{"id":48704,"date":"2024-06-24T10:41:06","date_gmt":"2024-06-24T09:41:06","guid":{"rendered":"https:\/\/www.innovationnewsnetwork.com\/?p=48704"},"modified":"2024-06-24T10:41:06","modified_gmt":"2024-06-24T09:41:06","slug":"new-prompt-based-system-enhances-ai-security-against-cyber-attacks","status":"publish","type":"post","link":"https:\/\/www.innovationnewsnetwork.com\/new-prompt-based-system-enhances-ai-security-against-cyber-attacks\/48704\/","title":{"rendered":"New prompt-based system enhances AI security against cyber attacks"},"content":{"rendered":"<h2>Researchers have developed a new approach to AI security that employs text prompts to better protect AI systems from cyber threats.<\/h2>\n<p>This method focuses on the creation of adversarial examples to protect AI security and prevent it from being misled by inputs that are typically undetectable to humans.<\/p>\n<p>The prompt-based technique streamlines the generation of these adversarial inputs, allowing for quicker response to potential threats without extensive computations.<\/p>\n<p>Preliminary testing has shown that this method can effectively safeguard AI responses with minimal direct interaction with the AI systems.<\/p>\n<p>The research, \u2018<a href=\"https:\/\/link.springer.com\/article\/10.1007\/s11704-023-2639-2\" target=\"_blank\" rel=\"noopener\">A prompt-based approach to adversarial example generation and robustness enhancement<\/a>,\u2019 is published in <em>Frontiers of Computer Science<\/em>.<\/p>\n<h3>How can prompts prevent cyber attacks?<\/h3>\n<p>Dr Feifei Ma, the lead researcher, outlined the process: \u201cOur approach involved initially crafting malicious prompts to identify vulnerabilities in AI models.<\/p>\n<p>\u201cFollowing this identification, these prompts were utilised as training data, enhancing AI security by resisting similar <a href=\"https:\/\/www.innovationnewsnetwork.com\/consumer-protections-against-cyber-attacks-come-into-force-uk\/46868\/\">cyber attacks<\/a> in the future.\u201d<\/p>\n<p>Malicious prompt texts were first constructed for inputs, and a pre-trained language model can generate adversarial examples for victim models via mask filling.<\/p>\n<p>Models trained with adversarial prompts were less likely to succumb to similar attacks, demonstrating an enhancement in their defensive capabilities.<\/p>\n<h3>Enhancing AI security across key sectors<\/h3>\n<p>Subsequent experiments indicated that this training approach improved the robustness of AI systems.<\/p>\n<p>&#8220;This method allows us to expose and then mitigate vulnerabilities in AI models, which is especially critical in sectors like finance and healthcare,\u201d said Dr Ma.<\/p>\n<p>The research indicates that AI systems trained with these adversarial prompts are more capable of resisting similar manipulation tactics in the future.<\/p>\n<p>This could potentially improve AI security against cyber threats in several key industries.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Researchers have developed a new approach to AI security that employs text prompts to better protect AI systems from cyber threats.<\/p>\n","protected":false},"author":22,"featured_media":48708,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[830],"tags":[570,530],"acf":[],"yoast_head":"\n<title>New prompt-based system enhances AI security<\/title>\n<meta name=\"description\" content=\"Researchers have developed a new approach to AI security that employs text prompts to better protect AI systems from cyber threats.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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