Top 10 Application Security Testing Tools (2026 Edition)

TLDR;

• Most teams prioritize infrastructure and cloud security, but 82% of known vulnerabilities actually originate in the application layer, making AppSec tools essential to use early in the pipeline.
• Legacy scanners flood teams with alerts, and most of it is noise that buries real risk. OX Security analyzed 101+ million findings across 178 organizations. By correlating results with reachability, exploitability, and business impact, the platform surfaces what actually matters and eliminates it at the source.
• OX Security is an enterprise-grade platform that secures applications from AI code generation to runtime, using AI-native context to predict risk, trace it to the exact line of code, and eliminate it at the source. With unified capabilities across VibeSec, code, cloud, and agentic pentesting, OX gives security teams a single control plane to prevent, detect, and remediate risk across the entire software lifecycle.
• Tools like SonarQube and Checkmarx remain strong for language-specific rule enforcement but lack full pipeline integration, real-time context, and any response to AI-speed development.
• DAST tools like Burp Suite and StackHawk surface runtime issues but can’t tie findings back to source code or cover build-time misconfigurations. Agentic penetration testing closes this gap by running continuous exploit simulation and tracing every finding to the exact repository, file, and commit responsible.
• AI coding tools like Cursor and Copilot now generate code faster than security teams can review it. AppSec tooling that only scans after code is written can’t keep pace; the gap between code creation and security review is where most risk now lives.

Top 10 Static Application Security Testing Tools

1. OX Security
2. Checkmarx
3. SonarQube
4. Burp Suite
5. OWASP ZAP
6. Invicti
7. StackHawk
8. Contrast Assess
9. Snyk
10. Mend.io (WhiteSource)

Security testing is one of the most important phases of the Software Development Life Cycle (SDLC). Testing helps prevent data breaches and security concerns in the real world. Based on what I’ve seen in mid-to-large engineering orgs, the process either gets sidelined due to delivery pressure or is limited to a surface-level scan that rarely blocks real threats. But ignoring AppSec doesn’t shift the risk; it only delays it.

82% of all known vulnerabilities are estimated to be present in application code itself. This means that the attack surface is no longer limited to exposed ports or unpatched servers; it can be your React API gateway, your misconfigured JWT logic, or that npm package you updated last night without noticing the changelog.

Another impressive statistic about security risks – OX Security analyzed 101+ million security findings across 178 organizations. Only 2 to 5% of alerts were truly high-risk. That means 95% of alerts are noise, distracting AppSec and DevOps teams from real issues

When engineers are forced to chase irrelevant alerts, they risk missing the vulnerabilities that actually open the door to attackers.

As an AppSec Manager, I have experienced concerns that security issues rarely show up during staging. Many of them are a result of developer shortcuts or tech debts, legacy patterns, or a misunderstood token refresh flow buried deep in a service.

These are the types of risks that don’t show up in a basic vulnerability scan. Insecure Direct Object Reference (IDOR) issues, for example, often pass through automated tools undetected because they depend on authorization logic that’s hard to model statically. 

Secrets in environment files get committed during early development stages and are forgotten. Vulnerable dependencies are bundled into builds without visibility unless an SCA tool is properly integrated. These problems aren’t caught unless you have AppSec controls that understand application behavior, not just syntax.

That’s why I always encourage my team to use Application Security Testing (AppSec) firsthand after development or on an ongoing basis. AppSec refers to the set of tools and practices used to identify, prioritize, and remediate security vulnerabilities in application code, configuration, and dependencies. We can easily carry this out via tools purpose-built for different layers of the application stack. 

Teams can also adopt ASPM alongside these tools to consolidate visibility and streamline prioritization. ASPM doesn’t replace AST, but it connects the dots, bringing together findings from SAST, DAST, IAST, and SCA tools, correlating them with pipeline metadata, and surfacing the issues that are reachable, exploitable, and impactful. 

