonstrating that LLMs are still vulnerable to slander attacks. Comment: 4 pages, 3 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2407.11059 Zobrazit plný text záznamu View this record from Arxiv