What Is Application Security Testing? SAST, DAST, and SCA Explained

What Is Application Security Testing? Types, Tools, and How to Cut the Noise

Your scanners flag thousands of issues each quarter, and your team can fix a fraction of them. Application security testing (AST) checks an application for security weaknesses across its code, open source dependencies, containers, and secrets.

Application security testing (AST) checks an application for security weaknesses across its code, open-source dependencies, containers, and secrets, before and after release.

Two words come up a lot in this field. A vulnerability is a flaw in software that could be misused. An exploit is the method an attacker uses to turn that flaw into real damage.

The stakes are easy to measure, and you do not need scare tactics to see them. Nearly a third (31%) of all breaches start with vulnerability exploitation, the first time in 19 years it has surpassed stolen credentials as the biggest point of entry, according to the Verizon 2026 DBIR.

Application security testing spans several methods, and each one looks at a different layer of your app. Together they cover the code you write, the open-source code you borrow, the containers you ship, and the secrets that tie it all together.

Here is the part most guides skip. Finding weaknesses was never the hard part, because modern scanners find plenty of them. The hard part is finding the ones that matter.

What are the main types of application security testing?

No single method catches everything, so strong programs layer several. Those methods include static, dynamic, and interactive application security testing (SAST, DAST, and IAST). They also include software composition analysis (SCA), plus secret, container, and API (application programming interface) scanning.

The table below compares the main types. It adds a lens most vendor tables leave out: whether the method can tell exploitable risk apart from noise.

How does SAST fit into application security testing?

SAST reads source code or binaries without running the application. It catches problems early, and it is good at spotting injection flaws and hardcoded secrets.

It is also noisy. SAST tends to over-report, and it can miss real issues too. Our research shows traditional SAST misses 47–80% of vulnerabilities while flooding teams with alerts, a pattern we cover in false negatives in SAST.

What is dynamic application security testing (DAST)?

DAST tests the running application from the outside, the way an attacker would. It needs no source code, and it finds runtime and configuration issues.

It runs later in the cycle, so it can miss the code-level root cause of a problem. That is why it pairs well with methods that read the code directly.

What is interactive application security testing (IAST)?

IAST instruments the application while it runs during testing. That combines code visibility with runtime behavior, so its results tend to be more precise.

The trade-off is setup. You need a running, instrumented app, which takes more effort than a plain code scan.

How does SCA test open source dependencies?

SCA finds known vulnerabilities, license problems, and malware in open-source and third-party dependencies. Since most modern codebases are mostly open source, this is a large risk surface.

The catch is accuracy. Manifest-only SCA reads dependency files and over-reports, so reachability-based SCA is worth the switch. We expand on reachability in its own section below.

What do secret, container, and API testing cover?

Three more checks round out the picture. Secret scanning finds hardcoded keys, tokens, and passwords. Container scanning inspects image layers, and API security testing probes the endpoints your app exposes.

These usually come from separate tools. Correlating them into one view, along a real attack path, tells you far more than three siloed reports.

Why does traditional application security testing create so much noise?

The core failure of legacy application security testing is volume without context. Scanners surface thousands of findings, but few come with proof of real risk.

Older tools flag every possible issue and rank it by CVSS, the Common Vulnerability Scoring System. That treats findings as guilty until proven innocent, so teams spend hours on alerts that carry no real danger.

The imbalance is stark. Most listed findings sit on code paths that an attacker can never reach. Sorting the exploitable few from the theoretical many is the work that reduces risk.

The cost is measurable. Half of surveyed developers said they spend an estimated 19% of their weekly hours on security-related tasks, according to the IDC InfoBrief.

And volume does not mean coverage. Our research on SAST shows it can miss 47–80% of real bugs while still over-reporting, so more alerts can mask weaker security.

How does reachability analysis cut testing noise?

Reachability analysis asks a sharper question. Does a real execution path let an attacker reach the vulnerable code? If nothing can reach the flaw, it is not an urgent risk.

Think of it like home security. You do not worry about every door in the neighborhood, only the unlocked ones that lead into your house.

• Key point: Reachability separates exploitable risk from theoretical risk, so teams fix what matters first.

This matters because most vulnerabilities are never used. Only 1.5–2.0% of reported vulnerabilities are exploited each year, per J.P. Morgan analysis.

A call graph is a map of which functions call which other functions across your app. Full stack reachability builds that graph across your code, dependencies, and container layers to find the paths that truly matter.

Our data shows fewer than 9.5% of open-source vulnerabilities are exploitable at the function level. In practice, full stack reachability cuts false positives by up to 90%, and reachability-based SCA reduces dependency noise by more than 92%.

How do you prioritize open source vulnerabilities?

