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This research demonstrates how LLMs can be leveraged to automate deep inspection of XML parsing logic in SAML implementations, revealing critical authentication bypasses that traditional testing might miss. The findings underscore the necessity for AI-assisted tools to probe inconsistent parser behaviors and signature wrapping vulnerabilities inherent in widely used enterprise identity protocols.
Claude AI Finds SAML Security Flaws That Can Let Attackers Take Over Accounts
Security researchers have used Anthropic’s Claude AI to uncover serious flaws in Security Assertion Markup Language (SAML) implementations that could allow attackers to bypass authentication and take over user accounts. The findings highlight the persistent security risks created by XML signature processing, inconsistent parser behavior, and custom SAML code. A researcher at Oblique Security built […] The post Claude AI Finds SAML Security Flaws That Can Let Attackers Take Over Accounts appeared first on Cyber Security News.
Security researchers have used Anthropic’s Claude AI to uncover serious flaws in Security Assertion Markup Language (SAML) implementations that could allow attackers to bypass authentication and take over user accounts. The findings highlight the persistent security risks created by XML signature processing, inconsistent parser behavior, and custom SAML code. A researcher at Oblique…
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