Elite LLM Red Teaming and Adversarial AI Security
Standard security scanners are built for code; they are blind to the logic of Large Language Models. AI introduces a new class of "non-deterministic" risks where the vulnerability isn't in a line of code, but in the model’s behavior.
From prompt injections that bypass system controls to the accidental leakage of PII through model outputs, your AI surface area is larger and more unpredictable than a standard SaaS application. At WhiteHackLabs, we bridge the gap between traditional cybersecurity and Adversarial Machine Learning.
Core Service Offerings
A. Prompt Injection & Jailbreaking
We simulate sophisticated adversarial attacks designed to bypass system prompts, neutralize safety filters, and hijack the model’s intent. We ensure your AI remains focused on its intended purpose and cannot be coerced into harmful or unauthorized actions.
B. Data Leakage & Privacy Audits
LLMs can inadvertently memorize and regurgitate sensitive training data. We perform "Inversion Attacks" and extraction simulations to ensure your proprietary intellectual property and customer PII remain confidential and cannot be extracted via clever prompting.
C. Insecure Output Handling
AI-generated content is often trusted blindly by downstream systems. We test the "Integration Layer"—identifying vulnerabilities where a model’s output could lead to Cross-Site Scripting (XSS), Remote Code Execution (RCE), or unauthorized API calls within your infrastructure.
D. Model Poisoning & Supply Chain Risk
For teams fine-tuning models, we evaluate the integrity of your training pipeline. We identify risks associated with poisoned datasets and insecure third-party plugins (RAG systems) that could compromise the integrity of your entire AI ecosystem.
Our Methodology: The White Hack Labs Framework
Beyond the OWASP Top 10 for LLMs
While we align with the OWASP Top 10 for LLM Applications, our approach is driven by the "hacker mindset." We don't just run a checklist; we think like an adversary.
Reconnaissance & Threat Modeling: We map your AI architecture, identifying where the model interacts with users, databases, and third-party APIs.
Adversarial Simulation: Our engineers employ automated and manual techniques to "stress test" the model’s boundaries.
Impact Analysis: We don't just report "flaws"—we demonstrate how a prompt injection could lead to a full data breach or brand reputation collapse.
Remediation & Guardrail Implementation: We provide actionable code fixes and guidance on implementing robust guardrails (like NeMo-Guardrails or custom sanitization layers).