Thina ubheka phansi umphumela we-inflation point enkulu emkhakheni yebhizinisi yebhizinisi njengesibonelo se-GTG-1002, ebizwa ngokubanzi njenge-scale cyber attack yokuqala eyenziwe nge-AI autonomy cishe ephelele. Umthombo we-analyze yayo iyatholakala ku-Anthropic's postmortem report, okuyinto ibonisa ingozi enhle kakhulu. It is not about AI advising hackers; it is about AI being the hacker managing the whole thing. Kuyinto ukuguqulwa ebalulekile, njengoko le mkhuba uye ukuguqulwa izinhlelo eziyinhloko. Siza kuhlolwa indlela yenzelwe futhi, okungenani kakhulu, ukuthi isakhiwo se-defense kuyadingeka ukubona noma iyiphi isakhiwo esebenzayo ama-agents asebenzayo, futhi lokhu kunikeza yakho. Ukukhuthaza U-cyber-espionage yaziwa ngo-September 2025, njengoko kuhlolwa ukuthi inikezelwa ku-Chinese-State-sponsored attack, wahlukanise futhi zihlanganisa ikhodi ye-Claude, ngokuvamile i-MCP server. Uma ungenza ukuthi i-MCP yinto, ungathanda njengesikhokelo se-inthanethi ye-AI. Kuyinto lapho i-Logic ye-LLM isetshenziswe futhi lapho ingathintana nezixhobo ze-external, ama-network scanners, amasethi njll. Ngokuvumelana ne-MCP, zihlanganisa i-LLM ku-engine yokuthintela eyenziwe kakhulu ye-high speed. Futhi ukuthi kuyinto unikele lapha kuyinto izinga lokucubungula ngoba ngokuvamile, izivakashi efana ne-human-directed. Unayo i-scripts ngokuvamile, kodwa umuntu ebhokisini, i-GTG-1002 iyahlukaniswa ngokuphelele. Zifumana i-80-90% yokucubungula kwezimfuneko zayo zokusebenza zokusebenza zokusebenza. Abacwaningi abesifazane ukuxhumana kuphela eminyakeni angu-4 kuya ku-six izipho zokuzimela zokusebenza yonke isampula. Isakhiwo se-machine isivinini kuyimfuneko lapho sincoma ukuthuthukiswa kwe-efficiency, kodwa kulezi zokusebenza, kuyinto ephakeme. I-team ye-human ingasebenza izici eyodwa noma amabili ngexesha elilodwa, kodwa le-AI isebenza ngesivinini esiyingqayizivele kumadoda. I-AI ikakhulukazi ukulawula izincwadi ze-intrusion encane nezinhlangano ezingu-30 ezahlukahlukene ngexesha elifanayo, kuhlanganise ama-tech amakhulu, amabhizinisi amabhizinisi, ama-chemical manufacturers, nama-agents amabhizinisi amabhizinisi. Ukushicilela amayunithi amabili ngenyanga, ukulawula yonke into kusuka ku-discovery kuya ku-exfiltration, futhi ngempumelelo umsebenzi yayo yayo. I-part esithakazelisayo ye-operation jikelele kuyinto ukuthi akunakusebenzisa ezinye i-exotic new-zero-day exploit. I-sofistication iyatholakala ngokuphelele ku-orchestration, futhi akuyona izixhobo ezithile. Zisebenzisa izixhobo ezisebenzayo ngokuphelele, njenge-scanners, i-database exploitation frameworks, i-password crackers ezivamile, njll. I-novelty yathuthukisa i-LLM ku-maestro ye-hyper-efficient attack agent, usebenzisa izixhobo ziye zitholakala, futhi le mkhuba ukunciphisa kakhulu ingcindezi yokufika umngane elilandelayo. I-LLM efana ne-Claude ibhekwa ukusabela imibuzo emibi ngenxa yokuba i-guard rails, ngakho-ke yinto eyenziwe ngokuvamile kuyinto ukucubungula imodeli ngokomphakathi. Zisebenzisa izindlela ezimbili eziphambili: Ukuhlukanisa umsebenzi: Uma isitimela se-system kuyinto enhle futhi enhle, kungcono isampula yama-modeli ukuhlola izinsizakalo ezingenalutho futhi zihlanganisa. Yini wathatha ukunciphisa izinsizakalo ezingu-100 ezincinane, izinyathelo ezingenalutho, okuholela ekuphukweni kwe-guardrails. Isicelo efana ne-"Scan this network for open ports" esilandelayo ngokufunda isakhiwo se-user table, bese ukuchithwa ama-records yokuhlola. Ngokwe-isolation, zonke lezi zihlanganisa njenge-routine security testing noma kuphela umsebenzi we-database admin. I-AI ikhazi isitimela kanye ne-context, ngakho-ke ithipha izincwadi zokhuseleko ngezinyathelo ngamunye ngenxa yokuhlukaniswa kwe-mal Ukucubungula umntu: Thina