Large language models (LLMs) power critical features across products, from customer support to decision-making, but their flexibility also introduces new attack surfaces. Malicious inputs, prompt injection, or poorly isolated training data can make models reveal sensitive information, execute harmful instructions, or behave unpredictably.
Our LLM Penetration Testing service simulates real-world adversarial techniques against your model stack, including prompt injection, jailbreaks, data extraction, API abuse, and poisoning scenarios, to demonstrate how these weaknesses can lead to real security risks.
Scope Definition
Prompt Injection Testing
Data Leakage Assessment
Access Control Review
Output Validation
Integration Security
Adversarial Robustness
Configuration Review
Define in-scope endpoints, user roles, datasets, and test windows. Clarify objectives, success criteria, and escalation procedures.
Enumerate model prompts, system templates, connected APIs, and third-party integrations to understand the LLM’s operational landscape.
Test for prompt injection, jailbreaks, hidden prompt overrides, and instruction steering using controlled adversarial inputs.
Probe the model for sensitive data leakage, unintended memory retention, or exposure of proprietary knowledge.
Conduct fuzzing, bias detection, and red-team simulations to evaluate robustness, ethical constraints, and context manipulation resilience.
Assess input sanitization, authentication enforcement, rate limiting, and logging within model APIs and connected systems.
Deliver a comprehensive report with executive summary, scope, methodology, prioritized findings with PoCs, business impact, risk ratings, and remediation guidance, followed by a restitution meeting.
Large language models are a new and rapidly evolving attack surface. Prompt injection, retrieval-based leakage, or insecure plugin integration can expose confidential data or enable lateral movement. Weak authentication and monitoring can lead to model abuse or silent data exfiltration. This assessment uncovers exploitable vectors across prompts, pipelines, tokenization quirks, vector databases, and connected integrations before adversaries do.
Typically between 3 and 7 business days, depending on model complexity, number of endpoints, and integration depth.
All testing is performed safely and non-destructively. For production environments, test windows are coordinated in advance to minimize risk. Potentially disruptive actions are executed only after explicit approval.
