AI & Agentic System Security Testing

Large Language Models (LLMs) and AI agents introduce entirely new attack surfaces, such as prompt injection and data poisoning, that traditional security tools cannot protect against. We provide specialized testing for the models, their data pipelines, and their application integrations. Securing AI requires a deep understanding of both traditional cybersecurity and the unique behaviors of machine learning models.

Prompt injection occurs when an attacker inputs malicious instructions to trick an LLM into ignoring its safety guardrails or leaking sensitive data. We rigorously test your models with a wide variety of adversarial prompts to identify and mitigate these jailbreak attempts. Ensuring your AI follows its intended instructions is critical for preventing unauthorized actions and data disclosure.

RAG connects LLMs to your private data, creating a risk that the AI might reveal sensitive information to unauthorized users. We test the access controls and document parsing within the RAG pipeline to ensure the AI only retrieves and presents data the user is permitted to see. This prevents the AI from becoming an unintentional internal data leak tool.

Data poisoning is an attack where malicious data is introduced into the AI's training or fine-tuning set to corrupt its behavior. This can cause the model to output biased information or create hidden backdoors that an attacker can later exploit. We review your data collection and training processes to ensure the integrity of the information that shapes your AI's intelligence.

We assess the autonomy limits of AI agents to ensure they cannot execute unauthorized actions, such as deleting databases or sending unauthorized emails. We also test for model inversion attacks, where an attacker tries to reconstruct sensitive training data by querying the model. Setting clear boundaries for AI autonomy is essential for maintaining control over your automated systems.