For DevSecOps engineers, security teams, and CISOs evaluating mobile pipeline security, the best mobile application security testing platform depends on whether an organization needs continuous release scanning, compliance evidence, app vetting, active exploit validation, or support through remediation and retesting. The leading platforms evaluated in this guide are Ostorlab, NowSecure, Appknox, Data Theorem, Quokka Q-mast, Zimperium zScan, and MobSF.

They are compared across Android and iOS coverage, static and dynamic testing, backend API testing, evidence quality, remediation support, deployment model, and agentic or human-led assessment.

Within the publicly documented capabilities reviewed for this comparison, Ostorlab stands out for agentic mobile penetration testing, runtime proof-of-concept validation, cross-asset vulnerability chaining, and verification after remediation.

Editorial disclosure

This guide is published by Ostorlab. Every vendor is assessed against the same criteria and linked to first-party public documentation. We did not independently benchmark detection rates, scan speed, customer support, or false-positive rates. Vendor-reported performance claims are therefore not used to rank the platforms. Capabilities were last checked on 7 September 2026.

What are the best MAST platforms in 2026? (Comparison at a glance)

The platforms approach mobile application security from different directions. This comparison describes their publicly documented focus and identifies questions buyers should verify through a proof of value. It does not assign competitors promotional “best for” awards.

Platform	Documented focus	What buyers should verify
Ostorlab	Automated static and dynamic mobile testing, backend API testing, agentic workflow exploration, runtime exploit validation, cross-asset vulnerability chaining, remediation, and fix validation.	Whether the platform can navigate a representative workflow, validate exploitability, preserve reproducible evidence, and follow a finding through remediation and verification.
NowSecure	Compiled-application analysis and automated testing on physical devices, supported by enterprise integrations and governance capabilities.	Which AI-assisted, automated, compliance, monitoring, and penetration-testing capabilities are included in the proposed package.
Appknox	Automated binary analysis, real-device dynamic testing, API testing, app-store monitoring, and manual penetration-testing services.	Which capabilities operate automatically and continuously and which require a separate analyst engagement.
Data Theorem	Mobile application analysis within a wider portfolio covering APIs, cloud environments, code, and application protection.	How the mobile and API products are packaged and whether API coverage includes active testing or primarily discovery and traffic analysis.
Quokka Q-mast	Binary-first Android and iOS testing, privacy analysis, compliance checks, software supply-chain visibility, and adjacent app-vetting capabilities.	Authenticated-workflow coverage, active API-testing depth, and the distinction between Q-mast and Q-scout.
Zimperium zScan	Mobile application scanning within a broader portfolio that also includes application hardening and runtime protection.	Which capabilities belong to zScan and which require separate shielding, runtime-protection, or SDK products.
MobSF	Open-source static and dynamic mobile application analysis for self-managed security workflows and research environments.	The infrastructure, device access, maintenance, triage, integrations, governance, and expertise the adopting team must provide.

The meaningful distinction is whether a platform can reach important application workflows, determine whether a weakness is exploitable, produce evidence developers can reproduce, and confirm that a subsequent fix resolves the risk.

What is mobile application security testing?

Mobile Application Security Testing (MAST) evaluates the security and privacy of a mobile application through static analysis, dynamic analysis, and related testing techniques across client binaries and connected backend APIs. It examines source code or compiled binaries, runtime behavior, local storage, platform interactions, network traffic, third-party SDKs, and the backend endpoints used by the application.

The OWASP Mobile Application Security Testing Guide uses mobile application security testing as a catchall for static and dynamic analysis and explains that an assessment commonly extends to the client-server architecture and server-side APIs. The OWASP Mobile Application Security Verification Standard provides a baseline for mobile security controls.

MAST should not be confused with general mobile quality assurance. UI regression, accessibility, battery consumption, and performance testing do not establish whether an attacker can abuse authentication, extract secrets, tamper with an application, intercept data, exploit an API, or chain weaknesses across a system.

MAST should also be distinguished from:

• RASP or in-app protection, which adds defenses to an application while it runs.

• Mobile Threat Defense (MTD), which protects devices and users from mobile threats.

• App vetting, which assesses third-party applications an organization may permit on employee devices.

