Latest News

OVERVIEW

In mid-2025, McDonald’s McHire platform showed how basic authorization failures still break at internet scale. Public reporting described roughly 64 million applicant records as exposed through a combination of weak admin credentials and an IDOR-style record access flaw that let researchers retrieve other applicants’ PII. The endpoint accepted a sequential lead_id parameter. Decrement by one, get someone else's PII. No sophisticated tooling required. The attacker just changed a number.

That same month, GitLab patched multiple critical authorization bypasses: CVE-2025-4972, CVE-2025-6168, and CVE-2025-3396. Users bypassing group-level restrictions. A 2FA bypass. All logic and authorization flaws.

Broken Access Control has been OWASP's #1 vulnerability category for many years. We've known about IDOR, BOLA, and privilege escalation for decades. The tooling has improved dramatically. And yet the breaches keep happening.

Let’s understand why - and what's actually changing in 2026.

 

Why scanners have always missed business logic flaws

The practitioner objection you'll find on every security forum is this: "Automated tools can't find IDOR. You need a real pentester for business logic."

This has been true. Here's the technical reason why.

Traditional DAST scanners crawl endpoints and match responses against known vulnerability signatures. It knows what a SQL injection looks like. It knows what a missing security header looks like. What it cannot do:

• Understand that a lead_id parameter should only return results belonging to the authenticated user
• Recognize that transitioning from a free trial to a paid account unlocks an endpoint that's missing authorization checks
• Chain a credential found in one service against an SSH server in a different one
• Understand what your application is supposed to do - and test whether it actually does it

Business logic flaws require context. Scanners don't have context. They have signatures.

We observe that more than 50% of critical vulnerabilities are authorization and access control flaws, and the traditional SAST detection rate for these was near zero. Finding them manually took 2 to 4 hours per endpoint. The same task, with AI reasoning, can be done in minutes.

That gap between what scanners find and what attackers exploit is where most breaches actually live.

 

The three structural problems with how enterprises test web apps today

The business logic blind spot is part of a larger structural problem. After fifteen years of building security testing tools and running a community of 40,000+ security professionals, I see the same three gaps at almost every enterprise:

Gap 1 - Scope: 20% tested vs. 100% probed

Most organizations test their crown-jewel applications. Peripheral apps, forgotten subdomains, UAT environments, third-party integrations - these get assumed safe.

• The average enterprise tests ~20% of its web application portfolio annually
• 20% of real-world breaches begin through peripheral asset initial access, the exact assets not being tested
• Attackers probe 100% of your surface. Your testing covers 20% of it.

Gap 2 - Depth: Findings in isolation vs. attackers who chain everything

DAST tools generate 40 to 70% false positive rates. Your security team spends most of its time triaging noise - while missing the connections between findings that matter.

• 22% of breaches start with credential abuse - a pattern no single-endpoint scanner can follow
• An IDOR is a medium finding in isolation. Combined with a leaked credential and an SSH pivot, it's a full database compromise.
• Business logic flaws - IDOR, BOLA, privilege escalation, workflow abuse - are app-specific, context-dependent, and scanner-invisible

Gap 3 - Speed: Annual testing vs. daily deployment

Development teams ship weekly. Sometimes daily. Most enterprises still pentest once a year.

• Every release cycle widens the gap between when a vulnerability was introduced and when it's discovered
• By the time the annual pentest report arrives, the application has already changed - multiple times
• Attackers chain findings, reuse credentials, and pivot between apps and infrastructure. Defenders see isolated, noisy alerts - months after the fact.

 

What agentic AI actually changes - and what it doesn't

The shift worth understanding isn't faster scanning. It's a different kind of reasoning.

Here's a real example of what this looks like in practice. An agentic AI system was testing a web application when it found an exposed .git directory:

• Step 01 - Recon: Agent reconstructed the repo and extracted database credentials from config files
• Step 02 - Blocked: Agent tried direct database access. The port was closed externally. A traditional scanner files a medium-severity finding and stops.
• Step 03 - Pivot: Agent hypothesized credential reuse - the same reasoning a skilled attacker applies, and tested those credentials against SSH. Got root.
• Step 04 - Escalate: From root, the agent discovered private keys, pivoted through the internal network, and reached the database from the inside.

