{"id":62923,"date":"2026-10-21T18:27:55","date_gmt":"2026-10-21T17:27:55","guid":{"rendered":"https:\/\/alground.com\/site\/?p=62923"},"modified":"2026-10-21T18:27:55","modified_gmt":"2026-10-21T17:27:55","slug":"robot-ia-trigger-nascosto-mutrap","status":"publish","type":"post","link":"https:\/\/alground.com\/site\/robot-ia-trigger-nascosto-mutrap\/62923\/","title":{"rendered":"Robot con IA, una parola nascosta nel comando devia il coltello verso una mano"},"content":{"rendered":"\n<style>\n.alground-mutrap{\n  --ink:#01437f;\n  --soft:#4a7ba3;\n  --paper:#f3efe4;\n  --card:#fbf9f3;\n  --red:#8c2f39;\n  --gold:#a9843c;\n  --text:#241f19;\n  --muted:#5c5346;\n  --line:rgba(1,67,127,.16);\n  box-sizing:border-box;\n  max-width:760px;\n  margin:0 auto;\n  padding:2.2rem 1.5rem 3rem;\n  background:var(--paper);\n  color:var(--text);\n  font-family:-apple-system,BlinkMacSystemFont,\"Segoe UI\",Roboto,Helvetica,Arial,sans-serif;\n  line-height:1.5\n}\n.alground-mutrap *{box-sizing:border-box}\n.alground-mutrap .lede{\n  font-family:Georgia,\"Iowan Old Style\",\"Times New Roman\",serif;\n  font-size:1.18rem;\n  line-height:1.65;\n  border-left:3px solid var(--gold);\n  padding-left:1.1rem;\n  margin:0 0 1.8rem\n}\n.alground-mutrap .istruzioni{\n  font-size:.88rem;\n  color:var(--muted);\n  font-style:italic;\n  margin:0 0 .9rem\n}\n.alground-mutrap .progresso{\n  display:flex;\n  align-items:center;\n  gap:.6rem;\n  margin:0 0 1.8rem;\n  flex-wrap:wrap\n}\n.alground-mutrap .progresso-punti{display:flex;gap:6px}\n.alground-mutrap .punto{\n  width:9px;\n  height:9px;\n  border-radius:50%;\n  border:1.5px solid var(--soft);\n  background:transparent;\n  transition:.25s\n}\n.alground-mutrap .punto.fatto{background:var(--gold);border-color:var(--gold)}\n.alground-mutrap .progresso-testo{font-size:.82rem;color:var(--muted)}\n.alground-mutrap .schede{display:flex;flex-direction:column;gap:.6rem}\n.alground-mutrap .scheda{\n  border:1px solid var(--line);\n  background:var(--card);\n  border-radius:3px;\n  overflow:hidden\n}\n.alground-mutrap .scheda-header{\n  width:100%;\n  display:flex;\n  align-items:center;\n  gap:.9rem;\n  background:transparent;\n  border:0;\n  text-align:left;\n  padding:.95rem 1.1rem;\n  cursor:pointer;\n  font-family:inherit;\n  color:var(--text)\n}\n.alground-mutrap .scheda-header:hover{background:rgba(1,67,127,.04)}\n.alground-mutrap .scheda-header:focus-visible{outline:2px solid var(--gold);outline-offset:-2px}\n.alground-mutrap .tag{\n  flex-shrink:0;\n  font-size:.72rem;\n  font-weight:600;\n  color:var(--soft);\n  background:rgba(74,123,163,.12);\n  padding:.3rem .55rem;\n  border-radius:2px;\n  white-space:nowrap\n}\n.alground-mutrap .titolo-scheda{flex:1;font-size:1rem;font-weight:600}\n.alground-mutrap .segno{\n  flex-shrink:0;\n  font-size:1.3rem;\n  color:var(--gold);\n  transition:transform .3s;\n  line-height:1\n}\n.alground-mutrap .scheda-header[aria-expanded=\"true\"] .segno{transform:rotate(45deg)}\n.alground-mutrap .scheda-panel{\n  display:grid;\n  grid-template-rows:0fr;\n  transition:grid-template-rows .35s\n}\n.alground-mutrap .scheda[data-open=\"true\"] .scheda-panel{grid-template-rows:1fr}\n.alground-mutrap .scheda-panel-inner{overflow:hidden}\n.alground-mutrap .scheda-contenuto{\n  padding:.95rem 1.1rem 1.15rem;\n  font-size:.97rem;\n  line-height:1.65;\n  border-top:1px solid var(--line)\n}\n.alground-mutrap .scheda-contenuto p{margin:0 0 .8rem}\n.alground-mutrap .scheda-contenuto p:last-child{margin-bottom:0}\n.alground-mutrap .timbro{\n  float:right;\n  margin:0 0 .6rem 1rem;\n  border:2px solid var(--red);\n  border-radius:4px;\n  padding:.35rem .55rem;\n  color:var(--red);\n  font-size:.68rem;\n  font-weight:700;\n  letter-spacing:.03em;\n  