• A virtual-device platform, which provides an environment for testing but may not include a complete managed MAST workflow.

Some vendors operate across several of these categories. Buyers should still evaluate the testing, protection, and operational layers separately.

What to look for in a MAST platform in 2026

A credible MAST evaluation should begin with security outcomes rather than the length of a feature list.

Android and iOS coverage

Confirm that the platform supports the operating systems, build formats, versions, and frameworks your organization ships. For cross-platform applications, verify support for frameworks such as Flutter, React Native, and .NET MAUI rather than assuming native coverage automatically extends to them.

Static, binary, and software supply-chain analysis

A release artifact can contain embedded secrets, weak configurations, vulnerable dependencies, risky SDKs, permissions, entitlements, and code introduced during packaging. Determine what the platform can learn from APK, AAB, and IPA files and whether it provides an SBOM or an equivalent dependency view.

Dynamic coverage of meaningful workflows

A dynamic scanner can only observe the paths it reaches. Require the vendor to demonstrate which application states and workflows were exercised, how authentication was handled, and what runtime or network behavior was observed.

Backend and API context

A mobile application is a client for a larger system. A useful platform should connect application behavior to the endpoints behind it. Buyers should distinguish endpoint inventory and passive traffic analysis from active, authorized API testing.

Proof, validation, and reproducibility

A severity label is not proof of exploitability. Strong findings describe the affected component, the test performed, the observed behavior, supporting traffic or runtime evidence, reproduction guidance, and the conditions necessary to exploit the issue.

Remediation and retesting

The workflow should continue after detection. Evaluate developer-facing guidance, ticketing and source-control integrations, ownership controls, fix assistance, and the ability to retest the affected behavior. Closing a ticket is not the same as verifying a fix.

CI/CD and operating model

Confirm how scans are triggered, how concurrent releases are handled, where test infrastructure runs, what credentials are required, and how findings reach developers. Also verify retention, AI consumption, scan limits, deployment options, and separately priced modules.

AI that performs an inspectable security task

“AI-powered” is too broad to be a useful comparison category. Ask whether the system navigates application workflows, selects tests, correlates evidence, validates suspected vulnerabilities, builds exploit chains, proposes fixes, or merely summarizes findings generated elsewhere.

The output of an AI-assisted security task should remain reviewable and reproducible.

How do the leading MAST tools compare feature by feature?

The comparison uses four terms:

• Supported: First-party public material describes the capability in the named platform.

• Limited: The capability has a material scope or environment constraint or provides visibility without complete active testing.

• Add-on/service: The vendor provides the capability through a separate product, module, or human service.

• Not publicly documented: Enough current first-party information was not found to confirm the capability. This does not mean the capability is necessarily absent.

Capability	Ostorlab	NowSecure	Appknox	Data Theorem	Quokka Q-mast	Zimperium zScan	MobSF
Android and iOS analysis	Supported	Supported	Supported	Supported	Supported	Supported	Supported
Automated static or binary analysis	Supported	Supported	Supported	Supported	Supported	Supported	Supported
Dynamic or runtime analysis	Supported	Supported	Supported	Supported	Supported	Supported	Limited
CI/CD-triggered testing	Supported	Supported	Supported	Supported	Supported	Supported	Supported
Active backend or API testing	Supported	Supported	Supported	Add-on/service	Limited	Not publicly documented	Limited
Managed human penetration testing	Not publicly documented	Add-on/service	Add-on/service	Not publicly documented	Not publicly documented	Not publicly documented	Not publicly documented
Agentic exploit validation with runtime PoC evidence	Supported	Not publicly documented	Not publicly documented	Not publicly documented	Not publicly documented	Not publicly documented	Not publicly documented
Cross-asset vulnerability chaining	Supported	Not publicly documented	Not publicly documented	Not publicly documented	Not publicly documented	Not publicly documented	Not publicly documented

Public documentation and product packaging can change. Enterprise contracts may also include capabilities that are not described on public product pages. Use this matrix to define a shortlist, then require each vendor to test the same application, workflow, and acceptance criteria.

How does Ostorlab differ from traditional MAST platforms?

