LLM & Agentic Apps Penetration Testing

We attempt various prompt injection techniques to bypass safety mechanisms and manipulate your AI's behavior. This includes direct injection attacks, indirect injection through data poisoning, jailbreak attempts, and role-play scenarios designed to make the AI ignore its instructions. We test whether attackers could manipulate your LLM to leak sensitive information, generate harmful content, or perform unauthorized actions that could damage your business or users.

We test whether your AI system could inadvertently expose sensitive data from training materials, conversation history, or system prompts. We examine data retention practices, conversation privacy, and whether the model can be manipulated into revealing information about other users, internal systems, or confidential business data. We verify compliance with GDPR, CCPA, and other privacy regulations relevant to AI systems.

AI agents that can access databases, APIs, file systems, or external services introduce unique risks. We test whether these integrations are properly secured - examining authentication mechanisms, authorization boundaries, input validation, and whether an attacker could manipulate the agent into performing unauthorized database queries, API calls, or file operations. We verify that agents can't be tricked into escalating privileges or accessing resources beyond their intended scope.

We test whether your application is vulnerable to data poisoning attacks where malicious inputs could influence model behavior or cause denial of service through adversarial examples. For applications that use retrieval-augmented generation (RAG) or fine-tuning, we examine whether attackers could inject malicious content into knowledge bases or training data. We assess the security of your data pipelines and vector databases used for AI applications.

We test for all OWASP LLM Top 10 vulnerabilities including prompt injection, insecure output handling, training data poisoning, model denial of service, supply chain vulnerabilities, sensitive information disclosure, insecure plugin design, excessive agency, overreliance, and model theft. This emerging framework represents the cutting edge of LLM security and ensures comprehensive coverage of AI-specific risks that traditional security testing misses.

We examine whether your application properly validates and sanitizes LLM outputs before presenting them to users or using them in downstream systems. We test for generation of harmful content, injection of malicious code (XSS, SQL injection) through AI outputs, and whether output filtering can be bypassed. We also assess whether the AI could generate outputs that violate your usage policies, regulatory requirements, or ethical guidelines.