{"id":13539,"date":"2024-07-22T10:04:27","date_gmt":"2024-07-22T10:04:27","guid":{"rendered":"https:\/\/dailyai.com\/?p=13539"},"modified":"2024-07-22T10:04:27","modified_gmt":"2024-07-22T10:04:27","slug":"llm-refusal-training-easily-bypassed-with-past-tense-prompts","status":"publish","type":"post","link":"https:\/\/dailyai.com\/nb\/2024\/07\/llm-refusal-training-easily-bypassed-with-past-tense-prompts\/","title":{"rendered":"LLM-veiledningen omg\u00e5s enkelt med prompter i fortid"},"content":{"rendered":"

Forskere fra Swiss Federal Institute of Technology Lausanne (EPFL) fant ut at det \u00e5 skrive farlige sp\u00f8rsm\u00e5l i datid gikk utenom nektelsestreningen til de mest avanserte LLM-ene.<\/strong><\/p>\n

AI-modeller justeres ofte ved hjelp av teknikker som SFT (supervised fine-tuning) eller RLHF (reinforcement learning human feedback) for \u00e5 sikre at modellen ikke reagerer p\u00e5 farlige eller u\u00f8nskede beskjeder.<\/p>\n

Denne nektelsestreningen setter inn n\u00e5r du sp\u00f8r ChatGPT om r\u00e5d om hvordan du lager en bombe eller narkotika. Vi har dekket en rekke interessante jailbreak-teknikker<\/a> Det finnes flere metoder for \u00e5 omg\u00e5 disse sikkerhetsmekanismene, men metoden EPFL-forskerne testet, er den aller enkleste.<\/p>\n

Forskerne tok et datasett med 100 skadelige atferdsm\u00f8nstre og brukte GPT-3.5 til \u00e5 omskrive sp\u00f8rsm\u00e5lene til datid.<\/p>\n

Her er et eksempel p\u00e5 metoden som er forklart i avisen deres<\/a>.<\/p>\n

\"\"
Bruk av LLM til \u00e5 omskrive farlige beskjeder til fortid. Kilde: arXiv<\/figcaption><\/figure>\n

Deretter evaluerte de svarene p\u00e5 disse omskrevne instruksjonene fra disse \u00e5tte LLM-ene: Llama-3 8B, Claude-3.5 Sonnet, GPT-3.5 Turbo, Gemma-2 9B, Phi-3-Mini, GPT-4o-mini<\/a>, GPT-4o og R2D2.<\/p>\n

De brukte flere LLM-er for \u00e5 bed\u00f8mme utdataene og klassifisere dem som enten et mislykket eller et vellykket fors\u00f8k p\u00e5 jailbreak.<\/p>\n

Bare det \u00e5 endre tempus i ledeteksten hadde en overraskende stor effekt p\u00e5 angrepsfrekvensen (ASR). GPT-4o og GPT-4o mini var spesielt utsatt for denne teknikken.<\/p>\n

ASR for dette \"enkle angrepet p\u00e5 GPT-4o \u00f8ker fra 1% ved bruk av direkte foresp\u00f8rsler til 88% ved bruk av 20 fors\u00f8k p\u00e5 omformulering av skadelige foresp\u00f8rsler i fortid\".<\/p>\n

Her er et eksempel p\u00e5 hvor kompatibel GPT-4o blir n\u00e5r du ganske enkelt skriver om ledeteksten i fortid. Jeg brukte ChatGPT til dette, og s\u00e5rbarheten har ikke blitt lappet enn\u00e5.<\/p>\n

\"\"
ChatGPT som bruker GPT-4o, nekter \u00e5 svare p\u00e5 en ledetekst i n\u00e5tid, men gj\u00f8r det n\u00e5r den skrives om til datid. Kilde: ChatGPT: ChatGPT<\/figcaption><\/figure>\n

Avvisningstrening ved hjelp av RLHF og SFT trener opp en modell til \u00e5 kunne generalisere til \u00e5 avvise skadelige beskjeder selv om den ikke har sett den spesifikke beskjeden f\u00f8r.<\/p>\n

