GitHub hat die Einf\u00fchrung eines experimentellen maschinellen Lernsystems im Code-Scanning-Service angek\u00fcndigt, um h\u00e4ufige Arten von Schwachstellen im Code zu identifizieren. W\u00e4hrend der Testphase ist die neue Funktionalit\u00e4t vorerst nur f\u00fcr Repositories mit Code in JavaScript und TypeScript verf\u00fcgbar. Es wird festgestellt, dass der Einsatz des maschinellen Lernens das Spektrum der erkennbaren Probleme erheblich erweitert hat, da das System nun nicht mehr nur die \u00dcberpr\u00fcfung typischer Muster durchf\u00fchrt und nicht an bekannte Frameworks gebunden ist. Zu den von dem neuen System erkannten Problemen geh\u00f6ren Fehler, die zu Cross-Site-Scripting (XSS) f\u00fchren, und die Verzerrung von Dateipfaden (zum Beispiel durch die Angabe von \u00ab\/..\u00bb), sowie die Injektion von SQL- und NoSQL-Abfragen. <\/p>\n
Der Code-Scanning-Service erm\u00f6glicht die Erkennung von Schwachstellen in der fr\u00fchen Entwicklungsphase durch das Scannen jeder \u201egit push\u201c-Operation auf m\u00f6gliche Probleme. Das Ergebnis wird direkt an den Pull-Request angeh\u00e4ngt. Zuvor wurde die \u00dcberpr\u00fcfung mit der CodeQL-Engine durchgef\u00fchrt, die Muster mit typischen Beispielen von anf\u00e4lligem Code analysiert (CodeQL erm\u00f6glicht es, ein Muster f\u00fcr anf\u00e4lligen Code zu erstellen, um das Vorhandensein \u00e4hnlicher Schwachstellen in anderen Projekten zu identifizieren). Die neue Engine, die maschinelles Lernen nutzt, kann zuvor unbekannte Schwachstellen identifizieren, da sie nicht an die Durchmusterung von Code-Mustern gebunden ist, die spezifische Schwachstellen beschreiben. Der Preis f\u00fcr diese F\u00e4higkeit ist eine Erh\u00f6hung der Anzahl der Fehlalarme im Vergleich zu den \u00dcberpr\u00fcfungen auf Basis von CodeQL. 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Quelle: opennet.ru<\/a> <\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"