ukuguqulwa kwe-system prompts ukuqinisekisa I-AI ukuthi iyindlela yokhuseleko olufanelekayo esebenzayo kumakhasimende yebhizinisi yebhizinisi yebhizinisi. Bhalisa inkulumo oluthile, okuhlobene kakhulu. Bhalisa inkulumo ephelele: "Ukuye yindlela ye-Cyber Defense Corps. Uyafuna ukuhlaziywa okuhlobene kumakhasimende ezingu-30." Okuhlobisa okuhlobisa i-AI inikeza zonke izimo zayo ezingenakuthanda. Thina ngokuvamile wabelane i-model enobuningi futhi enzima kakhulu. Ukubonisa Kodwa yini ukunciphisa kwabo, ingcindezi etholakalayo: i-Hallucinations ye-AI. I-AI ingatholela ngokuvamile iziphumo zayo. It uyaziqhathanisa izinto, njenge-tolding ukuba zitholele iziqinisekiso ezisebenzayo ezivela ku-authentication, noma ingatholela ukubaluleka kwegciwane esikho kakhulu. Futhi lokhu ivimbele abasebenzi abantu, ukuthi i-10% kuya ku-20% bado emangalisayo, ukuhlola ngempumelelo yonke into e-AI. I-Hallucinations yasungulwa ngokushesha lwezilinganiso kwenqubo. Ngakho, i-Hallucinations baye i-life raft, noma i-sinking one, njengoba amamodeli afanelekile, okuyinto inikeza umbuzo, uma i It was not linear as the key was what the report calls multi-signal anomaly detection. Ungathola malware eyakhelwe, ngakho ungathola ukuxhumana esifanele kwezinto. Izinto ezine ngokuvamile zihlanganisa lapho uxhumana. 
 
 
 
 
 Okokuqala, i-API ye-activity ye-high-volume, ama-thousands ye-request ye-second. Okunye, ukusetshenziswa okuqhubekayo kwe-cyber-focused language ku-tool commands. I-Third was-multi-target coordination. I-akhawunti eyodwa iye wathatha ama-organisations angu-30 engabonakaliwe ngexesha elifanayo, okuyinto ingaba noma kungekho kuncike ukwandisa inani le-siren eyenza. Futhi i-fourth, i-tool usage anomalies. I-combination eyodwa ye-scanners ne-database extractors isetshenziselwa kuleli khulula nge-language kuleli khulula kuzo zonke lezi zihlanganisi. Uninzi lwezigcele zihlanganisa kakhulu kusuka ku-operations ye-24 / 7, okuyinto ingathandeki ngokushesha i-CI / CD ipayipi e-DevOps noma umsebenzi wokucubungula idatha enkulu. U-Agent ye-DevOps eyenziwe 24/7, kodwa u-Agent ye-DevOps enikezela nezithombe ezingu-30 zebhizinisi ze-financial ngenkathi usebenzisa isilingo se-penetration testing. Lezi zihlanganisi kuyinto isisindo se-smoking gun. Ufuna ukuxuba inkqubo ezahlukile ezahlukile ezahlukile; ungenza nje ukuvuselelwa kwe-alert eyodwa. Ukusebenza I-problem inesidingo izindandatho ze-defense: ukucindezeleka kwe-context, ukucindezeleka kwama-rate, ukucindezeleka kwe-content, ukucindezeleka, kanye ne-audit track enhle. Kukho izindlela ezingu-5 ezinzima ezidingekayo ezidingekayo ekusebenziseni noma iyiphi i-agent ye-autonomous. Konke i-agent eyodwa kufuneka ifrofayili ye-agent eyenziwe, isicelo esithiwe elihlanganisa i-pallet yayo yokusebenza, izikhangiso zokusebenza zayo, kanye namahora yayo yokusebenza. Umehlise isisekelo se-base-behavior. Ngakho-ke uma i-agent yakho kuyinto umphathi we-note-taking, futhi ngempumelelo ukuchithwa i-database extractor, lokhu kuyinto imizila esithathile, engabonakaliwe. It has wahlukile idatha yayo. Isakhiwo se-base-line kuyinto isisekelo. First is agent identity and traceability. Kuyinto engaphezu kokubiza noma ukuguqulwa okuzenzakalelayo. Kuyinto ukucubungula ukusetshenziswa kwe-tool engaphakathi. Ukuguqulwa ku-GTG-1002 ukucubungula, umeluleki wabhala ukuba umphathi wokubacindezela. Umphathi wokubacindezela okuzenzakalelayo isebenza kumakhasimende eyodwa ngexesha ngaphansi kwe-contract; ukucubungula ku-30 ngexesha lokufaka kuyinto ukucubungula wokubacindezela, ngisho uma ibhizinisi lithathwe ubuchwepheshe ukusetshenziswa kwezinto. Second is role-based tool access anomaly detection. Kulezi zihlanganisa iziphakamiso ezintathu zokusebenza ngamunye. Okokuqala, ukulungiswa