Question 1

What is an LLM-based SAST tool?

Accepted Answer

An LLM-based SAST tool leverages large language models (LLMs) to analyze your source code for security vulnerabilities, providing accurate, context-aware results that go beyond traditional rule-based scanning methods. It uses advanced AI to detect both known and emerging threats in your codebase.

Question 2

How is this different from traditional SAST solutions?

Accepted Answer

Unlike conventional SAST tools, which rely on hardcoded patterns and static rules, LLM-based SAST dynamically interprets code logic, recognizes complex vulnerabilities (such as logic and authorization flaws), and minimizes false positives. It adapts to new threats and can explain issues and remediations in natural language, making it more comprehensible for developers.

Question 3

Will this interfere with my existing CI/CD pipeline or developer workflow?

Accepted Answer

No, integration with CI/CD pipelines is seamless. The tool is built to fit naturally into existing workflows, offering automated scanning on every commit or pull request, plus dashboard-based oversight as needed. Developers can receive feedback without slowing development or disrupting standard practices.

Question 4

Is my code safe? How does the system handle privacy and security?

Accepted Answer

Your code privacy is a priority. The LLM-based SAST can operate on-premises or in a secure cloud environment, with strict access controls and compliance with industry standards (such as GDPR). No code leaves your environment without your explicit consent.

Question 5

What types of security issues does it detect?

Accepted Answer

The tool catches: Common vulnerabilities (e.g., injection, XSS, insecure deserialization); Logic and business flaws; Authorization and authentication issues; Hardcoded secrets and configuration leaks; Both known and emerging vulnerability patterns, with continuous LLM updates.

Question 6

How accurate are the results? Can it reduce false positives?

Accepted Answer

Yes. Thanks to advanced AI analysis and continuous learning, false positives are minimized compared to traditional scanners. The system can also prioritize findings by risk, helping your team focus on the most critical issues.

Question 7

Does it offer auto-remediation or code fix suggestions?

Accepted Answer

Yes. For many vulnerabilities, the tool will not only flag the issue, but also provide AI-generated fixes or remediation guidance, making it easier to resolve security risks quickly and correctly.

Question 8

Will this affect my code’s version history or repository?

Accepted Answer

No, the system does not alter your actual codebase or history. All scans and results are sandboxed or annotated separately, ensuring your repository remains clean and only approved changes are committed.

Question 9

Does it work across different programming languages and frameworks?

Accepted Answer

Absolutely. LLM-based SAST is designed to handle multiple languages and common frameworks, adapting its detection strategies accordingly for each codebase.

Question 10

How is it kept up to date with the latest threats?

Accepted Answer

The AI models are regularly updated with new threat intelligence, vulnerability patterns, and secure coding practices. This ensures you’re always protected against the latest security risks without manual rule updates.

Question 11

Can I see how it works before buying?

Accepted Answer

Yes. Most providers offer a live demo, free trial, or interactive sandbox where you can test the tool on your own code or with sample projects to see real-time results and explanations.

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