In this post, I’ll break down 10 of the most relevant AppSec testing tools in 2025, categorized by their testing method:

• SAST (Static Application Security Testing)
• DAST (Dynamic Application Security Testing)
• IAST (Interactive Application Security Testing)
• SCA (Software Composition Analysis)

Types of Application Security Testing

Before discussing the tools and their features, I’ll brief you on the types of ASTs to better understand which tools align best with each phase of SDLC. We have to remember that AST is not a single technique; it’s a layered approach that covers different phases of the SDLC and different attack vectors. Choosing the right combination of testing types helps teams catch both structural and runtime issues, misconfigurations, and supply chain risks.

Here’s a breakdown of the key AST types used in production workflows, along with tool examples (you’ll find detailed tool evaluations in the sections below via bookmarks).

In-Depth Breakdown of Top 10 AppSec Testing Tools

SAST (Static Application Security Testing) Tools

1. OX Security

In most environments I’ve worked in, teams are dealing with two problems at once, a growing backlog of vulnerabilities they can’t get through, and AI coding tools such as Cursor and Copilot introducing new ones faster than they can be reviewed. OX is built to address both.

The platform is structured around four products that cover the full code-to-runtime lifecycle. OX Code handles detection and prioritization across your SDLC, using context that predicts risk before runtime rather than waiting for issues to surface in production. OX Cloud connects that code-level context to your runtime and cloud environment, so risks don’t fall through the gap between development and deployment. 

VibeSec is OX’s response to AI native development; it embeds security directly into AI coding assistants such as Cursor and Copilot, making prevention at creation possible rather than scanning after the fact. And the Agentic Pentester continuously simulates real attacker techniques against your application, tying every finding back to the specific file and the commit responsible.

What holds all of this together is the PBOM( Pipeline Bill of Materials), which tracks every component, dependency, and configuration change across your pipeline in real time. It’s what makes the unified control plane across code, pipelines, and cloud actually work in practice, rather than just being another dashboard that aggregates alerts

Key Features

• Code Projection identifies vulnerabilities by reachability, exploitability, and business impact, not just severity score
• VibeSec embeds security policies into AI coding assistants such as Cursor and Copilot
• Agentic Pentester runs continuous exploit simulation tied directly to source code
• Covers SAST, SCA, SBOM, secrets, IaC, CI/CD posture, and cloud in one place
• 100+ integrations, zero-agent setup, connects to repos and pipelines in hours

Hands-On: Scan of ERPNext Repos Using OX Security

I tested OX Security on my forked version of the ERPNext open-source repository, which is an actively maintained ERP platform with over 50,000 commits. 

After connecting the GitHub repo, OX scanned 38 repositories across the organization and automatically pulled in the CI/CD pipeline metadata.

From the results, OX flagged 398 total alerts, out of which 210 were aggregated, and only 22 required prioritization based on exploitability. This can significantly reduce the triage time while also mapping findings across code security, open-source dependencies, and secrets in the codebase.

For example, in the Open Source Security module, OX identified 30 vulnerable components across 3,471 libraries. Under Secret/PII Scan, it found 5 total exposures, including hardcoded secrets, something that SAST tools often miss without custom rules. The platform also assessed CI/CD posture, flagging 25 risks linked to insecure or misconfigured build pipelines.

It helped me find the top 5% of issues that actually matter. Also, the visual flow in the dashboard helped me trace the security posture from source control through the pipeline and into the deployment registry. This allowed me to quickly understand where the gaps were, not just in the code, but across the entire delivery chain.

Pros

• Built-in support for multiple security use cases (SAST, SCA, Secrets, IaC)
• High signal-to-noise ratio with contextual alerts
• Easy onboarding via GitHub, GitLab, or Bitbucket with zero-agent setup
• Security posture visualization across all projects and pipelines
• Great for scaling AppSec across 100+ repos and teams

Cons

• Relatively new in the market, so initial struggles integrating with older on-prem CI tools
• UI can feel overwhelming initially due to its breadth of features

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2. Checkmarx

Checkmarx is another SAST platform built for organizations that need customizable and policy-driven code scanning. It’s widely adopted in regulated industries (finance, healthcare, defense) where security reviews are formalized and must align with standards like OWASP Top 10, PCI-DSS, and NIST 800-53.

