Security by the Numbers: What the BreachLock 2026 Penetration Testing Intelligence Report Found from Nearly 5K Pentests

Summary

• BreachLock’s 2026 Penetration Testing Intelligence Report analyzes 4,970 penetration tests and 531,770 findings from the past year.
• Business logic flaws (OWASP A04) rose from 8% to 16% of findings year over year, the defining shift in the web application dataset.
• Cloud audits carried a Critical finding rate of 1.34%, thirteen times higher than web application testing.
• Hardcoded credentials in iOS apps drove 97% of Critical mobile findings.
• 100% of AI applications tested contained OWASP LLM Top 10 vulnerabilities, led by prompt injection, which was present in 28% of tested applications.

➡️ Get the full BreachLock 2026 Penetration Testing Intelligence Report.

Key Insights from the BreachLock 2026 Penetration Testing Intelligence Report

A vulnerability scan asks if a flaw exists. Attackers ask if the application behaves the way it’s supposed to, and increasingly, it doesn’t.

That question is what separates a scanner finding from a real breach path, and it’s the throughline of BreachLock’s 2026 Penetration Testing Intelligence Report. Across 4,970 real-world engagements and 531,770 individual findings, the data shows attackers moving into the places automated tools were never built to look—application workflows, cloud configuration, mobile code, and now AI systems.

Business Logic Is the Blind Spot Scanners Can’t See

The clearest shift in this year’s dataset is the rise of Insecure Design and business logic findings, tracked under OWASP A04. These findings climbed from 8% to 16% of the web application dataset year over year, making it the defining trend in how applications are being broken into in 2026.

Broken Access Control remained the most common high and critical web application finding in 2026 at 27%, but the most significant year-over-year shift was the rise of OWASP A04 Insecure Design and Business Logic flaws, which doubled to 16%. These findings require human reasoning to identify because they stem from how applications behave, not simply whether security controls exist.

Cloud Is the Highest-Probability Breach Path in the Dataset

If business logic is where scanners fall short, cloud is where the consequences compound fastest. Cloud security audits in this year’s dataset carried a Critical finding rate of 1.34%, more than thirteen times higher than the rate found in general web application testing.

The drivers behind that gap are familiar, which is part of the problem. Exposed S3 buckets, Lambda functions leaking data through overly permissive execution roles, and GuardDuty monitoring left disabled all showed up repeatedly across engagements. None of these require sophisticated exploitation. They require an attacker who knows where cloud teams tend to leave the door open.

Mobile Risk Now Concentrates in a Single Failure Mode

Mobile findings told a narrower story this year, and that narrowness is the point. Hardcoded credentials in iOS applications accounted for 97% of all Critical mobile findings, credentials that can be pulled out of a compiled app with free, publicly available tools in a matter of minutes.

When one failure mode accounts for nearly every Critical finding in a category, it stops being a tooling gap and starts being a process gap. Mobile teams don’t need better scanners to catch this. They need credential management that never lets a hardcoded secret reach a build in the first place.

AI Applications Are Entering Production with the Same Gaps Web Apps Had a Decade Ago

BreachLock’s first formal AI and LLM penetration testing dataset found that 100% of AI applications tested contained vulnerabilities aligned with the OWASP Top 10 for LLMs. Every single one. Prompt injection (LLM01) was the most prevalent and impactful finding in the dataset, present in 28% of tested applications.

Prompt Injection was the most common LLM application security finding in 2026 at 28%, but Excessive Agency emerged as a leading AI-specific risk at 15%. The results show that securing AI applications requires defending not only against malicious prompts, but also against how autonomous agents access data, invoke tools, and make decisions.

This pattern should look familiar to anyone who worked through the early years of web application security. A new class of software is entering production faster than the practices needed to secure it have matured, and the OWASP LLM Top 10 exists precisely because these applications carry their own attack surface, one that traditional web testing wasn’t built to evaluate.

What This Means for Security Programs

The programs pulling ahead this year share a pattern.

1. They’re testing for business logic flaws specifically, not just running automated scans and calling it coverage.

2. They’re auditing cloud configuration on a recurring basis instead of treating it as a one-time hardening exercise.

3. They’re building credential management into the mobile development pipeline rather than catching hardcoded secrets after the fact.

4. And they’re testing AI and LLM applications against the OWASP LLM Top 10 before those applications ever reach production.

The common thread across every finding in this report is that automated tools are good at confirming what’s technically present. They’re far less reliable at telling you how an application actually behaves when someone tries to break it on purpose. That gap, between what’s been scanned and what’s been validated, is where this year’s breaches are living.

Get BreachLock’s 2026 Penetration Testing Intelligence Report for the full breakdown of cloud, mobile, API, and AI vulnerability data, along with benchmarks across key industries.

Frequently Asked Questions about the BreachLock 2026 Penetration Testing Intelligence Report

What is OWASP A04, and why did it rise so sharply in 2026?

OWASP A04, Insecure Design, covers vulnerabilities in an application’s intended business logic rather than a specific coding defect. It rose from 8% to 16% of findings year over year because these flaws, such as race conditions in checkout flows or workflow bypasses in approval processes, don’t appear in automated scans and require testers who understand how the application is supposed to behave.

Why do cloud security audits find so many more Critical issues than web application testing?

Cloud audits carried a Critical finding rate of 1.34%, thirteen times higher than web application testing, largely because of exposed storage buckets, overly permissive serverless function permissions, and disabled monitoring services. These issues often grant broad access once found, which raises their severity compared to a typical application-layer bug.

What is the OWASP LLM Top 10, and why does it matter for AI penetration testing?

The OWASP Top 10 List for LLMs is a standardized list of the ten most critical security risks specific to large language model applications, covering issues like prompt injection, insecure output handling, and excessive agency. BreachLock’s first formal round of AI and LLM penetration testing found these vulnerabilities in 100% of AI applications tested, with prompt injection the most prevalent and impactful finding at 28%.

What should security teams prioritize based on BreachLock’s 2026 Penetration Testing Intelligence Report?

Security teams should prioritize testing for business logic flaws that automated scanners miss, auditing cloud configuration on a recurring basis, building credential management into mobile development pipelines, and testing AI and LLM applications against the OWASP LLM Top 10 before production deployment.

Author

BreachLock Labs