The Role of LLMs in Application Security Tooling

Large language models are being integrated into security tools across the industry. Some applications are genuinely useful. Others are marketing-driven additions that reduce reliability. Understanding the distinction matters.

Where LLMs add value

Explaining findings

LLMs excel at translating technical findings into plain-language explanations. A deterministic detector identifies the vulnerability. An LLM explains what it means, why it matters, and how to fix it in terms the developer understands.

Generating remediation suggestions

Given a vulnerable code pattern and its context, LLMs can suggest specific fixes. This is more useful than linking to a generic remediation guide because the suggestion accounts for the project's language, framework, and coding patterns.

Triaging and prioritizing

LLMs can assess the exploitability of a finding by considering its context: is the vulnerable code reachable? Is it behind authentication? Is the input user-controlled? This contextual triage helps teams focus on findings that matter.

Summarizing scan results

For teams that run scans with dozens of findings, LLMs can generate executive summaries that highlight the most important issues and patterns across the result set.

Where LLMs fall short

Primary detection

LLM-based detection is non-deterministic. The same code can produce different findings on consecutive runs. This breaks reproducibility requirements in CI pipelines and makes it impossible to guarantee that a suppressed false positive stays suppressed.

Policy enforcement

Gate decisions (pass/warn/fail) must be deterministic and auditable. An LLM that says "this looks okay" on one run and "this looks risky" on the next is not suitable for automated enforcement.

Handling adversarial input

LLMs can be prompt-injected through code comments, variable names, or string literals. In a security scanning context, this means an attacker could craft code that convinces the LLM to suppress a finding.

The right architecture

Use LLMs downstream of deterministic detection:

1. Deterministic detectors identify findings with stable, reproducible rules.
2. LLM enrichment adds explanations, context, and remediation suggestions.
3. Policy engine makes gate decisions based on deterministic findings, not LLM output.

This gives teams the benefit of AI-powered developer experience without compromising the reliability of the scanning pipeline.