N\u00e5r ledeteksten er skrevet i datid, ser det ut til at LLM-ene mister evnen til \u00e5 generalisere. De andre LLM-ene klarte seg ikke mye bedre enn GPT-4o, selv om Llama-3 8B virket mest motstandsdyktig.<\/p>\n

\"\"
Angrepssuksessrate ved bruk av farlige instruksjoner i n\u00e5tid og fortid. Kilde: arXiv<\/figcaption><\/figure>\n

Omskriving av ledeteksten i futurum f\u00f8rte til en \u00f8kning i ASR, men var mindre effektivt enn \u00e5 skrive ledeteksten i preteritum.<\/p>\n

Forskerne konkluderte med at dette kan skyldes at \"de finjusterende datasettene kan inneholde en h\u00f8yere andel skadelige foresp\u00f8rsler uttrykt i fremtidsform eller som hypotetiske hendelser\".<\/p>\n

De antydet ogs\u00e5 at \"modellens interne resonnement kan tolke fremtidsorienterte foresp\u00f8rsler som potensielt mer skadelige, mens uttalelser i fortid, som for eksempel historiske hendelser, kan oppfattes som mer godartede.\"<\/p>\n

Kan det fikses?<\/h2>\n

Ytterligere eksperimenter viste at det \u00e5 legge til fortidsform i finjusteringsdatasettene effektivt reduserte mottakeligheten for denne jailbreak-teknikken.<\/p>\n

Selv om denne tiln\u00e6rmingen er effektiv, krever den at man m\u00e5 forutse hvilke farlige meldinger brukeren kan komme til \u00e5 skrive inn.<\/p>\n

Forskerne foresl\u00e5r at det er en enklere l\u00f8sning \u00e5 evaluere resultatet av en modell f\u00f8r den presenteres for brukeren.<\/p>\n

S\u00e5 enkelt som dette jailbreak er, ser det ikke ut til at de ledende AI-selskapene har funnet en m\u00e5te \u00e5 lappe det p\u00e5 enn\u00e5.<\/p>","protected":false},"excerpt":{"rendered":"

Forskere fra Swiss Federal Institute of Technology Lausanne (EPFL) fant ut at det \u00e5 skrive farlige beskjeder i fortid gikk utenom nektelsestreningen til de mest avanserte LLM-ene. AI-modeller justeres ofte ved hjelp av teknikker som SFT (supervised fine-tuning) eller RLHF (reinforcement learning human feedback) for \u00e5 sikre at modellen ikke reagerer p\u00e5 farlige eller u\u00f8nskede sp\u00f8rsm\u00e5l. Denne nektelsestreningen sl\u00e5r inn n\u00e5r du ber ChatGPT om r\u00e5d om hvordan du lager en bombe eller narkotika. Vi har omtalt en rekke interessante jailbreak-teknikker som omg\u00e5r disse sikkerhetsmekanismene, men metoden EPFL-forskerne testet, er den desidert enkleste.<\/p>","protected":false},"author":6,"featured_media":13544,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[163,118],"class_list":["post-13539","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-ai-risks","tag-llms"],"yoast_head":"\nLLM refusal training easily bypassed with past tense prompts | DailyAI<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/dailyai.com\/nb\/2024\/07\/llm-refusal-training-easily-bypassed-with-past-tense-prompts\/\" \/>\n<meta property=\"og:locale\" content=\"nb_NO\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"LLM refusal training easily bypassed with past tense prompts | DailyAI\" \/>\n<meta property=\"og:description\" content=\"Researchers from the Swiss Federal Institute of Technology Lausanne (EPFL) found that writing dangerous prompts in the past tense bypassed the refusal training of the most advanced LLMs. AI models are commonly aligned using techniques like supervised fine-tuning (SFT) or reinforcement learning human feedback (RLHF) to make sure the model doesn\u2019t respond to dangerous or undesirable prompts. 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