Application Security Testing Tools: Build a Complete AppSec Stack
Written by the Rafter Team
Application security testing tools scan your code, dependencies, and running applications for vulnerabilities before attackers find them first. The challenge isn't whether you need them — it's choosing the right combination from dozens of overlapping tools, each covering a different slice of the attack surface.
Most teams start with one tool and discover gaps the hard way: a hardcoded API key that SAST missed because it only checks code patterns, a vulnerable dependency that DAST never saw because it tests runtime behavior, or an injection flaw that SCA can't detect because it only tracks package versions. A complete AppSec testing stack requires multiple categories working together.
No single application security testing tool covers every vulnerability class. Teams relying on one category of scanner are missing entire attack surfaces — the average application has vulnerabilities across code, dependencies, and runtime behavior simultaneously.
Try Rafter free — scan your code for vulnerabilities in under two minutes.
Categories of Application Security Testing Tools
Application security testing tools fall into five core categories. Each one targets a different phase of the development lifecycle and catches a different class of vulnerability.
Static Application Security Testing (SAST)
SAST tools analyze source code without executing it. They parse your codebase, build data-flow models, and flag patterns that lead to vulnerabilities — SQL injection, cross-site scripting, path traversal, insecure cryptography, and broken access controls.
Strengths: Finds bugs early (before code even compiles), pinpoints the exact line of vulnerable code, covers every code path regardless of test coverage.
Limits: Cannot detect runtime issues like misconfigured servers or broken authentication flows. Produces false positives when it cannot resolve dynamic values. Blind to vulnerabilities in third-party binaries.
Top tools: Semgrep, CodeQL, SonarQube, Checkmarx, Rafter
For a deeper dive into static analysis, see our SAST static analysis guide.
Dynamic Application Security Testing (DAST)
DAST tools test running applications from the outside, simulating how an attacker would interact with your endpoints. They send crafted requests, observe responses, and identify vulnerabilities that only manifest at runtime.
Strengths: Finds real exploitable vulnerabilities in deployed applications. No source code access required. Catches server misconfigurations, authentication bypasses, and response-header issues that static analysis cannot see.
Limits: Only tests reachable endpoints — anything behind complex auth flows or feature flags may be missed. Cannot pinpoint the vulnerable line of code. Slower than SAST because it requires a running application.
Top tools: OWASP ZAP, Burp Suite, Nuclei, StackHawk, Invicti
Software Composition Analysis (SCA)
SCA tools map your dependency tree and cross-reference every package against known vulnerability databases (NVD, GitHub Advisory, OSV). They flag libraries with active CVEs, track license compliance, and detect when transitive dependencies introduce risk.
Strengths: Catches vulnerabilities in code you didn't write — which makes up 70–90% of most applications. Tracks transitive dependencies that developers don't even know exist. Provides actionable upgrade paths.
Limits: Only detects known vulnerabilities with published CVEs. Cannot find zero-days or logic flaws in dependencies. May flag vulnerabilities in code paths your application never actually calls.
Top tools: Snyk, Dependabot, Trivy, Grype, OWASP Dependency-Check
Secrets Detection
Secrets detection tools scan your repository history, configuration files, and environment templates for hardcoded credentials — API keys, database passwords, tokens, private keys, and connection strings that should never be committed.
Strengths: Catches credentials before they reach a public or shared repository. Scans git history, not just the current commit. Detects secrets in file types that other scanners skip (YAML, JSON, .env templates, Dockerfiles).
Limits: Cannot verify whether a detected secret is still active or has been rotated. Pattern-based detection may miss custom credential formats. High-entropy string detection can produce false positives.
Top tools: GitLeaks, TruffleHog, detect-secrets, GitHub Secret Scanning, Rafter
Interactive Application Security Testing (IAST)
IAST tools instrument your application at runtime, combining elements of SAST and DAST. They monitor data flow through the running application and flag vulnerabilities with full stack traces and code-level context.
Strengths: Low false positive rates because it observes actual runtime behavior. Provides precise remediation guidance with exact code locations. Works during QA and integration testing without extra scan steps.
Limits: Requires instrumentation agents installed in the application runtime, which adds complexity and can impact performance. Language support is narrower than SAST or DAST. Not suitable for production monitoring.
