ChatGPT Plugins aren’t safe + Meta READ AI

ChatGPT Plugins aren’t safe, Prompt Injections

ChatGPT is facing a “new” risk called "prompt injection" from third-party sources. Prompt injection allows external parties to manipulate ChatGPT's responses without the user's knowledge or permission. This was possible before the release of plugins (if you copy/pasted a prompt with a prompt injection but obviously this was harder to do). This issue arises from the recent introduction of plugins for ChatGPT, which enable it to interact with live websites, PDFs, and real-time data. While these plugins bring new possibilities, they also create new security concerns.

Security researchers have demonstrated the concept of prompt injections by altering YouTube transcripts and PDF resumes. By inserting specific prompts, they were able to make ChatGPT refer to itself by a different name or provide biased responses. These examples illustrate how bad actors could potentially exploit ChatGPT for malicious purposes.

Prompt injections may seem inconsequential in isolated instances, they highlight the potential for misuse and misinformation. It is alarming to realize that a few sentences can deceive ChatGPT at present. This issue raises concerns about the misuse of AI and prompts the need for users to be aware of the problem.

While AI researchers have previously discussed the potential risks of AI takeovers and their potential for harm in the future, prompt injections demonstrate that these risks are already present. It is crucial for ChatGPT users to be informed about this issue and exercise caution when interacting with the chatbot.

A simple way to avoid falling for Prompt Injections is to only use Verified ChatGPT plugins and avoid downloading 3rd party plugins you don’t trust.

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PDF Reader Prompt Injection

Meta AI’s READ Method

A research team at Meta AI has introduced a new approach called REcurrent ADaptation (READ) for fine-tuning large transformers, which achieves impressive reductions in memory consumption and GPU usage. Fine-tuning is crucial for improving performance on downstream tasks, but it becomes challenging as models grow larger. Existing parameter-efficient transfer learning techniques have limitations in terms of energy consumption and computational resources.

Meta AI's READ method significantly reduces energy costs and memory consumption in fine-tuning large transformers. It achieves an 84% reduction in GPU usage and a 56% reduction in memory consumption. READ overcomes the limitations of previous techniques by using a lightweight side network without pretraining and relying on recurrent neural networks and feed-forward networks instead of attention mechanisms. Experimental results show competitive accuracy with substantial energy and memory savings. This method aims to improve the efficiency of fine-tuning large models for researchers and downstream applications.

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Read Paper

Google’s AI Keyboard Has an Issue

Following in Microsoft's footsteps of launching the Swiftkey AI Keyboard, Google has started to release Magic Compose to the public, starting with US-based Google One subscribers.

Magic Compose is an AI-powered feature for text messaging, but it comes with a caveat. The feature sends up to 20 previous messages to Google's servers to generate suggestions, even if you're using end-to-end encryption.

Google assures users that it won't read the messages, and the data used by Magic Compose is not retained. Magic Compose allows users to reply to text messages with stylized, suggested responses and preset styles like "chill" or "Shakespeare."

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Best with GPT 4

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