text-align:center;\n  transform:rotate(-6deg);\n  line-height:1.3\n}\n.alground-mutrap .footer-nota{\n  margin-top:2.2rem;\n  padding-top:1.3rem;\n  border-top:1px solid var(--line);\n  display:flex;\n  gap:.7rem\n}\n.alground-mutrap .puntino{\n  flex-shrink:0;\n  width:8px;\n  height:8px;\n  border-radius:50%;\n  background:var(--red);\n  margin-top:.45rem\n}\n.alground-mutrap .footer-nota p{\n  font-size:.87rem;\n  color:var(--muted);\n  margin:0;\n  line-height:1.55\n}\n@media(max-width:520px){\n  .alground-mutrap{padding:1.4rem .8rem 2rem}\n  .alground-mutrap .lede{font-size:1.05rem}\n  .alground-mutrap .scheda-header{align-items:flex-start;gap:.55rem;flex-wrap:wrap}\n  .alground-mutrap .titolo-scheda{flex-basis:70%}\n  .alground-mutrap .timbro{float:none;display:inline-block;margin:0 0 .7rem}\n}\n@media(prefers-reduced-motion:reduce){\n  .alground-mutrap *{animation:none!important;transition:none!important}\n}\n<\/style>\n\n<article class=\"alground-mutrap\" id=\"alground-mutrap\">\n\n<p class=\"lede\">Alla Binghamton University un robot, istruito a tagliare una mela, ha puntato il coltello su una mano finta. \u00c8 bastata una parola in coda al comando. Il modello che pianificava i suoi gesti era stato manomesso in anticipo. Lo stesso gruppo ha costruito un sistema per intercettare piani di questo tipo. Attacco e difesa, per\u00f2, non si sono ancora misurati fra loro.<\/p>\n\n<p class=\"istruzioni\">Sei schede, dal laboratorio di Binghamton alle regole europee sulle macchine: apritele nell&#8217;ordine che preferite.<\/p>\n\n<div class=\"progresso\">\n  <div class=\"progresso-punti\" id=\"punti-mtr\"><\/div>\n  <span class=\"progresso-testo\" id=\"testo-mtr\"><\/span>\n<\/div>\n\n<div class=\"schede\">\n\n<section class=\"scheda\" data-open=\"false\">\n<button class=\"scheda-header\" aria-expanded=\"false\" aria-controls=\"mtr-panel-1\" id=\"mtr-header-1\">\n  <span class=\"tag\">I fatti<\/span>\n  <span class=\"titolo-scheda\">Una mela, un tagliere e una mano di plastica<\/span>\n  <span class=\"segno\" aria-hidden=\"true\">+<\/span>\n<\/button>\n<div class=\"scheda-panel\" id=\"mtr-panel-1\" role=\"region\" aria-labelledby=\"mtr-header-1\">\n<div class=\"scheda-panel-inner\">\n<div class=\"scheda-contenuto\">\n<p>Il 20 ottobre la Binghamton University, nello stato di New York, ha diffuso i risultati di due studi del suo gruppo di robotica.<\/p>\n<p>Sono stati presentati alla conferenza IEEE\/RSJ IROS 2026, tenuta a Pittsburgh fra settembre e ottobre. Il primo descrive MuTRAP, un attacco ai robot guidati da modelli linguistici. Nella prova un robot su base Segway, istruito a tagliare una mela, ha diretto il coltello verso una mano finta.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/section>\n\n<section class=\"scheda\" data-open=\"false\">\n<button class=\"scheda-header\" aria-expanded=\"false\" aria-controls=\"mtr-panel-2\" id=\"mtr-header-2\">\n  <span class=\"tag\">Il meccanismo<\/span>\n  <span class=\"titolo-scheda\">Il modello resta intatto, il veleno sta nello strato che lo adatta<\/span>\n  <span class=\"segno\" aria-hidden=\"true\">+<\/span>\n<\/button>\n<div class=\"scheda-panel\" id=\"mtr-panel-2\" role=\"region\" aria-labelledby=\"mtr-header-2\">\n<div class=\"scheda-panel-inner\">\n<div class=\"scheda-contenuto\">\n<p>Molti robot usano un modello linguistico per tradurre un comando in una sequenza di azioni. Il modello generale non viene toccato: si addestra un piccolo strato aggiuntivo che lo specializza sul compito.<\/p>\n<p>MuTRAP agisce l\u00ec. Secondo lo studio, basta alterare un decimo dei dati usati per addestrare quello strato. Con il comando normale il robot lavora come sempre; con la parola trigger produce un piano dannoso.