Checkmarx claims to focus on code path analysis and the fine-grained control over scanning rules. It builds a complete abstract syntax tree to trace data flows across services, detect taint propagation, and uncover issues like insecure data deserialization, path traversal. Moreover, Checkmarx supports custom queries in its own language (CxQL), helping security teams to define organization-specific anti-patterns easily. 

Key Features:

• Deep dataflow and control-flow analysis across files and modules.
• Support for 30+ languages and frameworks.
• Custom query language (CxQL) for writing bespoke security rules.
• Policy-based scan gating tied to compliance requirements.
• Integration with IDEs (VSCode, IntelliJ), CI/CD tools, and ticketing systems.

Hands-On: Checkmarx Flags Command Injection in Python Script

While testing a vulnerable Python project with Checkmarx, I scanned a brute-force script that used user-supplied arguments in a subprocess.run() call. The code takes input from sys.argv to build a command like this:

result = subprocess.run([program, username, password], stdout=subprocess.DEVNULL)

Checkmarx flagged this line with a high-severity Command Injection warning. Since the program variable comes directly from user input (sys.argv[1]), it’s possible for an attacker to insert malicious commands, which the subprocess would execute without validation. Here’s a snapshot:

This kind of vulnerability allows arbitrary command execution and is commonly exploited in real-world attacks.

Checkmarx automatically traced the data flow from input to sink and surfaced the issue along with contextual explanation and source file metadata. Alongside this high-priority finding, the scan also surfaced multiple SQL Injection and Stored XSS issues, grouped by severity, making it easier to triage based on exploitability and impact.

Pros:

• Unmatched rule customization and query capabilities
• Strong language coverage and deep scanning logic
• Suited for regulated organizations with high compliance burden
• Centralized policy enforcement with audit trails
  

Cons:

• Scan times can be long for large codebases
• High High Total Cost of Ownership (TCO), licensing, and infrastructure require planning
  

3. SonarQube

SonarQube was initially a code quality tool, and later the company also implemented a lightweight SAST engine focused on clean code, maintainability, and security hotspots. The company emphasizes adoption across security and DevOps pipelines, providing continuous feedback that supports secure coding practices without disrupting CI/CD workflows.

SonarQube performs rule-based static analysis across major languages and flags security issues, code smells, and duplications. Its security rules cover injection flaws, auth misuses, and hardcoded secrets, but it’s not a full replacement for dedicated AppSec tooling. 

Key Features:

• Real-time analysis during pull requests and commits.
• Out-of-the-box rules for code quality + security (OWASP Top 10 aligned).
• Quality gates for blocking merges based on thresholds.
• IDE plugins (JetBrains, VSCode) for local feedback.
• Supports monorepos and multi-language projects.
• Self-hosted or cloud deployment options.

Hands-On: Testing SonarQube with a Forked Java Web App

To test SonarQube, I ran a static analysis on the forked version of the Spring PetClinic sample, which is designed to demonstrate various features and best practices of the Spring Framework. The tool immediately flagged 26 total issues, broken down into 9 vulnerabilities and 17 code smells. It clarifies the issues by severity: critical, major, or minor, as shown in the image below:

One of the critical vulnerabilities SonarQube caught was in OwnerController.java, where it suggested replacing a persistent entity with a simple POJO or DTO object. This directly relates to insecure deserialization and object injection risks, especially relevant for REST APIs. 

Another issue in CrashController.java pointed out the use of generic exceptions without context, which weakens error handling and complicates root cause tracing in production. Also, the interface is simple to assign issues, add comments, and sort by file or rule.

Pros:

• CI-friendly with fast scan cycles
• Blends code quality and security into a single dashboard
• Good ROI for smaller teams or early-stage AppSec adoption
  

Cons:

• Less vulnerability detection vs. tools like OX
• No built-in SBOM or SCA functionality

DAST (Dynamic Application Security Testing) Tools

4. Burp Suite

Burp Suite is a DAST tool widely used by penetration testers, red teams, and AppSec engineers to find vulnerabilities in live web applications. Developed by PortSwigger, it offers a combination of manual control and automated scanning, giving teams full flexibility to simulate complex attack flows in real-world conditions.