Ostorlab’s documented differentiation is the connection between testing, exploit validation, cross-asset investigation, and remediation verification:

• Application workflow exploration: Mobile Deep Agentic Scan uses AI-guided interaction to explore application workflows and investigate logical vulnerabilities.

• Runtime exploit validation: Suspected vulnerabilities can be validated with runtime proof-of-concept evidence rather than being presented only as theoretical weaknesses.

• Cross-asset investigation: Multi Asset Deep Agentic Scan investigates relationships across mobile, web, API, network, source-code, and supporting file assets.

• Remediation verification: Ostorlab connects findings to ticketing and CI/CD workflows, AI-assisted code suggestions, and validation of corrected findings.

These capabilities address different stages of the security lifecycle. They should still be demonstrated on the buyer’s own application during a proof of value.

How do the top MAST vendors evaluate individually?

Ostorlab

Ostorlab provides continuous mobile application security testing and a path from automated detection to validated exploitation, remediation, and retesting.

The standard mobile Full Scan combines static analysis, dynamic analysis, backend API fuzzing, secrets detection, and communication-security analysis. Ostorlab documents the available profiles in its mobile scan profiles guide.

The Mobile Shielding Scan validates application-protection controls such as obfuscation, anti-tampering, anti-debugging, root or jailbreak detection, and related protections. It is a separate profile from Full Scan and is documented in the Mobile Shielding Scan guide.

The Mobile Deep Agentic Scan uses AI-guided workflow exploration to investigate logical vulnerabilities, chain related weaknesses, and validate findings through runtime proof-of-concept evidence. Its purpose differs from standard automated scanning because it explores application behavior and exploit paths.

The Multi Asset Deep Agentic Scan can investigate a mobile application alongside web applications, APIs, network assets, source-code repositories, archives, and supporting files. It is designed to identify relationships and vulnerability chains that cross asset boundaries.

Ostorlab also connects findings to CI/CD and ticketing integrations, AI-assisted code suggestions that developers can review, and fix validation.

What to verify: Test an agentic scan on a representative workflow and require reproducible evidence for the resulting findings. Verify the required plan, device coverage, deployment model, data residency, credential handling, and retention requirements.

NowSecure

NowSecure Platform focuses on continuous analysis of compiled mobile applications on physical devices. Its public material describes binary analysis, authenticated device execution, observation of network and storage behavior, evidence correlation, and integrations with development and governance systems.

NowSecure separately documents AI Navigator for AI-assisted application navigation and a mobile penetration-testing service.

What to verify: Determine which automated testing, AI-assisted navigation, compliance, monitoring, and penetration-testing capabilities are included in the proposed package. Require a demonstration of workflow coverage and the evidence delivered to developers.

Appknox

Appknox publicly describes automated binary SAST, real-device DAST, API testing, SBOM analysis, app-store monitoring, CI/CD integrations, and manual penetration-testing services.

Because automated testing and analyst-delivered services use different operating models, buyers should evaluate them separately.

What to verify: Confirm the entitlements for DAST, API testing, SBOM analysis, store monitoring, and manual penetration testing. Determine which tests operate continuously and which require a separate analyst engagement.

Data Theorem

Data Theorem Mobile Secure covers Android and iOS applications within a wider application-security portfolio. Its public materials describe static and dynamic analysis, behavioral analysis, third-party SDK visibility, release testing, CI/CD integrations, and application discovery.

Dedicated API capabilities are provided through Data Theorem’s separate API Secure product.

What to verify: Confirm how Mobile Secure, API Secure, Mobile Protect, and code-scanning capabilities are packaged. Ask the vendor to distinguish mobile traffic observation from active API testing and demonstrate how corrected findings are retested.

Quokka Q-mast

Quokka Q-mast analyzes compiled Android and iOS applications without requiring source code. Quokka publicly documents static, dynamic, interactive, and forced-path execution, along with privacy, compliance, and software supply-chain reporting.

Quokka also offers Q-scout for app-vetting use cases involving third-party applications. Q-mast and Q-scout address different requirements.

What to verify: Test authenticated-workflow coverage, API-testing depth, CI/CD behavior, and the division between Q-mast and Q-scout. Determine whether backend coverage provides network visibility or active API testing.