Four steps. Fully autonomous. No human steering. No predefined playbook.

This is the difference between signature detection and contextual reasoning. The agent didn't match a pattern - it formed a hypothesis, tested it, and adapted. That is exactly what IDOR exploitation looks like in the real world: an attacker who understands what the application is trying to do, and tests whether the authorization logic actually enforces it.

Modern agentic platforms can now detect BOLA, IDOR, privilege escalation, and workflow bypasses at scale - the vulnerabilities practitioners have always said required human creativity. That doesn't mean human pentesters are obsolete. It means the division of labor is changing:

• Agents handle: continuous coverage across hundreds of applications, credential reuse testing, app-to-app pivots, peripheral asset enumeration, OWASP Top 10 + business logic at scale
• Human experts handle: novel attack paths requiring deep product context, architecture-level threat modeling, regulatory and compliance interpretation, findings that require institutional knowledge no agent has

 

What good agentic web app testing actually looks like in 2026

Whether you're evaluating a platform or building internal capability, these are the questions that matter:

• Does it detect business logic vulnerabilities? BOLA, IDOR, privilege escalation, and workflow bypasses - not just OWASP Top 10 signatures
• Does it chain findings? A credential exposure finding should be tested for reuse. A peripheral asset should be checked for lateral movement paths.
• Does it prove exploitability? Every finding should come with working proof-of-exploit, exact reproduction steps, and business impact - not a CVSS score
• Does it handle authenticated testing? MFA flows, session tokens, SSO - the platform should persist through modern auth without collapsing
• Can it test internal applications? Not just your external attack surface - the apps inside your network that never touch the public internet
• Does it match your release cadence? Weekly. On-demand. CI/CD-triggered. Annual is not a cadence - it's a gap.

 

The numbers that should anchor any budget conversation

The cost comparison for web app pen testing in 2026:

In practice, one Fortune 500 company moved from testing 200 of its 2,000+ web applications annually - at ~$5,000 per app - to continuous coverage across its full portfolio at under $1,000 per app. That's an 11x cost reduction and a jump from 10% to 99% coverage. Simultaneously.

In 2024, proactive security testing prevented an estimated $2.88 billion in potential losses - with IDOR, AWS key leakage, and 2FA bypasses among the highest-value findings, each carrying six-figure damage potential individually.

The math on doing nothing has changed.

One question worth asking your team today

Business logic issues. IDOR. BOLA. Credential reuse across services. These are not exotic vulnerabilities. They're OWASP #1. They're McDonald's and GitLab, and the breach that happened at a company that looked exactly like yours.

The question worth asking: of your web application portfolio, how much of it has been tested - not scanned, tested - in the last 90 days? With proof-of-exploit for every finding? Across both external and internal assets?

If the answer is less than half, you're not running a security program. You're running a compliance exercise and hoping the untested part doesn't make headlines.

Here’s how you can try Agentic Web-Application Pentesting

• Start with a free web application pentesting at firecompass.com/explorer
• No agents to install, results in minutes. Most teams find applications they didn't know existed. That tends to reframe the conversation quickly.

 

About Priyanka Aash (Blog Author)

Priyanka Aash is Co-Founder of FireCompass, a 2025 SC Media Power Player Honoree, and the author of The AI Divide. She is known for building the global CISOPlatform community focused on bringing together 40,000+ cybersecurity leaders together and driving meaningful industry conversations.

 

About FireCompass

FireCompass is an Agentic AI platform for autonomous penetration testing and red teaming across Web, API, and infrastructure. It discovers shadow assets and web applications, safely validates what is exploitable, and connects findings into multi-stage attack paths with near-zero false positives. Unlike traditional scanners, it discovers credential reuse, business-logic flaws, privilege escalation, and app-to-app or app-to-network lateral movement. It can operate autonomously or with expert-in-the-loop validation. FireCompass has 30+ analyst recognitions across Gartner, Forrester, IDC, and is trusted by Fortune 1000 enterprises. Try it free at firecompass.com/explorer .