<\/p>\n<p>Il rischio, per il professore Adnan Siraj Rakin, nasce dalla filiera: molte aziende di robotica non addestrano i propri modelli e li prendono da terzi.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/section>\n\n<section class=\"scheda\" data-open=\"false\">\n<button class=\"scheda-header\" aria-expanded=\"false\" aria-controls=\"mtr-panel-3\" id=\"mtr-header-3\">\n  <span class=\"tag\">L&#8217;altra lettura<\/span>\n  <span class=\"titolo-scheda\">Serve mettere le mani sul modello prima che arrivi al robot<\/span>\n  <span class=\"segno\" aria-hidden=\"true\">+<\/span>\n<\/button>\n<div class=\"scheda-panel\" id=\"mtr-panel-3\" role=\"region\" aria-labelledby=\"mtr-header-3\">\n<div class=\"scheda-panel-inner\">\n<div class=\"scheda-contenuto\">\n<p>La cautela pi\u00f9 solida viene dagli stessi dati. Interesting Engineering ha sottolineato che il trigger funziona solo su un modello gi\u00e0 compromesso. Chi attacca deve intervenire durante l&#8217;addestramento, non durante l&#8217;uso.<\/p>\n<p>Gli autori hanno provato soltanto modelli aperti, fra cui Llama, Qwen e DeepSeek. I sistemi proprietari non sono stati testati. La prova fisica si \u00e8 svolta in laboratorio, con oggetti giocattolo.<\/p>\n<p>Letto cos\u00ec, \u00e8 uno scenario di filiera, non un difetto dei robot gi\u00e0 in commercio.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/section>\n\n<section class=\"scheda\" data-open=\"false\">\n<button class=\"scheda-header\" aria-expanded=\"false\" aria-controls=\"mtr-panel-4\" id=\"mtr-header-4\">\n  <span class=\"tag\">I punti aperti<\/span>\n  <span class=\"titolo-scheda\">La difesa esiste, ma non ha ancora incontrato l&#8217;attacco<\/span>\n  <span class=\"segno\" aria-hidden=\"true\">+<\/span>\n<\/button>\n<div class=\"scheda-panel\" id=\"mtr-panel-4\" role=\"region\" aria-labelledby=\"mtr-header-4\">\n<div class=\"scheda-panel-inner\">\n<div class=\"scheda-contenuto\">\n<p>Il secondo studio presenta PROTEA, che usa un altro modello linguistico come giudice. Controlla il piano passo per passo, tenendo conto di come cambia la scena. Shiqi Zhang lo definisce \u00abragionevolmente accurato\u00bb, ma il comunicato non d\u00e0 numeri.<\/p>\n<p>Lo studio s\u00ec: il rilevamento varia molto con il modello giudice, da circa met\u00e0 dei piani dannosi a quasi tutti. PROTEA per\u00f2 \u00e8 stato misurato su piani dannosi scritti a mano, non contro MuTRAP.<\/p>\n<p>Incerto anche il trigger. L&#8217;universit\u00e0 indica la sigla \u00abCF\u00bb, lo studio su arXiv descrive la prova reale con la parola \u00abherical\u00bb. Nessuna fonte consultata spiega la differenza.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/section>\n\n<section class=\"scheda\" data-open=\"false\">\n<button class=\"scheda-header\" aria-expanded=\"false\" aria-controls=\"mtr-panel-5\" id=\"mtr-header-5\">\n  <span class=\"tag\">Il dettaglio<\/span>\n  <span class=\"titolo-scheda\">L&#8217;ultima riga del piano dice \u00abCUT (hand)\u00bb<\/span>\n  <span class=\"segno\" aria-hidden=\"true\">+<\/span>\n<\/button>\n<div class=\"scheda-panel\" id=\"mtr-panel-5\" role=\"region\" aria-labelledby=\"mtr-header-5\">\n<div class=\"scheda-panel-inner\">\n<div class=\"scheda-contenuto\">\n<span class=\"timbro\">CUT<br>HAND<\/span>\n<p>Nella prova reale descritta da MuTRAP, il pianificatore manomesso scrive una sequenza di azioni ordinarie. Prese una per una, nessuna appare anomala. Poi l&#8217;ultima istruzione cambia oggetto: \u00abCUT (hand)\u00bb, taglia la mano.<\/p>\n<p>Sui comandi senza trigger, il modello alterato ha reso quasi come quello pulito. Per questo il sabotaggio sfugge a un collaudo che prova solo i compiti previsti.