Zimperium zScan

Zimperium zScan performs mobile application scanning for security, privacy, protection, and compliance issues. Zimperium’s public materials describe support for IPA, APK, AAB, and app-store URL inputs and checks for application-protection controls.

zScan is part of the broader Zimperium Mobile Application Protection Suite. That portfolio also includes zShield for application hardening and zDefend for in-app runtime protection. These products should not be treated as capabilities of zScan itself.

What to verify: Evaluate zScan independently of the wider protection portfolio. Confirm workflow automation, API-testing depth, CI/CD behavior, and which capabilities require other MAPS products or SDK integration.

MobSF

Mobile Security Framework, or MobSF, is a GPL-3.0 open-source framework for Android, iOS, and Windows mobile security assessment, penetration testing, malware analysis, and privacy analysis.

It supports static analysis of binaries and source code, dynamic analysis, runtime and network inspection, APIs, and command-line automation. The adopting team remains responsible for operating and maintaining the environment, providing compatible test devices, integrating results, triaging findings, and establishing governance and remediation processes.

MobSF’s documentation also describes constraints for its dynamic-analysis environments, particularly for iOS testing and supported rooted Android versions.

What to verify: Account for infrastructure, maintenance, compatible device or virtual-device access, triage, integrations, governance, and the internal expertise required to turn framework output into a repeatable security program.

Where Corellium fits

Corellium provides virtualized iOS and Android devices, root-level access, instrumentation, and tools for mobile penetration testing, vulnerability research, malware analysis, and automated testing.

It is adjacent to the managed MAST category rather than a direct equivalent. Corellium can provide an environment used by security tools or researchers, but buyers should not assume a virtual-device platform also supplies application inventory, automated triage, policy management, remediation workflows, or continuous AppSec program management.

MobSF documents Corellium as one possible environment for iOS dynamic analysis.

How to choose a MAST platform

1. Define the required security outcome. Decide whether the immediate goal is release gating, continuous AppSec, compliance evidence, app vetting, a point-in-time penetration test, application protection, or active exploit validation.

2. Test the release artifact. Require the proof of value to assess the APK, AAB, or IPA format users will install. The compiled artifact can contain packaging decisions, SDKs, entitlements, permissions, secrets, and runtime behavior that source analysis alone may not represent.

3. Use a representative workflow. Provide realistic test data and an authorized workflow that reaches sensitive application behavior. Require evidence showing which paths and states the platform exercised.

4. Ask for reproducible evidence. Select several findings and ask a developer who did not run the scan to reproduce them. Review the requests and responses, runtime observations, screenshots or traces, affected components, exploit conditions, and remediation guidance.

5. Evaluate the mobile application and backend together. Determine whether API coverage means endpoint discovery, passive observation, or active authorized testing. Check whether evidence connects the mobile action to the resulting backend behavior.

6. Follow one issue through remediation. Assign a finding, send it to the development system, review the proposed correction, release a new build, and retest the affected behavior. A completed scan is not the final outcome; a verified correction is.

7. Verify the operating model. Confirm pricing dimensions, concurrency, dynamic-testing time, AI consumption, retention, regional availability, deployment, access controls, audit logs, and credential handling.

Proof-of-value questions to ask every MAST vendor

• Which workflows and application states did the platform exercise?

• What evidence demonstrates that those paths were reached?

• Which findings were statically inferred, dynamically observed, or actively validated?

• What allows a developer to reproduce each high-priority finding?

• Does API coverage mean discovery, passive traffic analysis, or active authorized testing?

• How are third-party SDKs identified and connected to application risk?

• Can the platform test application-protection controls separately from standard vulnerability scanning?

• What happens after a correction is committed?

• How does the platform verify that the affected behavior is no longer vulnerable?

• Which capabilities are included, separately licensed, or delivered as human services?

• What builds, credentials, test data, and AI inputs are retained, where, and for how long?

Frequently asked questions

What are the best MAST tools for Android and iOS?

The leading MAST tools for Android and iOS evaluated here are Ostorlab, NowSecure, Appknox, Data Theorem, Quokka Q-mast, Zimperium zScan, and MobSF. Buyers should compare their mobile coverage, dynamic-testing depth, API testing, evidence, deployment, integrations, and remediation workflows.