Burp allows analysts to modify, replay, and fuzz HTTP/S requests. The Professional edition adds active scanning, session handling rules, macro support, and extensions for everything from JWT manipulation to SSRF detection.

In CI/CD, it’s typically run via CLI with headless scanning workflows, but most value still comes from targeted, interactive testing, especially around auth, token refresh, and session handling.

Key Features:

• Full interception proxy for traffic inspection and manipulation.
• Active scanner to detect XSS, SQLi, CSRF, etc.
• Intruder module for fuzzing and brute-force payloads.
• Repeater, Sequencer, and Decoder tools for testing edge cases.
• Extensions via BApp Store (e.g., for GraphQL, JWT, SSRF).
• Exportable scan reports for audit and compliance.
  

Hands-On : Reflected XSS in PHP Endpoint

While auditing one of our internal web projects using Burp Suite, I ran an active scan against user-facing routes to check for client-side input handling issues. The scanner flagged a reflected Cross-Site Scripting (XSS) vulnerability on a parameter passed to a PHP endpoint.

The payload was embedded in a query parameter and echoed back in the response without encoding or sanitization. Burp Suite highlighted this with high severity and confirmed the issue by injecting <script>alert(1)</script>. The reflected input was executed in the browser context, confirming exploitability.

The tool grouped related issues like missing CSP headers, relaxed CORS policies, and DOM-based vectors, which helped in trace systemic gaps in frontend sanitization logic. The advisory view provided exact reproduction steps, including request headers and response payloads, which helped the dev team reproduce and patch quickly.

Pros:

• Deep visibility into HTTP request/response behavior
• Ideal for testing complex auth, session, and multi-step flows
• Highly extensible via BApp Store and custom scripts
• Widely adopted; extensive documentation and community support
  

Cons:

• Not suited for automated CI workflows at scale
• Requires skilled operators to extract real value
• No source code context, DAST only

5. OWASP ZAP

OWASP ZAP (Zed Attack Proxy) is a free, open-source DAST tool built and maintained by the OWASP Foundation. It’s designed for automated security testing of web apps and APIs, and is often the first DAST tool adopted by testing teams due to its zero-cost model and flexible deployment options.

ZAP functions both as a proxy for manual testing and an automated scanner. It supports REST and SOAP APIs, spidering, fuzzing, passive/active scans, and scripting. ZAP can be deployed in CI via Docker or CLI, making it useful for automated pre-prod scanning.

Key Features:

• Automated spidering and active scanning.
• Intercepting proxy and request replay.
• API scanning with OpenAPI/Swagger support.
• Scripting via Zest, JavaScript, and Python.
• Docker container for CI pipeline integration.
• Community and plugin ecosystem.
  

Hands-On: ZAP Flags Missing HTTP Headers

I ran OWASP ZAP against a containerized healthcare application to test for baseline security misconfigurations. The scanner didn’t report any high-severity issues but flagged a few medium and low-level alerts related to missing or misconfigured HTTP headers.

The first was a duplicate X-Frame-Options header, which may lead to inconsistent behavior across browsers. OWASP ZAP recommends ensuring a single header entry to prevent potential clickjacking vectors.

The second issue involved the absence of proper X-XSS-Protection headers, which older browsers rely on to mitigate reflected XSS attacks. These weren’t business logic vulnerabilities, but they highlighted overlooked configurations that could affect client-side protections. 

Pros:

• Free and open source, no vendor lock-in
• Easy to automate via CLI and Docker
• Custom scripting makes it highly flexible
• Good community support and documentation
  

Cons:

• May be less suitable for depth in logic-aware testing (e.g., token refresh, role-based auth)
• UI and scan output can be noisy without tuning
• Limited support for modern protocols (e.g., GraphQL out of the box)

6. Invicti

Invicti (formerly Netsparker) is purpose-built for automated security testing of web applications, APIs, and microservices in CI/CD environments. It uses proof-based scanning, a feature that safely exploits some vulnerabilities to confirm they’re real and exploitable, not theoretical.

