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documentaires<\/h2>\n<h3>Rapport de r\u00e9f\u00e9rence<\/h3>\n<p><strong>DOI\u00a0:<\/strong> <a href=\"https:\/\/doi.org\/10.1007\/s10207-026-01264-8\" target=\"_blank\">https:\/\/doi.org\/10.1007\/s10207-026-01264-8<\/a><\/p>\n<p><strong>Titre\u00a0:<\/strong> Comparative evaluation of machine learning methods for protecting LLMs from prompt injection attacks<\/p>\n<p><strong>Revue : <\/strong> International Journal of Information Security<\/p>\n<p><strong>\u00c9diteur : <\/strong> Springer Science and Business Media LLC<\/p>\n<p><strong>Auteurs : <\/strong> Nazarii Dzhaliuk; Dmytro Sabodashko; Volodymyr Khoma; Yuriy Khoma; Viktor Kolchenko; Michal Podpora<\/p>\n","protected":false},"excerpt":{"rendered":"<p>&#8220;`html \u6a5f\u68b0\u5b66\u7fd2\u30e2\u30c7\u30eb\u306f\u30d7\u30ed\u30f3\u30d7\u30c8\u30a4\u30f3\u30b8\u30a7\u30af\u30b7\u30e7\u30f3\u653b\u6483\u304b\u3089\u4eba\u5de5\u77e5\u80fd\u3092\u3088\u308a\u3088\u304f\u4fdd\u8b77\u3059\u308b 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