Top tools: Contrast Security, Hdiv (now part of Datadog), Seeker
Application Security Testing Tools Comparison
This matrix shows what each category finds, when it runs, and where it falls short — so you can identify the gaps in your current setup.
| Category | What It Finds | When It Runs | Runtime Required | Typical Speed | Key Limitation |
|---|---|---|---|---|---|
| SAST | Code-level flaws (injection, XSS, crypto) | Pre-commit / CI | No | Seconds–minutes | Misses runtime and config issues |
| DAST | Exploitable runtime vulnerabilities | Staging / QA | Yes | Minutes–hours | Cannot pinpoint source code lines |
| SCA | Known CVEs in dependencies | CI / scheduled | No | Seconds | Only finds published vulnerabilities |
| Secrets | Hardcoded credentials and tokens | Pre-commit / CI | No | Seconds | Cannot confirm if secrets are active |
| IAST | Runtime data-flow vulnerabilities | QA / integration | Yes (instrumented) | Real-time | Requires agent instrumentation |
No single row covers the full attack surface. SAST finds the code bugs that DAST cannot attribute. DAST finds the runtime flaws that SAST cannot simulate. SCA covers the supply chain. Secrets detection catches the credentials that all other categories ignore. A production-grade AppSec stack needs at least three of these five categories.
Building Your Application Security Testing Stack
The right combination of tools depends on your team size, deployment model, and risk tolerance. Here are three proven configurations.
Starter Stack (Solo Developer or Small Team)
- SAST — Semgrep (open-source rules) or Rafter (zero-config)
- SCA — Dependabot (built into GitHub) or Trivy
- Secrets — GitLeaks pre-commit hook
This covers the three most common vulnerability classes with minimal setup. You catch injection flaws in your code, known CVEs in your packages, and credentials that slip into commits.
Growth Stack (10–50 Developers)
- SAST — Rafter or Semgrep with custom rules
- SCA — Snyk or Trivy with CI integration
- Secrets — TruffleHog or Rafter in CI pipeline
- DAST — OWASP ZAP against staging environment
Adding DAST fills the runtime gap. Custom SAST rules let you enforce organization-specific patterns — blocking internal API calls from public endpoints, enforcing input validation on user-facing routes.
Enterprise Stack (50+ Developers, Compliance Requirements)
- SAST — Checkmarx or CodeQL with policy-as-code
- SCA — Snyk with license compliance and reachability analysis
- Secrets — TruffleHog Enterprise with automatic rotation alerts
- DAST — Invicti or Burp Suite Enterprise with authenticated scanning
- IAST — Contrast Security during QA cycles
Enterprise stacks add IAST for precision, compliance reporting for auditors, and authenticated DAST scanning to cover endpoints behind login flows.
Integrating Application Security Testing into CI/CD
The most effective application security testing runs automatically on every pull request. Here is a GitHub Actions workflow that combines SAST, SCA, and secrets detection in a single pipeline:
name: Security Scan
on:
pull_request:
branches: [main]
jobs:
security:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
with:
fetch-depth: 0
- name: SAST Scan
uses: semgrep/semgrep-action@v1
with:
config: p/security-audit
- name: Dependency Scan
uses: aquasecurity/trivy-action@master
with:
scan-type: fs
severity: CRITICAL,HIGH
- name: Secrets Scan
uses: gitleaks/gitleaks-action@v2
This runs all three scans in parallel on every pull request. Critical findings block the merge. You get results before the code review even starts.
Running security scans in parallel — not sequentially — keeps your pipeline fast. Three 30-second scans running concurrently still finish in 30 seconds, not 90.
The Simpler Alternative
If configuring three separate tools sounds like overhead, Rafter runs SAST, secrets detection, and SCA in a single integration. Connect your GitHub repository, and scanning starts on every pull request — no YAML to write, no rulesets to maintain.
name: Rafter Security
on:
pull_request:
branches: [main]
jobs:
scan:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- name: Rafter Scan
uses: rafter-security/scan-action@v1
with:
api-key: ${{ secrets.RAFTER_API_KEY }}
One step replaces three. Results appear as inline PR comments with fix suggestions.
