“100% of AI applications tested contained vulnerabilities aligned with the OWASP Top 10 for LLMs.”
Prompt injection (LLM01): the most common and consequential flaw
A penetration testing report by BreachLock found that every AI application it tested had at least one vulnerability mapped to the OWASP Top 10 for LLMs, and prompt injection (LLM01) stood out as both the most prevalent and the most impactful single finding. BreachLock’s dataset shows prompt injection present in 28% of tested applications, a clear signal that attackers are focusing on manipulating model inputs and that practitioners must treat prompt handling as a distinct attack surface.
Insecure Design and business logic (OWASP A04): a doubling that changes how testers work
Testers reported a marked shift in web-application targeting: Insecure Design and business logic flaws (OWASP A04) rose from 8% to 16% of findings year over year in the 2026 web application dataset. BreachLock’s testers observed concrete exploitation techniques — race conditions in checkout flows, privilege escalation through parameter manipulation, and outright bypasses of approval workflows — that automated scanners routinely miss. According to the report, these issues require human testers who understand how an application is supposed to behave and can reason about how that logic can be subverted.
Cloud audits reveal concentrated critical risk: exposed S3, leaking Lambdas, and disabled GuardDuty
Cloud environments produced the highest concentration of severe risk in BreachLock’s dataset. Cloud security audits carried a Critical finding rate of 1.34%, a figure the report highlights as thirteen times higher than the Critical rate observed in web application testing. BreachLock attributes this elevated criticality largely to exposed S3 buckets, leaking Lambda functions, and disabled GuardDuty monitoring — specific cloud misconfigurations and exposures that drove the bulk of the most serious findings.
Mobile risk is narrow but severe: hardcoded iOS credentials dominate Critical findings
Mobile applications in the dataset showed a tight but high-severity profile: hardcoded credentials in iOS applications accounted for 97% of all Critical mobile findings this year. BreachLock notes those credentials can be extracted with free, publicly available tools in minutes, and emphasizes that credential-related vulnerabilities remain a top attack vector in headlines this year. The concentration of critical mobile risk around a single pattern — embedded credentials — points to a small set of high-impact fixes for mobile teams.
What this means for technologists, procurement leaders, and end users
- Technologists and security teams: expect to prioritize prompt-handling controls, hands-on business-logic testing, and cloud configuration audits. The report’s rise in OWASP A04 findings and the Cloud Critical rate of 1.34% underline the need for testers who can reason about application behavior rather than relying solely on automated scanners.
- Procurement leaders and affected enterprises: contracts and third-party risk assessments should require verification that providers scan for exposed S3 buckets, leaking Lambda functions, disabled GuardDuty, and that mobile builds do not contain hardcoded credentials — the latter accounting for 97% of Critical mobile findings in the dataset.
- End users and defenders: account and credential hygiene remains central. The report highlights that hardcoded iOS credentials can be extracted in minutes with free tools and that credential-related vectors continue to dominate headlines, reinforcing the practical consequences of leaks for user accounts and service integrity.
BreachLock’s findings draw a connecting line between three trends: AI-specific attack surfaces such as prompt injection, a year-over-year doubling in business-logic failures, and concentrated cloud and mobile misconfigurations that produce most of the Critical outcomes. For organizations building or buying AI-enabled services, the report implies a shift from checkbox scanning to threat modeling, logic-aware testing, and cloud-configuration hygiene.
For readers who want a practical next step, BreachLock has scheduled a live event on August 27, 2026 at 2 PM EDT to explore how AI-driven cloud security solutions can elevate detection and operations. The core question the report leaves on the table is operational: will organizations adapt their testing methodologies and procurement standards quickly enough to address a landscape where every tested AI application already shows an LLM-aligned vulnerability?
