AI Applications Contain Security Vulnerabilities, According to Report
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A penetration testing report by BreachLock found that 100% of AI applications tested contained vulnerabilities aligned with the OWASP Top 10 for LLMs. Prompt injection (LLM01) was the most prevalent and impactful finding in the dataset, present in 28% of tested applications.
The report also identifies a sharp shift in how attackers are targeting web applications. Insecure Design and business logic flaws (OWASP A04) rose from 8% to 16% of findings year over year, a trend-defining increase in the 2026 web application dataset. Testers observed attackers exploiting race conditions in checkout flows, escalating privileges through parameter manipulation, and bypassing approval workflows outright. These issues do not appear on automated scanner reports. Finding them requires testers who understand how an application is supposed to behave and can reason through how that logic can be subverted.
Cloud environments produced the highest concentration of severe risk in the dataset. Cloud security audits carried a Critical finding rate of 1.34%, thirteen times higher than the rate found in web application testing, driven largely by exposed S3 buckets, leaking Lambda functions, and disabled GuardDuty monitoring.
Mobile applications showed a similarly narrow but severe risk profile. Hardcoded credentials in iOS applications accounted for 97% of all Critical mobile findings this year. These credentials can be extracted with free, publicly available tools in minutes, and credential-related vulnerabilities continue to be a top attack vector in headlines this year.
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