Start scanning with Rafter — one integration covers SAST, secrets, and dependency checks.
Top Application Security Testing Tools by Category
SAST Tools
| Tool | Languages | Pricing | CI Integration | Best For |
|---|---|---|---|---|
| Semgrep | 30+ | Free (OSS) / Commercial | GitHub, GitLab, CI/CD | Custom rules, fast scans |
| CodeQL | 8 | Free (public repos) | GitHub native | Deep semantic analysis |
| SonarQube | 30+ | Free (CE) / Commercial | Most CI systems | Code quality + security |
| Checkmarx | 30+ | Enterprise | All major CI | Compliance-driven teams |
| Rafter | 15+ | Free tier / Commercial | GitHub native | Zero-config, AI-code aware |
SCA Tools
| Tool | Ecosystem Coverage | Pricing | Reachability Analysis | Best For |
|---|---|---|---|---|
| Snyk | npm, PyPI, Maven, Go, etc. | Freemium | Yes | Full-featured SCA |
| Dependabot | npm, PyPI, Maven, Go, etc. | Free (GitHub) | No | GitHub-native teams |
| Trivy | OS packages, languages, IaC | Free (OSS) | No | Container + code scanning |
| Grype | OS packages, languages | Free (OSS) | No | Lightweight CLI scanning |
DAST Tools
| Tool | Pricing | Auth Support | API Scanning | Best For |
|---|---|---|---|---|
| OWASP ZAP | Free (OSS) | Basic | Yes | Budget-conscious teams |
| Burp Suite | Free / Pro / Enterprise | Advanced | Yes | Penetration testers |
| Nuclei | Free (OSS) | Template-based | Yes | Template-driven scanning |
| StackHawk | Freemium | Full | Yes (OpenAPI) | Developer-first DAST |
| Invicti | Enterprise | Full | Yes | Enterprise web apps |
Common Mistakes When Choosing Application Security Testing Tools
Running only SAST and calling it done. SAST cannot find runtime vulnerabilities, dependency CVEs, or hardcoded secrets (unless the tool specifically includes secrets rules). You need at least SAST + SCA + secrets detection as a baseline.
Optimizing for coverage over signal. A tool that reports 500 findings with a 60% false positive rate creates more work than a tool that reports 50 real findings. Evaluate tools by their signal-to-noise ratio, not their feature checklist.
Treating security scanning as a gate instead of a guide. If every scan blocks the merge with zero tolerance, developers will find ways to bypass the process. Set severity thresholds — block on critical and high, warn on medium, suppress informational findings.
Ignoring AI-generated code. Code produced by AI assistants tends to use outdated patterns, weaker input validation, and insecure defaults. Your SAST tool needs rules tuned for these patterns, not just the OWASP Top 10. See our guide on AI code security for what to watch for.
AI-generated code is now a significant portion of new commits in many organizations. Application security testing tools that were trained on human-written patterns may miss the specific vulnerability classes that AI coding assistants introduce.
How Rafter Fits Your AppSec Testing Stack
Rafter covers three of the five categories — SAST, secrets detection, and SCA — in a single platform that connects to your GitHub repository and starts scanning immediately. No YAML configurations, no agent installations, no separate dashboards.
- SAST — scans source code for injection flaws, broken authentication, insecure cryptography, and patterns specific to AI-generated code
- Secrets detection — finds API keys, tokens, database credentials, and private keys across your repository and its history
- SCA — maps your dependency tree against vulnerability databases and flags packages with active CVEs
Results appear as inline comments on your pull requests with contextual fix suggestions. You set severity thresholds to control which findings block merges and which surface as warnings. Most repositories see results in 30 to 90 seconds.
For teams that also need DAST coverage, Rafter pairs well with OWASP ZAP or StackHawk for runtime testing against staging environments.
Get started with Rafter — connect your repo and scan for vulnerabilities in under two minutes.
Related Resources
- Vulnerability Scanning Guide
- SAST Static Analysis Guide
- Application Security Solution
- Security Tool Comparisons
- Automated Security Scanning
- CI/CD Security Best Practices
- AI Code Security Complete Guide
- Vulnerability Assessment Tools: 2026 Comparison
- DevSecOps Guide: Building Security Into Every Sprint