It’s positioned as a security automation layer for AppSec and DevSecOps teams that need to test hundreds of apps with minimal manual triage. Invicti also supports multi-tenant environments, centralized policy management, and SLA-based reporting, which makes it particularly effective for organizations with distributed application security ownership.

Key Features:

• Proof-based vulnerability validation to eliminate false positives.
• Deep scanning for OWASP Top 10, API misconfigurations, and CORS issues.
• Native support for Swagger/OpenAPI, GraphQL, SOAP, and REST.
• Role-based access, project grouping, and scan scheduling.
• Integrations with Jira, Azure DevOps, GitHub, Jenkins, and Slack.
• Compliance-ready reporting (PCI, HIPAA, ISO, etc).

Hands-On: Invicti Scan Finds Weak Ciphers and LFI Risks

I used Invicti to run a dynamic scan a publicly available web application, Quora. The scan surfaced several medium and high-risk findings. One confirmed vulnerability was “Weak Ciphers Enabled”, indicating support for outdated cryptographic algorithms such as TLS_RSA_WITH_3DES_EDE_CBC_SHA and other CBC-based ciphers. 

These algorithms do not meet modern security requirements and may leave encrypted communications vulnerable to downgrade attacks or brute-force attempts.

In addition, the scan flagged several common misconfigurations like User Controllable Cookies, Local File Inclusion (Probable), and missing security flags such as HttpOnly. Invicti’s breakdown by issue type and severity helped me understand how these weaknesses are grouped, especially under categories like transport layer security, form validation, and insecure HTTP headers.

Pros:

• Suited for large-scale, multi-team DAST automation
• Proof-based findings build trust with testing teams
• Strong CI/CD integration and policy enforcement workflows
• API coverage is better than most commercial DAST tools

Cons:

• Expensive, designed for mid-to-large enterprise budgets
• Limited value in small teams or where manual testing dominates
• Less granular control for testing custom auth flows or chained logic

7. StackHawk

StackHawk is a DAST tool designed for integration into security and DevOps pipelines. It treats dynamic testing as a repeatable unit test, running it in staging, preview branches, or even during local development with the same YAML config.

It supports OpenAPI, GraphQL, and SOAP definitions out of the box and integrates seamlessly with Docker-based test environments. The CLI-driven workflow is designed for DevSecOps teams that want automated security coverage without leaving the build system.

StackHawk also emphasizes exploitability. When a vulnerability is found, it provides a replayable request with a stack trace (when IAST is enabled via HawkScan agent), making it easier for appsec managers to triage and fix.

Key Features:

• CI-native: run security scans via GitHub Actions, GitLab CI, Jenkins, CircleCI.
• OpenAPI and GraphQL spec-based scanning.
• Easy configuration via stackhawk.yml.
• Replayable requests with curl examples.
• Optional HawkScan IAST agent for additional code context.
• Integrations with Slack, Jira, and Snyk for alerting and workflow automation.

Hands-On: StackHawk Flags SQL Injection in Spring Ap

I configured StackHawk to run a security scan on a local Java Spring Boot application deployed at http://localhost:9000, integrated via GitHub Actions. The application, named JavaSpringVulny, returned several findings categorized by severity.

The scan identified 6 high-, 17 medium-, and 12 low-severity issues. Among the high-priority findings was an SQL Injection, flagged through SAST analysis. This indicates that the application may be vulnerable to direct user input being embedded in SQL queries without proper sanitization.

Compound issues included Proxy Disclosure, Parameter Tampering, and the absence of a Content Security Policy (CSP) header. Each of these findings reflects potential risks in request handling and browser-side protection, common in web applications with insufficient hardening.

The report also highlighted cookie-related misconfigurations such as missing Secure, HttpOnly, and SameSite flags. While these were marked as low severity, they still represent best-practice gaps that can affect session management and CSRF mitigation.