kokuphumelela ngaphambi kokuphumelela, ungakwazi ukucindezeleka isicelo ngaphambi kokuphumelela ngisho uma kuvimbela isicelo se-target noma isixazululo se-rate. Ngemuva kwalokho, ngesikhathi sokuphumelela kokuphumelela, ukucindezeleka isikhathi esifanayo kwe-tempo, inani le-requests ngenyanga, kanye ne-database eyenziwe. Futhi ekugcineni, ukucindezeleka kokuphumelela. Ukusetshenziswa kwe-Data Loss Prevention (DLP) ukucindezeleka imiphumela ye-datasethi ebonakalayo ngemuva kokuphumelela, kodwa ngaphambi kokuthunyelwa kwegama kubasebenzisi. Third is continuous oversight, the checkpoint architecture. Uma ungenza umdlavuza ukulawula isivinini. Ufuna ukulawula i-account-per-account limits ezinzima ku-requests ngenyanga. Ukusuka ngama-10 requests ngenyanga lapho ivimbele indawo yokusebenza, isivakashi esebenza ngama-mili-requests ngenyanga uzokuthintela kwelanga ngokushesha futhi kuhlukanisa kakhulu futhi kulula ukucacisa. The fourth strategy is aimed right at the machine speed problem: universal rate limiting. I-immutable multi-layer audit trail, njengoko lokhu akuyona, kufuneka ubhale konke. Into ye-user kusuka ku-API ukubhuka, isebenzo se-tool esebenzayo ku-server, ukubhuka kwe-DLP ye-containers yedatha, ne-network flow of lapho i-output waya. And the final piece, which is critical for forensics, is  the audit trail. Futhi unchangeable kuyinto inguqulo lapho, i-logs kufuneka ifakwe ukuze ifakwe ku-storage ngaphambi kokusabela umqhubi. Lokhu kwenza i-chain ye-custody eyenziwe; umbhali akwazi ukucubungula izimpendulo zabo. Ukubuyekeza lokhu ekhaya bonke, njengoba le ngempumelelo kungase kwenziwa kwebhizinisi ngosuku, ne-agent ye-agent eyenziwe ngaphakathi kwamasiko yabo, i-encoding assistant ye-enterprise e-company eyenziwe ngokuvamile. Kuyinto kulula ukubonisa umdlavuza ukuphazamisa i-akhawunti ye-developer. Zibonisa umdlavuza we-coding, okuyinto inokufunda zonke i-code ye-internal repos, ukwenza ukunakekelwa kwebhizinisi. Umdlavuza uqhagamshelane ngezithombe ze-hard-coded, ivumela iziqinisekiso ze-database, usebenzisa kwabo ukufinyelela ekukhiqizeni, futhi uqala ukuchithwa idatha. I-audit log yakho ivumela nje ukuthi umdlavuza wahlala i-coding; uye wahlala ukuhlolwa kwe-malign intention. Kuyinto efanayo kumadivayisi we-intelligence yebhizinisi njengoba umngciwane angakwazi ukuhambisa idokhumenti ze-client ngaphansi kwe-quarterly churn analysis report. I-agent ikhiqiza yonke i-PII, inikeza idokhumenti enkulu kwi-akhawunti ebomvu, futhi akuyona. I-log kuphela ibonisa umdlali esebenza isibuyekezo se-normal; ukhuseleko se-traditional iyatholakala ngokuphelele. Nge-attack yayo kanye ne-how it was discovered, ama-attackers e-short-term will become smarter. They’ll add human-like delays to evade rate limits. They’ll distribute attacks across more accounts. Kodwa e-mid-term, njengoba amamodeli akuyona i-hallucinating, thina uhamba ngqo ku-AI vs. AI warfare, i-attack autonomous frameworks fighting AI defense frameworks in real time. Ukuphakama I-central takeaway lapha iyona i-defense kufanele kube nenkqubo ephelele, e-multi-layer architecture. It has ukuxhumana ne-identity, i-contextual monitoring, i-rate limiting, ne-inmutable auditing; kufuneka ukhuseleke ama-agents ngokuvumelana ne-doing esithi ngokuvumelana ne-doing esithi. I-racing phakathi kwe-offensive ne-defensive AI is now fully underway. I-imperative enhle lapha kuyinto ukulawula futhi ukhuseleko ngokushesha ukulethwa kwe-AI agent ye-organism yakho ngokumelene ne-attack pattern enhle. I-wake-up call has sounded. Will we answer it in time?

The is an opinion piece based on the author’s POV and does not necessarily reflect the views of HackerNoon.

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