# When to use SAST versus an LLM security scanner

DevFeed: [When to use SAST versus an LLM security scanner](<https://devfeed.tech/articles/when-to-use-sast-versus-an-llm-security-scanner-31474.md>)

Original publisher: [Read original article](<https://about.gitlab.com/blog/sast-vs-llm-security-scanner/>)

Author: Chris Widstrom

Published: 2026-09-16T00:00:00Z

Content type: comparison

Language: en

Sources: [GitLab](<https://devfeed.tech/sources/gitlab.md>)

Topics: [Security](<https://devfeed.tech/topics/security.md>), [LLM security](<https://devfeed.tech/topics/llm-security.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Application Security](<https://devfeed.tech/topics/application-security.md>), [Testing](<https://devfeed.tech/topics/testing.md>), [Frontier Model](<https://devfeed.tech/topics/frontier-model.md>), [audit trail](<https://devfeed.tech/topics/audit-trail.md>), [business logic](<https://devfeed.tech/topics/business-logic.md>)

Tags: [application-security](<https://devfeed.tech/tags/application-security.md>), [audit-trail](<https://devfeed.tech/tags/audit-trail.md>), [features](<https://devfeed.tech/tags/features.md>), [frontier-model](<https://devfeed.tech/tags/frontier-model.md>), [llm](<https://devfeed.tech/tags/llm.md>), [llm-security](<https://devfeed.tech/tags/llm-security.md>), [product](<https://devfeed.tech/tags/product.md>), [sast](<https://devfeed.tech/tags/sast.md>), [security](<https://devfeed.tech/tags/security.md>), [vulnerabilities](<https://devfeed.tech/tags/vulnerabilities.md>)

## AI overview

This comparison explains when to use static application security testing (SAST) and when to use LLM-based security review. SAST provides predictable, repeatable, low-cost scanning across commits, while LLMs can reason about context and identify some intent-based or novel issues. The article recommends using both according to the task and scan scope.

## Source excerpt

You're probably running some version of this experiment already: Point a frontier model at a merge request and ask it to double as a vulnerability scanner. On a single merge request, it often works well. The model reads the code, reasons about what it's supposed to do, and catches real issues, sometimes ones a pattern-based scanner misses entirely. So the next thought is reasonable: If a model reviews one merge request this well, why not let it replace the scanner across your whole pipeline? That's where it breaks down. Running a frontier model as your primary scanner on every commit across an enterprise codebase costs more and behaves less predictably, than asking it to review one merge request. If you run application security for a platform or product team, the decision was never mutually exclusive: static application security testing (SAST) or LLM scanners. It's which one runs where. Get that split right and you keep deterministic coverage on every commit, add reasoning where it earns its place, and avoid both an unpredictable inference bill and gaps in your audit trail. Here's how the two compare: Deterministic scanner (SAST)AI-based review (LLM)CostBetter Fractions of a cent per scan, fixed and predictable. A paid inference call per scan; cost scales with code volume and is hard to forecast.ConsistencyBetter Flags the same vulnerabilities, every time. Can flag or miss different vulnerabilities on the same code, run to run.Audit evidenceBetter Reproducible: same fixed method every time, mapped to a CWE Output can vary between runs, harder to use as standalone audit evidence.Vulnerability typesDepends on the vulnerability type Pattern-based, traceable flaws (injection)Depends on the vulnerability type Intent-based and novel flaws: missing auth checks, broken ownership logic, and issues not yet mapped to a CVE or CWE. Could reason about business logic using context from issues, epics, and docs.Validating a finding Flags potential vulnerabilities; can't confirm whe