Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Your stack has a SAST. A DAST. An SCA. A SIEM. And probably seven more tools your developers have quietly stopped reading alerts from. None of them were built for mobile. That's not a criticism. It's a fact about what those tools were designed to do. They were built for web applications, network infrastructure, and cloud environments, which were the priorities of a different era. Mobile apps came later. And the security tooling never fully caught up.

You run the pipeline. You own the releases. And somewhere between the security team's findings and the development team's sprint, you're the one getting asked to explain why nothing is getting fixed. That's not a security problem. It's a coordination problem, and it's structural. According to the DuploCloud AI + DevOps Report, Sep 2025, The pipeline is under more pressure than it's ever been. The attack surface is wider than it's ever been.

Security budgets have never been higher. The average enterprise now runs 50 security tools, and most teams added more last year than the year before. And yet, alert fatigue is at the breaking point. Coverage gaps in mobile and API environments continue to widen. The exploitability problem at the center of most AppSec programs remains unsolved. Breaches keep happening. Risk scores don't move.

Your app passed testing. CI/CD ran clean. The App Store approved it. Your security team signed off. Six weeks later, attackers are reverse-engineering the binary on rooted devices, injecting JavaScript into your runtime, and probing API endpoints your scanner never modeled. Nothing in the code changed. The threat environment did. This is the central problem of modern mobile application security, and it doesn't get fixed by adding more pre-release scanners.

81% of development teams knowingly ship code with vulnerabilities. That number gets quoted a lot. Usually to make a point about how developers don't take security seriously. Here's a different reading: most of those developers knew the vulnerability was there. They just couldn't do anything about it in time. That's not apathy. That's a system failure. Feature deadlines are usually less flexible than security work.

That is not a failure of fuzzing. It is a failure of interpretation. In a recent AFL++ fuzzing campaign targeting libarchive, we ran approximately 8.5 billion executions across all fuzzing phases, generated over a thousand crash files, and ultimately reduced them to two unique crash sites through structured crash triage and deduplication. This blog is a practical, engineering-first guide to that process: If your fuzzing pipeline stops at crash counts, you are not measuring security.

357 crash reports. 2 actual bugs. That is not a typo. That is the reality of modern application security testing. In a recent fuzzing campaign, over a thousand crash files were generated across billions of executions. After crash deduplication and triage, that number collapsed to just two unique issues. Not hundreds of vulnerabilities. Not dozens of risks. Two. And yet, most security teams would have celebrated the initial numbers.

Most mobile security workflows end in a familiar way. A scan runs, a report is generated, and the output looks reassuring. There are no critical issues, maybe a few medium findings, nothing that blocks a release. The process completes, the team moves forward, and the app ships. At that moment, the assumption is clear. The app has been tested. The risk is understood. But there is a question that rarely gets asked, and it changes the entire conversation.

“AI-powered DAST” is everywhere. It signals progress, but assumes something fundamental was missing. It wasn’t. DAST struggled not from lack of intelligence, but from lack of depth. Most tools never reached inside authenticated, stateful, multi-step journeys where real logic, sensitive data, and critical vulnerabilities exist. That’s the part Appknox solved years ago. AI here is not a reset. It is an accelerator, applied to a system already operating where risk actually lives.

357 crash files. 2 real bug sites. That’s the outcome of this AFL++ campaign after roughly 8.5 billion executions across multiple harnesses, binaries, and phases. At first glance, everything looked like success. Crashes were increasing steadily. New inputs were being generated every few seconds. Coverage appeared to improve over time. From a surface-level perspective, the campaign looked productive. Then triage began.