Most application code today is open source, and most of that risk hides in transitive dependencies. Those are the packages your packages pull in, often without you noticing.

Manifest-only scanning misses phantom dependencies and over-reports the rest. Reachability plus context works better, because it asks the practical questions.

Is the code in production, is a fix available, and does it have a high EPSS score? EPSS is the Exploit Prediction Scoring System, which estimates the chance a flaw will be attacked.

The problem is widespread. 59% of Java services contain at least one known exploitable vulnerability, according to Datadog State of DevSecOps 2026.

The lesson is not to patch everything. It is to see which risky dependencies your code calls, then fix those first. Everything else can wait for a normal maintenance window.

How do you test AI-generated code?

Artificial intelligence (AI) coding assistants ship more code, faster. They also introduce more risk, and they can generate findings that are not real.

Two things need testing now: the AI-written code itself, and the dependencies it pulls in. Our State of Dependency Management 2025 research found that 80% of AI-suggested dependencies contain risks. Only 1 in 5 recommended versions are safe, and 34% are hallucinated packages that do not exist.

Hallucinated packages are their own hazard. Attackers watch for common fake names, then publish real malicious packages under them. A developer who trusts an AI suggestion can install one by accident, so AI-written code needs the same checks as anything else.

Today the bottleneck is validation rather than discovery. Comcast makes the point in a frontier-AI security assessment. Of critical- and high-severity findings from frontier-AI security assessments that completed validation, 56% were confirmed vulnerabilities and 44% were false positives.

That is why Endor Labs analyzes AI-written code with data-flow analysis, not just hand-written rules. It reviews AI-generated code the same careful way it reviews the rest of your stack.

How do you fix findings without breaking the build?

Testing only helps if fixes happen. The best programs prioritize by exploitability first, then make each fix easy to trust.

Start with safe upgrades. Upgrade impact analysis shows exactly what changed between versions, so you can pick safe upgrade paths that will not break your build.

When upgrading is not an option, Endor Magic Patches apply backported security fixes to the vulnerable version, so you can fix on your own schedule. That beats handing a developer a 500-item list and saying "good luck."

Evidence-based remediation also builds trust with engineers. When a fix arrives with proof that the flaw is reachable and a tested upgrade path, developers act on it. When it arrives as a raw severity score, they push back, and rightly so.

What are application security testing best practices?

A strong program is less about buying more tools and more about trusting the signal you already have. Treat these as steps you can adopt in order. When you compare vendors, evaluating an SCA tool on its reachability and context handling tells you more than any feature checklist.

1. Layer multiple methods so code, dependencies, containers, and secrets all get covered.
2. Demand accurate signal before you buy application security posture management, because dashboards only help when the underlying scanners are precise.
3. Prioritize by reachability and exploitability, not by CVSS score alone.
4. Integrate testing into CI/CD and the developer workflow so fixes land in context.
5. Track outcome metrics like reachable-critical backlog, false-positive rate, and mean time to remediate (MTTR).
6. Generate a software bill of materials (SBOM) and a Vulnerability Exploitability eXchange (VEX) record so auditors get clear evidence.

An SBOM lists every component in your app, and a VEX record notes which listed vulnerabilities affect you.

Frequently Asked Questions

What Is the Difference Between SAST, DAST, and SCA?

SAST reads your source code at rest, DAST tests your running app from the outside, and SCA checks the open-source dependencies your app relies on.

Is Manual Penetration Testing Still Necessary With Automated Application Security Testing?

Yes; automated testing gives broad, repeatable coverage, while skilled human pen testers find business-logic and chained flaws that tools still miss.

When and How Often Should You Run Application Security Testing?

Run it continuously inside CI/CD so every code change is checked, and add deeper scans and manual testing before major releases.

How Should Teams Prioritize Application Security Testing Findings?

Prioritize by what is reachable and exploitable in your environment, then weigh exposure and available fixes, rather than sorting by CVSS score alone.

Does Application Security Testing Cover AI-Generated Code?

It should; treat AI-written code and its dependencies like any other input, and validate whether each finding is exploitable before acting.

How do you get started with application security testing?

Modern application security testing layers methods across the full stack, then prioritizes by what is exploitable. The payoff is fewer, safer fixes and far less wasted time.

Here is a simple way to start:

1. Audit your current tools for reachability, and note how much of your backlog is truly exploitable.
2. Pilot reachability-based prioritization on one high-noise repository, then measure the drop in false positives.
3. Compare your options against coverage and signal quality, using our roundup of application security tools.

That is what "Code without compromise" means in practice: teams ship faster without treating every alert as equally urgent.

Ready to see it on your own code? Book a demo.