WhiteHaX AI-ASM Platform

Continuously Verify AI-deployment before it becomes a Business Risk

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Overview

WhiteHaX AI-ASM Platform is an enterprise-grade, automated testing and validation platform designed to harden Generative AI and Large Language Model (LLM) applications against a comprehensive spectrum of security threats and compliance risks. By simulating thousands of sophisticated adversarial attacks and policy violations, WhiteHaX AI-ASM provides developers and security teams with actionable intelligence to identify AI-specific vulnerabilities, prevent data leaks, and ensure regulatory adherence before deployment.

The WhiteHaX AI-ASM verifies the full-scale cyber-readiness of Agentic-AI deployments (including those using RAG) while allowing businesses to measure AI responsiveness and performance through average response time measurement of their AI applications.

2. Technical Specifications

CategorySpecification
Deployment ModelSaaS (Cloud-Hosted) Management with On-Premise WhiteHaX App (Win, Linux, MaCOS)
IntegrationREST API, CLI, CI/CD Pipelines (including SAST/DAST integration)
Testing VolumeCapable of executing Hundreds of Thousands of unique test cases per hour per target system.
Attack Library Curated and continuously updated library of 1,000,000+ malicious prompts; 5,000+ malicious docs; Ability to generate custom prompts using Semantically-trained internal LLMs, Hundreds of testing scenarios.
CustomizationFull customization of prompts using training inputs; Expected results verification as per deployed business-application and other params
Report GenerationDetailed PDF/HTML reports with failed prompts/docs and other details, remediation guidance, and compliance gaps.
DashboardCentralized web dashboard for managing tests, viewing results, and tracking trends over time.

3. Target Users

  • Security & Red Teams: Proactively find and remediate critical vulnerabilities in AI deployments.
  • Compliance & Risk Officers: Audit AI systems for adherence to internal policies and external regulations.
  • AI Application Developers :Integrate security testing into the development lifecycle (DevSecOps) – along with SAST/DAST tools.
  • Product Managers: Ensure customer-facing AI features are safe, reliable, and trustworthy.

4. Benefits

  • Proactive Security: Shift-left security testing to identify vulnerabilities before they reach production.
  • Compliance Confidence: Automate evidence gathering for audits and ensure continuous compliance.
  • Protect Brand Reputation: : Prevent high-profile security incidents and biased outputs that cause reputational damage.
  • Reduce Risk:: Significantly lower the risk of AI-related data breaches, model theft, confidential data leakage and system compromise via AI endpoints.
  • Save Time & Resources: Automate thousands of tests that to continuously validated AI business deployments.

5. Supported Use-Cases & Deployment Types

WhiteHaX is designed to test a wide array of AI deployments, including following example use-cases:

  • Public-Facing Chatbots & Assistants
  • Internal Copilots and Productivity Tools
  • AI-Powered Search and Retrieval Systems
  • Code Generation Assistants (e.g., GitHub Copilot alternatives)
  • Content Generation and Marketing Platforms
  • Research & Development Applications in variety of industries
  • AI APIs serving multiple downstream applications

Key Features & Capabilities


WhiteHaX's AI testing modules are organized into Nine Core pillars of AI security:

1.1. Malicious Prompt Injection & Jailbreak Testing
  • Direct Prompt Injection: Systematically attempts to override initial system prompts to force unintended model behavior.
  • Indirect (Jailbreak) Attacks: Tests thousands of sophisticated jailbreak techniques (e.g., DAN, character 扮演, encoded instructions) designed to bypass ethical safeguards.
  • Backdoor Triggers: : Injects hidden triggers and anomalous patterns into seemingly benign inputs to test for unexpected and malicious outputs.
  • Model Hijacking: Attempts to subvert the AI's intended task to perform a hidden, malicious objective.

1.2. AI User-Based Behavioral & Model Threat Testing
  • Model Drift Simulation: Monitors and measures output consistency over time to detect performance degradation and concept drift.
  • Model Poisoning Attempts: Simulates training data poisoning attacks by testing how the model responds to inputs designed to corrupt future learning or fine-tuning cycles.
  • Multi-Step Malicious Intent: Executes complex, multi-prompt attack sequences that appear harmless individually but achieve a malicious goal collectively.
  • Bias & Influence Detection: Probes the model for inherent biases related to demographics, ideology, and culture. Tests susceptibility to persuasion and influence campaigns.

