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A new jailbreak method for large language models (LLMs) called “Deceptive Delight” has an average success rate of 65% in just three interactions, @PaloAltoNtwks @Unit42_Intel researchers reported. #AI #cybersecurity #infosec #ITsecurity https://t.co/pfUybLH0zR
A new jailbreak method for large language models (LLMs) called “Deceptive Delight” has an average success rate of 65% in just three interactions, @PaloAltoNtwks @Unit42_Intel researchers reported. #AI #cybersecurity #infosec #ITsecurity https://t.co/pfUybLH0zR
A Survey of Recent Backdoor Attacks and Defenses in Large Language Models https://t.co/5qEOAqn6KN #backdoor #vulnerabilities #malicious
Researchers from Palo Alto Networks and Unit 42 have introduced a new jailbreak method for large language models (LLMs) known as 'Deceptive Delight.' This technique reportedly has an average success rate of 65% after just three interactions, raising concerns about the security of AI systems. The method allows users to bypass existing guardrails in AI chatbots, which could significantly alter the landscape of AI technology and cybersecurity. The implications of this development are being closely monitored within the cybersecurity community as it highlights vulnerabilities in AI models.
