GitHub ha annunciato l'aggiunta di un sistema sperimentale di machine learning al servizio di Code scanning, per identificare i tipi comuni di vulnerabilit\u00e0 nel codice. Durante la fase di testing, questa nuova funzionalit\u00e0 \u00e8 disponibile solo per i repository contenenti codice in linguaggi JavaScript e TypeScript. Si segnala che l'uso del sistema di machine learning ha notevolmente ampliato lo spettro dei problemi rilevabili, poich\u00e9 nell'analisi non ci si limita pi\u00f9 a controllare modelli standardizzati e non si \u00e8 legati a framework noti. Tra i problemi identificati dal nuovo sistema, ci sono errori che portano a script intersiti (XSS) e l'alterazione dei percorsi dei file (ad esempio, tramite l'indicazione \u00ab\/..\u00bb), per la sostituzione di query SQL e NoSQL. <\/p>\n
Il servizio Code scanning consente di rilevare vulnerabilit\u00e0 nelle prime fasi dello sviluppo, attraverso la scansione di ogni operazione di \u00abgit push\u00bb alla ricerca di potenziali problemi. Il risultato viene direttamente allegato alla pull request. In precedenza, la verifica veniva effettuata utilizzando il motore CodeQL, che analizzava i modelli con esempi tipici di codice vulnerabile (CodeQL consente di creare un modello di codice vulnerabile per identificare la presenza di vulnerabilit\u00e0 simili nel codice di altri progetti). Il nuovo motore, che utilizza il machine learning, pu\u00f2 identificare vulnerabilit\u00e0 precedentemente sconosciute poich\u00e9 non \u00e8 legato a un semplice confronto di modelli di codice che descrivono vulnerabilit\u00e0 specifiche. Il prezzo di tale capacit\u00e0 \u00e8 un aumento del numero di falsi positivi rispetto ai controlli basati su CodeQL. 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Fonte: opennet.ru<\/a> <\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"