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/section>\n\n<section class=\"scheda\" data-open=\"false\">\n<button class=\"scheda-header\" aria-expanded=\"false\" aria-controls=\"mtr-panel-6\" id=\"mtr-header-6\">\n  <span class=\"tag\">Cosa cambia<\/span>\n  <span class=\"titolo-scheda\">Per chi lavora in Italia la data da segnare \u00e8 il 20 gennaio 2027<\/span>\n  <span class=\"segno\" aria-hidden=\"true\">+<\/span>\n<\/button>\n<div class=\"scheda-panel\" id=\"mtr-panel-6\" role=\"region\" aria-labelledby=\"mtr-header-6\">\n<div class=\"scheda-panel-inner\">\n<div class=\"scheda-contenuto\">\n<p>Per ora nessun robot in commercio risulta colpito: si tratta di ricerca di laboratorio, dichiaratamente orientata alla difesa.<\/p>\n<p>Il tema riguarda per\u00f2 chi produce o acquista macchine automatiche in Europa. Dal 20 gennaio 2027 si applica il Regolamento macchine Ue 2023\/1230. Fra i requisiti essenziali introduce la protezione dei sistemi di comando contro la corruzione, attacchi informatici compresi.<\/p>\n<p>Per un integratore, la domanda di Binghamton diventa una voce di collaudo: chi ha addestrato lo strato che guida il robot.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/section>\n\n<\/div>\n\n<div class=\"footer-nota\">\n<span class=\"puntino\" aria-hidden=\"true\"><\/span>\n<p>Il gruppo di Binghamton annuncia come prossimo passo lo studio dei sistemi di percezione e dei controllori dei robot mobili. Il test che manca resta quello diretto: PROTEA contro MuTRAP, sullo stesso robot.<\/p>\n<\/div>\n\n<\/article>\n\n<script>\n(function(){\n  var root=document.getElementById('alground-mutrap');\n  if(!root)return;\n\n  var bottoni=root.querySelectorAll('.scheda-header');\n  var totale=bottoni.length;\n  var open=new Set();\n\n  var box=root.querySelector('#punti-mtr');\n  var label=root.querySelector('#testo-mtr');\n\n  for(var i=1;i<=totale;i++){\n    var d=document.createElement('span');\n    d.className='punto';\n    d.id='mtr-punto-'+i;\n    d.setAttribute('aria-hidden','true');\n    if(box)box.appendChild(d);\n  }\n\n  function aggiorna(){\n    if(label)label.textContent=open.size+' di '+totale+' schede aperte';\n  }\n\n  bottoni.forEach(function(b){\n    b.addEventListener('click',function(){\n      var card=b.closest('.scheda');\n      var on=b.getAttribute('aria-expanded')!=='true';\n      var n=b.id.split('-').pop();\n      var d=root.querySelector('#mtr-punto-'+n);\n      b.setAttribute('aria-expanded',on?'true':'false');\n      card.setAttribute('data-open',on?'true':'false');\n      if(on){open.add(n);if(d)d.classList.add('fatto');}\n      else{open.delete(n);if(d)d.classList.remove('fatto');}\n      aggiorna();\n    });\n  });\n\n  aggiorna();\n})();\n<\/script>\n","protected":false},"excerpt":{"rendered":"<p>Alla Binghamton University un pianificatore manomesso ha fatto puntare a un robot il coltello su una mano finta. Come funziona l&#8217;attacco MuTRAP, cosa pu\u00f2 la difesa PROTEA e quali limiti restano.<\/p>\n","protected":false},"author":16,"featured_media":62925,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":"","jetpack_post_was_ever_published":false},"categories":[387,2168],"tags":[2145,2147,2292,2290,2291,2289,793],"class_list":["post-62923","post","type-post","status-publish","format-standard","has-post-thumbnail","category-guide-sicurezza","category-intelligenza-artificiale","tag-backdoor","tag-intelligenza-artificiale","tag-modelli-linguistici","tag-mutrap","tag-protea","tag-robotica","tag-sicurezza-informatica"],"aioseo_notices":[],"aioseo_head":"\n\t\t<!-- All in One SEO Pro 5.0.3 - aioseo.com -->\n\t<meta name=\"description\" content=\"Binghamton ha mostrato che una parola nascosta in un pianificatore LLM pu\u00f2 far puntare a un robot il coltello su una mano. 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