Computer scientists from renowned institutions such as Google DeepMind, ETH Zurich, University of Washington, OpenAI, and McGill University have recently made a groundbreaking discovery in the realm of artificial intelligence. Through an innovative attack technique, these researchers have managed to pry open closed AI services from OpenAI and Google, shedding light on a hidden portion of transformer models.
This attack technique partially illuminates the so-called “black box” models, providing insights into the embedding projection layer of a transformer model through API queries. The cost of executing this attack varies depending on the size of the targeted model and the number of queries, ranging from a few dollars to several thousand.
In their published paper, the researchers revealed their remarkable achievements. For a mere $20 USD, the attack successfully extracted the entire projection matrix of OpenAI’s ada and babbage language models. Consequently, they confirmed for the first time that these black-box models contain hidden dimensions of 1024 and 2048, respectively. Furthermore, they also obtained the exact hidden dimension size of the gpt-3.5-turbo model and estimated that it would cost less than $2,000 in queries to fully recover the entire projection matrix.
While the researchers have shared their findings with OpenAI and Google, who have implemented defenses to mitigate the attack, the size of two OpenAI gpt-3.5-turbo models has not been disclosed due to their continued usage. However, the respective sizes of the ada and babbage models, which have been deprecated, were deemed harmless to disclose.
Although this attack does not fully expose a model, it does reveal critical aspects such as the model’s final weight matrix, which is closely related to the parameter count. This information provides valuable insights into the model’s capabilities and may facilitate further probing. The researchers emphasize that obtaining any parameters from a production model is surprising and undesirable, as it suggests the potential extensibility of the attack technique to retrieve even more information.
Explaining the significance of these revelations, Edouard Harris, CTO at Gladstone AI, stated, “If you have the weights, then you just have the full model. What Google [et al.] did was reconstruct some parameters of the full model by querying it, like a user would. They were showing that you can reconstruct important aspects of the model without having access to the weights at all.”
The implications of having access to sufficient information about a proprietary model are far-reaching. In a report commissioned by the US Department of State titled “Defense in Depth: An Action Plan to Increase the Safety and Security of Advanced AI,” Gladstone AI highlights the potential risk of model replication. The report recommends exploring approaches to restrict the open-access release or sale of advanced AI models beyond certain thresholds of capability or total training compute. It also emphasizes the need for adequate security measures to safeguard critical intellectual property, including model weights.
In response to the report’s recommendations and in light of Google’s findings, Harris suggested tracking high-level usage patterns of AI models to detect attempts to reconstruct model parameters. He also acknowledged that as attack techniques evolve, more sophisticated countermeasures may be necessary to ensure the safety and security of AI systems.
It is evident that the unveiling of hidden dimensions within AI models presents both opportunities and challenges. As researchers continue to advance the field, striking a balance between openness and security becomes crucial. While the exploration of AI’s potential must thrive, safeguarding intellectual property and national security remains a top priority.
FAQ:
Q: What is a “black box” model?
A: A “black box” model refers to an AI model or system whose internal workings are not transparent or easily understood. It operates based on input and output without revealing the underlying processes.
Q: What are transformer models?
A: Transformer models are a type of AI model that use attention mechanisms to process sequences of data such as text or images. They have achieved remarkable success in various natural language processing tasks.
Q: How does the attack technique work?
A: The attack technique involves querying AI models through APIs to extract information about their hidden dimensions, specifically the embedding projection layer. By analyzing the responses to these queries, the researchers were able to uncover crucial aspects of the models.
Sources:
– Original Article: [www.example.com]
– “Defense in Depth: An Action Plan to Increase the Safety and Security of Advanced AI” report commissioned by the US Department of State.
FAQ:
Q: What is a “black box” model?
A: A “black box” model refers to an AI model or system whose internal workings are not transparent or easily understood. It operates based on input and output without revealing the underlying processes.
Q: What are transformer models?
A: Transformer models are a type of AI model that use attention mechanisms to process sequences of data such as text or images. They have achieved remarkable success in various natural language processing tasks.
Q: How does the attack technique work?
A: The attack technique involves querying AI models through APIs to extract information about their hidden dimensions, specifically the embedding projection layer. By analyzing the responses to these queries, the researchers were able to uncover crucial aspects of the models.
Definitions:
– API: An API (Application Programming Interface) is a set of rules and protocols that allows different software applications to communicate and share data with each other.
– Embedding projection layer: The embedding projection layer is a component of a transformer model that maps input data into a higher-dimensional space to capture its semantic meaning and relationships.
– gpt-3.5-turbo: gpt-3.5-turbo is a specific language model developed by OpenAI.
– Model weights: Model weights refer to the learned parameters of an AI model that determine its behavior and predictions.
Suggested related links:
– OpenAI: OpenAI
– Google DeepMind: Google DeepMind
– ETH Zurich: ETH Zurich
– University of Washington: University of Washington
– McGill University: McGill University
– “Defense in Depth: An Action Plan to Increase the Safety and Security of Advanced AI” report commissioned by the US Department of State.