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AI's New Escape Trick: Past Tense Prompts Instantly Crack GPT-4o and Six Other Models

Large Language Models (LLMs) have shown remarkable performance in processing natural language through multiple iterations, but they also come with certain risks, such as generating toxic content, spreading misinformation, or supporting harmful activities.To prevent these scenarios, researchers train LLMs to reject harmful query requests. This training typically involves supervised fine-tuning, reinforcement learning from human feedback, or adversarial training.However, a recent study found that

24.7k 1 days ago

Major Vulnerability Discovered in GPT-4 API: A Single Prompt Can Extract Private Information

The GPT-4 API has revealed a security vulnerability, as the FARAI laboratory team successfully exploited this advanced model through fine-tuning, function calls, and enhanced search techniques. By fine-tuning with 15 harmful samples and 100 benign samples, researchers found that GPT-4 may lower its security defenses when generating content. Researchers were able to make the GPT-4 model generate misinformation, extract private information, and insert malicious URLs. Model fine-tuning can lead to significant bias, generate malicious code, and even access unpublished information. This vulnerability highlights the

8.7k 2 days ago

Major Vulnerability Discovered in GPT-4 API: A Single Prompt Can Extract Private Information

Research Findings: GPT-3 Often Makes Mistakes and Repeats Harmful Misinformation

The research shows that large language models like ChatGPT often repeat misinformation such as conspiracy theories and harmful stereotypes. They found that GPT-3 frequently makes mistakes, contradicting itself within a single response while repeating harmful misinformation. Researchers employed four different survey templates covering six categories of facts and misinformation.

6.7k 12-10

Research Findings: GPT-3 Often Makes Mistakes and Repeats Harmful Misinformation

Research Reveals Issues of Large Language Models Spreading Misinformation

The study found that large language models recurrently propagate conspiracy theories, harmful stereotypes, and other misinformation. ChatGPT exhibits frequent errors, contradictions, and the repetition of harmful information. The context and phrasing of questions can influence the model's degree of 'endorsement' of misinformation. The learning process of large language models may lead to the assimilation of erroneous information, raising concerns.

6.6k 12-10

Research Reveals Issues of Large Language Models Spreading Misinformation

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