Are Deterministic SAST Security Tools Enough for AppSec?
SAST security tools, Static Application Security Testing that scans source code, bytecode, and binaries for vulnerabilities before code is deployed, have long been the backbone of application security (AppSec). But recent advances in AI are exposing gaps traditional scanners can’t cover. The release of Mythos and Project Glasswing gave security teams a much clearer picture of the risks associated with AI. And the risk is growing: per Veracode’s 2026 GenAI Code Security Report, 44% of AI code generation tasks introduce detectable security vulnerabilities. The most striking gap? AI can now detect business logic vulnerabilities, which has been a blind spot for traditional program analysis for years. At the same time, agents can write their own exploits. That makes the case for Hybrid SAST hard to ignore.
Why CWE Coverage Alone Isn’t Enough
The concerns about LLMs creating exploits are serious. But here’s the thing: those exploits rely on CWEs.
With program analysis built on reliable deterministic scanning, you already have CWE coverage. If malicious AI exploiting CWEs is a threat to you, it’s because you can’t roll out fixes fast enough. Veracode Fix uses AI to generate patches to help engineers accelerate remediation.
What About Business Logic Vulnerabilities?
The other finding, that business logic vulnerabilities are now scannable, is a different problem entirely. Business logic vulns aren’t new. They just never showed up in scans.
Now they do. Contextually aware AI is detecting IDORs, auth bypass, and other logic vulnerabilities. The urgency is simple: if you can find these with a scan now, so can your adversaries.
The Simplest Argument for Hybrid SAST Security Tools
The simplest argument for hybrid SAST is that AI has proven it can exploit vulnerable code. The two vectors businesses are most exposed to are business logic vulns that go undetected and CWEs that get detected but never patched. To harden against potentially malicious AI, security programs have to find and fix faster. Hybrid SAST with automated remediation is how you do that. For a deeper look at how LLM code security compares to hybrid SAST, see our related post on LLM code security vs. hybrid SAST.
How Veracode SAST + DryRun AI-SAST Works
Veracode SAST with DryRun AI-SAST makes Hybrid SAST practical to deploy in your environment. DryRun is the inaugural partner in our new marketplace program. They bring contextual, LLM-based risk detection. Veracode brings compliance-ready static analysis and a suite of tools covering third-party, governance, and more. Together they catch what traditional scanners miss, including logic flaws, IDORs, and broken authentication.
Developers get faster feedback right in their pull requests through GitHub or GitLab, so they can fix issues before merging without slowing down delivery. Security and compliance teams get repeatable, audit-ready evidence with reporting, policy management, and findings mapped to major regulatory frameworks. It makes program management simpler too. One contract, one governance model, broader codebase visibility, and less operational overhead from custom rules or tuning.
Sign up here to get started with hybrid SAST security tools from Veracode and DryRun: https://www.veracode.com/solutions/marketplace/veracode-dryrun/
FAQ: SAST Security Tools
What are SAST security tools? SAST security tools (Static Application Security Testing) scan source code, bytecode, or binaries for vulnerabilities before code is deployed, giving developers early, deterministic feedback on weaknesses like CWEs.
Are SAST security tools enough on their own? Traditional SAST security tools reliably detect CWEs but miss business logic vulnerabilities such as IDORs and auth bypass. Hybrid SAST adds contextually aware AI to close that gap.
What is hybrid SAST? Hybrid SAST combines deterministic static analysis with LLM-based AI detection to find both traditional code weaknesses and business logic flaws, plus automated remediation to fix them faster.
How does hybrid SAST detect business logic vulnerabilities? Contextually aware AI models analyze code intent and data flow to spot logic flaws, IDORs, and broken authentication that rule-based scanners cannot identify.
How do I get started with hybrid SAST? Veracode SAST with DryRun AI-SAST delivers hybrid scanning with PR-time feedback and compliance-ready reporting. Sign up here.