1. Key Features & Capabilities

WhiteHaX's AI testing modules are organized into five 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) AttacksTests 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.
• Multi-Step Malicious Intent: Executes complex, multi-prompt attack sequences that appear harmless individually but achieve a malicious goal collectively.
• 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 FuzzingTests 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.
• LLM DoS:Attempts to flood the business application by simulating various types of Denial-of-Service attacks such as Resource DoS, Cost-Overrun DoS, Model-Corruption DoS etc..

1.6. 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).

3. Target Users

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

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