Glossary · Argus Pentest

What is DAST (dynamic application security testing)?

What is DAST (dynamic application security testing)? A clear explanation for Azerbaijani business — and how Argus Pentest applies it.

Advanced Dynamic Application Security Testing (DAST)

Dynamic Application Security Testing (DAST) is a critical security analysis methodology that evaluates running applications from an external perspective. Unlike static analysis, which examines code in isolation, DAST interacts with live web applications and their APIs to identify vulnerabilities by simulating real-world attack vectors. This ensures that security flaws are detected within the actual operational environment, accounting for configuration errors and runtime issues that static tools often miss. Within the Argus ecosystem, this capability is delivered as one of three core engines in a unified, self-hosted AI testing platform. By sharing a single console, sign-in, and encrypted credential store with the QA and AI engines, Argus provides a streamlined approach to enterprise security. The system focuses on authenticated dynamic scanning, ensuring that the security analysis penetrates beyond the login page to evaluate the actual authenticated surface of the application.

Capabilities

Strategic Advantages of Argus DAST

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Deep visibility into enterprise web applications and APIs through authenticated dynamic scanning.

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High-fidelity simulation of real user behavior using session cookies and bearer tokens to bypass authentication walls.

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Significant reduction in noise and false positives by re-checking every finding against the live target before reporting.

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Complete data sovereignty via a self-hosted architecture, keeping credentials and reports on your own infrastructure.

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Actionable intelligence delivered through severity-based reporting in both HTML and JSON formats.

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Strict operational safety through an explicit allow-list, ensuring scans only run against approved targets.

Core Capabilities of Argus Pentest

Authenticated Scanning

Utilizes a scripted login via an encrypted credential store to obtain session cookies and bearer tokens, allowing the scanner to operate as a logged-in user.

Synthesized API Discovery

Endpoints discovered during login and crawling seed a synthesized API specification, ensuring the scan reaches the authenticated surface.

AI-Driven Payloads

Integrates a model specifically for payload generation and finding triage, utilizing a text-in, text-out approach.

Strict Target Control

Scans are executed only against an explicit allow-list of approved targets to prevent unauthorized scanning.

Modular Engine Architecture

Powered by the Deep Eye open-source engine via a CLI contract, allowing the scanner to be upgraded or replaced without affecting the platform.

The Argus DAST Workflow

1Target Approval: The user deliberately adds a target to the explicit allow-list.
2Authentication: The QA engine's encrypted store provides credentials for a scripted login to bypass the auth wall.
3Crawling and Mapping: The system discovers endpoints to create a synthesized API specification of the authenticated surface.
4Payload Execution: The AI model generates payloads to test for vulnerabilities across the identified endpoints.
5Verification and Triage: Every finding is re-checked against the live target; those without evidence are quarantined and excluded from severity totals.

Frequently Asked Questions

How does Argus handle targets behind a login page?

Targets marked as requiring login cannot be scanned unauthenticated. The system refuses the scan rather than running it against the auth wall, preventing repeated error responses from being misread as exploits.

Where is the scan data stored and how is it secured?

Argus is entirely self-hosted. All scan data, credentials, and reports remain on your own infrastructure, eliminating external SaaS dependencies and ensuring maximum privacy.

What happens if a scan fails to authenticate?

A failure to authenticate is treated as an environment failure rather than a security finding. This follows the 'honest-failure' rule, ensuring that infrastructure issues are not misreported as vulnerabilities.

How does the system handle false positives and inconclusive results?

Every finding is re-verified against the live target. If a finding cannot be stood up—due to an unreachable endpoint or uniform response—it is quarantined and excluded from severity totals. Scans that reach the target but cannot exercise it are marked as 'inconclusive' rather than 'clean'.

How are the results delivered to the security team?

Findings are categorized by severity and delivered through comprehensive HTML and JSON reports for easy integration and analysis.

Secure Your Enterprise Applications

Experience authenticated security scanning with Argus—the self-hosted AI testing platform. Contact Allmaz to learn more.

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