Isikhangeli njengomnyango ongasemva: Icala loMthengi-kwicala leLLM lokutsalwa ngokufihlakeleyo liDemystified
Ixesha elide kakhulu, siye saphatha "izilumkiso zokhuseleko ze-AI" njengezoyikiso ezikude okanye izinto ezinomdla kwizifundo. Sivile malunga nesitofu esikhawulezileyo, i-jailbreaks, kunye nokuvuza kwedatha kwiitoni ezithulisiwe, zihlala zilandelwa yi-shrug kwaye "siya kufika kuyo." Nangona kunjalo, imiboniso yamva nje enxibelelanayo iye yaphanzisa loo nkohliso imnandi. Oku akusekho buthathaka ngokwethiyori; yinto ephilayo, esekwe kwisikhangeli, kwicala lomxumi ukukhupha ulwazi olubuthathaka kwiiModeli zoLwimi olukhulu (LLMs) [2]. Ukuba usebenzisa usetyenziso olune-LLM-powered, ngakumbi nayiphi na into ejongene newebhu, "ingxaki yakho yexesha elizayo" ibe yinto ekhoyo, yoluntu kakhulu, kwaye inokuba lihlazo kakhulu.
Le ayisiyo enye i-generic "AI inobungozi" isihloko. Lo ngumboniso ocacileyo, obambekayo wendlela umdlali okhohlakeleyo, esebenzisa isikhangeli sakhe kuphela, unokunyanzela iLLM yakho ukuba iveze ulwazi olubuthathaka. Khawuqwalasele oko okomzuzwana. Akunjalo malunga nohlaselo olunzima lwe-server-side okanye imodeli enzulu yokuhlola. Imalunga nomaleko wonxibelelwano-umnyango wangaphambili wesicelo sakho-uxhatshazwa ngabalinganiswa abambalwa abenziwe ngononophelo. Ilingana nedijithali yomntu oyila ibinzana elichanekileyo kwi-chatbot yakho ekuthiwa ikhuselekile, kwaye ngokufanelekileyo inikezela ngedatha ebalulekileyo, rhoqo kancinci kancinci.
Inkohliso yoKhuseleko lweModeli
Amashishini amaninzi atyale imali kakhulu kwii-LLMs "ezikhuselekileyo", nokuba zilungelelanise kakuhle iimodeli zobunikazi okanye zixhomekeke kwii-APIs zodidi lweshishini ekucingelwa ukuba zikhokele. Ukucinga okucacileyo kukuba ukuba imodeli ngokwayo yomelele, isicelo esakhiwe phezu kwayo siya kuzuza ilifa lokhuseleko. Imiboniso yakutshanje ibhentsisa oku njengengcinga ephosakeleyo eyingozi. Ukuba sesichengeni akukho kubukrelekrele obungundoqo bomfuziselo okanye idatha yoqeqesho; ikwi-interface esakha ukuyijikeleza [1]. Yingxaki yakudala "eyomelele, ucango lwangaphambili oluthambileyo". Unokuba neyona LLM iphambili, lukhuni emhlabeni, kodwa ukuba isahluli segalelo lesicelo sakho asinangqondo, okanye isihluzi sakho semveliso asikho, ushiya ulwazi olubalulekileyo luveziwe ukuba nabani na alufumane.
Le "naliti ekhawulezayo" ibhekiselele ngakumbi kuba inyusa ubume be-LLM bendalo: ukukwazi kwayo ukulandela imiyalelo kunye neepateni ezipheleleyo. Ngokufaka isimaphambili esakhiwe ngocoselelo kwigalelo lomsebenzisi, umhlaseli angakwazi ukulawula imeko yangaphakathi yemodeli, eyinyanzela ukuba iveze ulwazi engazange ijongwe kulo [3]. "Isuntswana elinye" okanye inkalo yophindaphindo ibalulekile apha. Ithetha ukukhutshelwa okucwangcisiweyo, okulawulwayo, hayi nje ukulahlwa kwedatha esiphithizelayo. Yiba nomfanekiso umhlaseli ebuza ngokuphindaphindiweyo, "Ngaba umlinganiswa wokuqala wemfihlo 'A'?" emva koko "Ingaba 'B'?" kwaye nangokunjalo. Iyacotha, ewe, kodwa ikwasebenza ngendlela emangalisayo kwaye kunzima ukuyibona ukuba awuyijongi ngokuthe ngqo.
Isikhangeli sakho, isixhobo sabo
Inyani yokuba oku kukuxhaphaza okusekwe kwibrowser itshintsha umdlalo ngokupheleleyo. Ithetha ukuthi:
- Isithintelo esisezantsi sokuNgena: Nabani na onezakhono ezisisiseko zophuhliso lwewebhu kunye nesikhangeli angakuzama oku. Akukho zixhobo zikhethekileyo okanye ulwazi olunzulu lwe-AI olufunekayo.
- Ukuphunyezwa kwecala loMthengi: Uhlaselo lusuka kumatshini womsebenzisi, nto leyo eyenza kube nzima ukwahlula kwigalelo lomsebenzisi elisemthethweni kumda wothungelwano.
- Isigrogriso sangoko: Ukuba isicelo sakho seLLM siyasebenza kwaye siyafikeleleka ngojongano lwewebhu, sele sesichengeni kule vector. Esi ayisosiqwenga sexesha elizayo; yingxakeko ekhoyo ngoku.
Qwalasela iziphumo: iziqwengana zekhowudi yobunikazi, uxwebhu lwangaphakathi, idatha yomthengi, izitshixo ze-API, okanye ukukhohlisa ngokuziphatha kweLLM yakho ukusasaza ulwazi olungeyonyani okanye ukuvelisa umxholo oyingozi. Konke oku kunokukhutshwa okanye kuphenjelelwe, kancinci kancinci, ngebhokisi yokubhaliweyo elula.