Pros:

• Designed for developer ownership and fast feedback
• Excellent support for OpenAPI and GraphQL-first workflows
• Easy to integrate and scale in modern CI environments
• Detailed request/response info for fast debugging
  

Cons:

• Not ideal for non-technical users or manual penetration testers
• No built-in proxy interface for exploratory testing
• IAST support is still maturing (limited language coverage)

SCA (Software Composition Analysis) Tools

8. Contrast Assess

Contrast Assess is an IAST tool that also covers some aspects of SCA by analyzing vulnerabilities at runtime through instrumentation. It sets lightweight agents into your application during integration or functional testing. These agents observe actual code execution, data flows, and library usage to identify vulnerabilities in custom code and third-party components, with significantly higher accuracy than static scanners.

Where traditional SCA tools flag every vulnerable dependency, Contrast flags only what is actually loaded and reachable in runtime, drastically reducing false positives. It fits best in teams that already have functional test coverage and want precise AppSec feedback with minimal manual tuning.

Key Features:

• Runtime visibility into application behavior via instrumentation agents.
• Tracks execution paths to verify the actual exploitability of vulnerabilities.
• Detects both code-level and third-party library issues.
• Supports Java, .NET, Node.js, Python, Ruby (language coverage still evolving).
• Integrates with CI/CD and observability stacks.
• Tester-focused UI with remediation suggestions.

Hands-On: Runtime Scan with Contrast Flags

I ran Contrast Assess on a Java-based demo application called webgoat-video, which is commonly used to test and demonstrate real-world vulnerabilities. The goal was to observe how runtime analysis tools handle active routes and uncover risks in a live application environment.

During the scan, Contrast detected 6 vulnerabilities. The most severe was an SQL Injection triggered through the account parameter on the /SqlInjection/attack5a endpoint. Since this input was directly used in a query without proper sanitization, it was flagged as critical.

I found three medium-severity issues. The session cookie in Response.java lacked the HttpOnly flag, exposing it to JavaScript access. A hardcoded password was present in SpringInputPasswordFieldAttrProcessor.java, which poses both security and maintenance risks. And the GranteeManager used the outdated MD5 algorithm, flagged due to known vulnerabilities.

Lastly, there was an informational alert about missing anti-caching controls, which could lead to sensitive data being stored in the browser cache.

Pros:

• High signal-to-noise ratio, runtime context drastically reduces false positives
• Combines SAST, DAST, and SCA into a single agent-based workflow
• Minimal configuration, just run your tests to get results
• Real usage visibility is a game-changer for teams with mature pipelines
  

Cons:

• Requires decent test coverage to be effective
• Agent integration adds runtime overhead (small but non-zero)
• Language and framework support is more limited than other tools

SCA (Software Composition Analysis) Tools

9. Snyk

Snyk is a developer-first SCA tool designed to integrate directly into development workflows, IDEs, Git platforms, containers, and CI/CD pipelines. It focuses on helping Appsec engineers identify and fix vulnerabilities in open-source dependencies, Docker images, IaC files, and more.

Snyk integrates easily with GitHub, GitLab, and Bitbucket, providing real-time scanning of PRs and proactive security suggestions based on your actual dependency graph. It also offers automatic fix PRs, version upgrades, and license compliance checks.

The platform has expanded into container security, code scanning (SAST), and cloud security (IaC), but its core strength remains scalable and actionable SCA that gives security and DevOps teams clear ownership of risk

Key Features:

• Dependency scanning for NPM, Maven, Go, PyPI, RubyGems, etc.
• Real-time feedback in PRs with fix suggestions.
• Vulnerability database maintained by Snyk’s research team.
• CLI, IDE, and Git integration.
• License risk management.
• API and container image scanning (Docker, Podman).

Hands-On: Open Source Risk in Node.js App Flagged by Snyk

To evaluate Snyk, I used OWASP Juice Shop as the target application. Juice Shop is an intentionally vulnerable Node.js-based web app commonly used for testing and training in application security.