1.3. Input/Output Data Leakage & Integrity Testing
  • PII Leakage Detection: : Inputs prompts designed to trick the model into revealing sensitive Personally Identifiable Information (PII) from its training data or context window.
  • Confidential Data Leakage: Tests for accidental exposure of proprietary business data, intellectual property, or confidential internal information.
  • Output Bias Analysis: Systematically audits outputs for fairness, identifying discriminatory language or unfair recommendations across protected classes.
  • Toxicity Analysis: Evaluates both user inputs and AI outputs for toxic, hateful, explicit, or harassing content.

1.4. Malicious Document Upload Testing
  • File Format Fuzzing: Tests the entire document processing pipeline with a vast library of maliciously crafted files, including:
    • MS-Office Docs (Word, Excel, PPT etc.): Macros, embedded objects, hidden content, and exploit code.
    • PDFs: :Malicious embedded objects such as images, hyperlinks, and corrupted structures.
    • Images (JPG, PNG, SVG, etc.): Steganography, pixel flood attacks, malicious SVG scripts, and prompt injections embedded in image metadata.
    • QR Codes: QR codes containing malicious URLs, prompt injections, or other exploit code.

1.5. LLM API Misuse & Abuse Testing
  • Invalid API Request Flooding: Stress-tests API endpoints with malformed requests, high-volume traffic, and various encoding attacks.
  • Key/Token Abuse Simulation: : Tests authentication and rate-limiting controls by simulating token theft, replay attacks, and permission bypass attempts.
  • Anomalous Access Pattern Detection: Generates traffic patterns indicative of scraping, data exfiltration, and brute-force attacks.
  • Access Rights Abuse: Attempts to access prompts, data, or functionalities outside the scope of a given user's API key permissions.
1.6. Agentic-AI specific Denial-of-Serice Attacks
  • Resource Exhaustion: Agentic-AI resource exhaustion can lead to unresponsive AI apps. Verify if your Agentic-AI can handle large number of Concurrent Requests-per-Sec simultaneously with large load of user responses.
  • Cost Amplification:Cost of LLM requests and responses can add up very quickly, if AI app doesn’t throttle back-end LLM usage. Stress-test Agentic-AI deployment to ensure that both request handling and request throttling for back-end LLM are handled without cost overrun.
  • Memory Exhaustion: : By varying payload size along with number of simultaneous requests, attackers can cause potential memory exhaustion of the AI-app and make it unresponsive. WhiteHaX can verify various load sizes with simultaneous requests for ensuring AI-app stress test limits.
  • Token Overflow & Rate Limit Bypass: By varying user request parameters and attacker can cause denial-of-service on your Agentic-AI. Test token overflow through various test strategies such as  IP Rotation, Endpoint Variation and Parameter Mutation to ensure your Agentic-AI infrastructure can thwart this types of attacks.

1.7. . Policy & Compliance Enforcement
  • GDPR Privacy Tests: Validates the system's ability to handle Right to Be Forgotten (erasure) requests and prevents PII leakage as mandated by GDPR.
  • HIPAA Tests: Specifically tests for scenarios that could lead to the exposure of Protected Health Information (PHI).
  • OWASP Top-10 for LLM Checks: Full test suite aligned with the Open Web Application Security Project's top vulnerabilities for LLMs (e.g., LLM01: Prompt Injection, LLM02: Data Leakage).
  • Custom Regulatory Frameworks: Allows for the creation of custom test cases to meet specific industry or regional regulations (e.g., CCPA, PCI DSS, EU AI Act).

1.8. Performance & Responsiveness Measurement
  • Response time for Prompt Inputs: Measure avg response time of input prompts and improve it for optimal response by allocating necessary resources.
  • Response time of Document Processing: Measure avg response time of uploaded docs for better throughput.
  • Overall System Performance: : Track AI system’s performance trend over time to monitor for degradation or improvements.

1.9. Stress test MCP Services:
  • Protocol Violation: Generate MCP protocol violations to see if your MCP services can handle attempts to bypass security controls.
  • MCP Tool Abuse: Maks sure your MCP service handles tools abuse that can result in service crash, disclosure of private or confidential info, service subversion or other errors.
  • Resource Exhaustion: : Generate enough MCP concurrent requests simultaneously with protocol requests and tool requests to stress test resource exhaustion on resource capacity of your MCP service.
  • Memory Leak:Ensure MCP service doesn’t cause memory leakage by iterating requests over and over with same or varying requests parameters.
  • Session Hijack: : Verify that MCP service is not prone to potential session hijack by stress testing number of simultaneous sessions.  
  • Additional MCP Tests: WhiteHaX AI-ASM also allows you to verify your MCP service against other MCP specific attacks such as Context Overflow or Malicious Tool insertion through generating MCP abusive traffic.

Contact: For more information, please visit www.WhiteHaX.com or contact sales team at sales@WhiteHaX.com.