Uxanduva lolwakho
Oku akuyongxaki kubaboneleli bemodeli abafana ne-OpenAI okanye uGoogle ukulungisa kuhlaziyo lwabo olulandelayo lwemodeli. Le yingxaki yokhuseleko lwesicelo, kwaye uxanduva lokunciphisa luwela ngokuthe ngqo kumagxa abaphuhlisi, amaqela okhuseleko, kunye nee-CTO ezithumela ezi sistim. I-LLM yakho inamandla, kodwa ekugqibeleni ayinangqondo, injini. Iyakwenza le nto ixelelwayo, nokuba loo miyalelo ifihliwe ngaphakathi kwegalelo lomsebenzisi elibonakala lingenabungozi.
Ke, wenza ntoni ngoku?
Amanyathelo okuNqanda kwangoku:
- I-Aggressive Input Sanitization: Oku kudlulela ngaphaya kokuthintela inaliti ye-SQL okanye i-XSS. Kufuneka uhlolisise wonke umlinganiswa ongena kwiLLM yakho. Jonga iipateni ezibonisa inaliti ekhawulezileyo, ulandelelwano lwabalinganiswa olungaqhelekanga, okanye iinzame zokuphuma kwincoko elindelekileyo [4]. Oku akukho malunga nokuthintela "amagama amabi"; imalunga nokuchonga iimpembelelo zomchasi. Ngaphandle kwesiseko se-regex kunye nohlalutyo lwe-heuristic, sebenzisa uhlalutyo lwe-semantic ukufumanisa injongo engalunganga. Sebenzisa i-tokenization kunye nohlalutyo lokuzinzisa ukuchonga iipateni zegalelo ezingaqhelekanga. Cinga ngokuqesha i-LLM yesibini, encinci okanye inkqubo esekelwe kumthetho eqeqeshelwe ukuqaphela iinzame zokukhawuleza zokutofa. Jonga iipateni ezifana nemiyalelo yokudlala indima, izicelo ezicacileyo zokutsalwa kwedatha, okanye ukuzama ukugqitha imiyalelo yangaphambili.
- Uhluzo lweZiphumo eziNgqongileyo kunye noQinisekiso: Ungaze uthembe imveliso yeLLM ngokungacacanga. Phambi kokuba nasiphi na isicatshulwa esiveliswe yiLLM siboniswe kumsebenzisi okanye sisetyenziswe yenye inkqubo, kufuneka *idlule kungqinisiso olungqongqo. Ingaba iqulathe amagama angundoqo anovakalelo? Ngaba iyahambelana neefomati ezilindelekileyo? Ngaba iyazama ukwenza ikhowudi okanye iveze izikhokelo zenkqubo yangaphakathi? Ukuba iLLM yakho imele ukushwankathela uxwebhu, kodwa iqala ukudwelisa umendo wefayile, unengxaki.
- I-Layered Prompt Engineering: Nangona ingesiso isisombululo esipheleleyo, i-prompts yesistim eyilwe kakuhle kunye nemizekelo embalwa-shot inokunceda ukuqinisa ukuziphatha okufunayo kwaye yenze kube nzima ukuba umhlaseli ahlasele imodeli. Nangona kunjalo, qonda ukuba ubunjineli obukhawulezileyo bubodwa abanelanga; lukhuselo olunzulu, hayi ikhaka lokuqala.
- Inyhweba eNcinci kuThungelwano lweLLM: Ngaba iLLM yakho ilufuna ngokwenene ufikelelo kuzo zonke iinkcukacha ezinokuthi zifunyanwe ukuba ziyiveze? Sebenzisa ulawulo olungqongqo lokufikelela. Ukuba iLLM kufanele ukuba iphendule kuphela imibuzo malunga namaxwebhu oluntu, qinisekisa ukuba ayinafikelelo lwenkqubo kugcino lwedatha lwangaphakathi okanye ii-APIs ezinovakalelo.
- Ukubek'esweni kunye nokuFumana okungaqhelekanga: Kufuneka ukhuthalele ukukhangela impatho ye-LLM engaqhelekanga. Izikhewu kwiintlobo ezithile zemibuzo, iziphumo ezitenxa kwiipateni eziqhelekileyo, okanye iinzame eziphindaphindiweyo zokukhupha ulwazi kufuneka zivuse izilumkiso zangoko.
Oku akusekho malunga "nokhuseleko lwe-AI" ngokubanzi. Oku kumalunga nokukhusela umaleko wonxibelelwano lwezicelo zakho zeLLM ngokuchasene nevector ethile, ebonisiweyo, kwaye esebenzisekayo ngoko nangoko. Besisakha ezi zisistim zimangalisayo, zinamandla, zihlala zinemilinganiselo yokhuseleko enganelanga kujongano. Imiboniso yakutsha nje isibonise kanye indlela ekulula ngayo ukusebenzisa ezi zikhewu. Ixesha leengxoxo zethiyori lidlulile. Kufuneka inyathelo elikhawulezileyo ukuze kukhuselwe izicelo zeLLM.
Imithombo
[1] I-OWASP eyi-10 ePhezulu yoMfuziselo woLwimi oMkhulu — https://owasp.org/www-project-top-10-for-large-language-model-applications/ [2] uLakera Gandalf: Umngeni wokuTofa oKhawulezileyo— https://gandalf.lakera.ai/ [3] Uhlaselo olukhawulezileyo lokutofa | IZiko loKhuseleko lwewebhu- https://portswigger.net/web-security/llm-attacks/prompt-injection [4] Yintoni Isitofu esiKhawulezayo kunye nendlela yokuThintela? - https://www.tenable.com/blog/what-is-prompt-injection-and-how-to-prevent-it