The Snyk scan detected multiple open-source dependency vulnerabilities listed in the package.json file. One high-severity issue was an uninitialized memory exposure in the base64url package, introduced via transitive dependencies like jsonwebtoken and express-jwt. Another critical issue it found was a previous undetected exception vulnerability in socket.io.

These results reflect realistic risks developers face when relying on unverified third-party packages. Snyk’s interface helped trace the origin of each issue, confirm exploit maturity, and recommend fixed versions, making it practical for managing vulnerabilities directly from the dependency tree.

Pros:

• Fast, clean developer experience, especially in PRs and CLI
• Fix suggestions are usually actionable and low-effort
• Great ecosystem coverage: containers, IaC, code, packages
• SaaS-first, low setup time
  

Cons:

• Doesn’t always detect reachability or real usage like Contrast or OX
• Alert fatigue for monorepos with large dependency graphs
• Premium tiers required for organization-wide policy enforcement and reporting

10. Mend.io (formerly WhiteSource)

Mend.io is a compliance-grade SCA platform focused on enterprise-scale software supply chain management. It performs deep scans of source code, artifacts, containers, and SBOMs to identify vulnerabilities, license violations, and open-source usage anomalies. Mend.io also offers automated remediation suggestions and integrates with the most popular CI tools, ticketing systems, and container registries.

Key Features:

• Comprehensive SCA and license compliance analysis.
• SBOM generation and third-party inventory management.
• Policy engine for blocking builds or PRs based on risk.
• Automated remediation suggestions and close pull requests.
• Integration with GitHub, GitLab, Azure DevOps, Jira, Artifactory.
• Custom compliance rules for specific frameworks or libraries.

Hands-On: Node App Scan with Mend.io (CVSS 9.8 EJS Issue)

When I ran a security scan on one of our Node.js applications using Mend.io, the report flagged 15 vulnerabilities, ranging from critical to medium severity. It mapped each one to its exact location in the code, along with the dependency hierarchy and fix suggestions.

For example, I saw a critical vulnerability (CVSS 9.8) in the ejs package (ejs-2.7.4.tgz), which was buried inside node_modules. Mend clearly pointed out that it stemmed from /package.json, and that upgrading to ejs@3.1.6 or later would resolve the issue.

Pros:

• Enterprise-grade license and compliance enforcement
• Centralized risk visibility across orgs and business units
• Scales well with large teams and multi-repo environments
• SBOM management and export support (CycloneDX, SPDX)
  

Cons:

• Heavy setup, better suited for orgs with dedicated AppSec or compliance teams
• Developer UX is clunkier than tools like OX
• Remediation guidance can feel generic because it lacks context
• Licensing cost and complexity can be a blocker for small teams

Side-by-Side Comparison Table For 10 Tools

Here’s a table of comparison, in case you need to skim it fast for a quick comparison of the tools:

OX Security for Enterprise AppSec

Security teams struggle with vulnerability findings that can’t be traced back to where they originated. A CVE surfaces in a production container, but no one knows which dependency introduced it, which pipeline stage missed it, or which developer owns the fix. By the time the finding is actionable, it has already passed through code review, CI, and deployment undetected.

OX is built around tracing every risk back to its exact line of code, from AI coding tools like Cursor and Copilot through CI/CD pipelines, artifacts, and into cloud runtime. The PBOM tracks every component, dependency, and configuration change across that entire journey in real time, giving security teams the context to know not just what is vulnerable, but where it was introduced, whether it is reachable in production, and who is responsible for fixing it.

Conclusion

Choosing the right AST tool depends on your preferences and requirements. Different tools serve different layers of the SDLC; some excel at static analysis, others at open-source risk, CI/CD posture, or runtime insights. 

The tools I covered in this blog each offer something valuable depending on your team’s maturity, stack, and threat model, and I personally like using OX Security as it focuses attention on the 5% that are actually exploitable and relevant.

For AppSec managers trying to cut through alert fatigue and drive meaningful remediation, that kind of context-driven prioritization is a major advantage. Ultimately, the right combination of tools depends on what you’re solving for, but if you’re looking to tie risk back to code, cloud, and pipeline in a single view, OX is worth serious consideration.