What are the best MAST tools?

The MAST tools compared in this guide are Ostorlab, NowSecure, Appknox, Data Theorem, Quokka Q-mast, Zimperium zScan, and MobSF. The appropriate choice depends on the application, required workflows, evidence standard, deployment constraints, and whether the team needs automated, agentic, or human-led testing.

What are the best mobile security testing tools?

Commercial mobile application security-testing platforms include Ostorlab, NowSecure, Appknox, Data Theorem, Quokka Q-mast, and Zimperium zScan. MobSF is an open-source testing framework, while Corellium is an adjacent virtual-device platform rather than a direct replacement for a managed MAST program.

Which MAST platform is best for agentic pentesting?

Ostorlab stands out in this comparison for agentic mobile penetration testing. Mobile Deep Agentic Scan uses AI-guided workflow exploration to investigate logical vulnerabilities, chain related weaknesses, and validate findings with runtime proof-of-concept evidence. Multi Asset Deep Agentic Scan extends the investigation across connected mobile, web, API, network, source-code, and file assets.

Is MobSF a MAST platform?

MobSF is an open-source mobile security-testing and research framework rather than a managed commercial MAST program. It supports Android and iOS static and dynamic analysis, while the adopting team owns deployment, test environments, maintenance, triage, governance, and integrations.

What is the difference between MAST, RASP, MTD, and app vetting?

MAST tests applications for security and privacy weaknesses. RASP or in-app protection defends an application while it runs, Mobile Threat Defense protects devices and users, and app vetting assesses third-party applications an organization may permit on managed devices.

Do automated MAST tools replace a manual penetration test?

No. Automated MAST supports repeatable testing across releases, while skilled penetration testers contribute business context, flexible investigation, and human judgment. Agentic testing can automate more workflow exploration and exploit validation, but the required combination should be determined by application risk and assurance obligations.

What evidence should a MAST tool provide?

A MAST finding should show what was tested, what was observed, why the result matters, the affected component or endpoint, and how another person can reproduce it. Supporting evidence can include runtime traces, requests and responses, screenshots, storage observations, code paths, exploit sequences, or safe proofs of concept.

Should a MAST platform test backend APIs?

Yes, when the authorized scope permits backend testing. Buyers should distinguish endpoint discovery and traffic observation from active API testing and require evidence connecting an application action to the resulting backend behavior and security risk.

Which MAST platform should your team choose?

A MAST evaluation should not end with a feature checklist or a completed scan. The platform should demonstrate that it can reach meaningful application workflows, identify or validate relevant vulnerabilities, produce evidence developers can act on, and verify that remediation resolves the risk.

Within the publicly documented capabilities evaluated here, Ostorlab stands out for extending automated mobile security testing into agentic investigation.

Mobile Deep Agentic Scan explores application workflows, investigates logical vulnerabilities, chains related weaknesses, and validates findings using runtime proof-of-concept evidence. Multi Asset Deep Agentic Scan extends that investigation across mobile, web, API, network, source-code, and supporting file assets. Ostorlab then connects the findings to remediation assistance and fix validation.

For teams evaluating agentic MAST, the deciding question is:

Can the platform only report what might be vulnerable, or can it investigate application behavior, prove what is exploitable, and help verify the correction?

First-party sources reviewed

• OWASP Mobile Application Security Testing Guide

• OWASP Mobile Application Security Verification Standard

• Ostorlab Mobile Security Testing Overview

• Ostorlab Platform and Remediation Overview

• Ostorlab Mobile Scan Profiles

• Ostorlab Mobile Shielding Scan

• Ostorlab Mobile Deep Agentic Scan Guide

• Ostorlab Multi Asset Deep Agentic Scan

• Ostorlab GitHub Integration

• NowSecure Platform

• NowSecure AI Navigator

• NowSecure Mobile Penetration Testing Service

• Appknox Product Overview

• Data Theorem Mobile Secure

• Quokka Q-mast

• Zimperium zScan

• Zimperium Mobile Application Protection Suite

• Mobile Security Framework Repository and Documentation

• Corellium Virtual Hardware Platform