AI Hacking Services

AI hacking is the practice of identifying and exploiting vulnerabilities specific to artificial intelligence systems — machine learning models, their training data, and the infrastructure that serves them — to find weaknesses before adversaries do.

Unlike traditional penetration testing, which targets applications, networks, and infrastructure, AI hacking targets the model itself and its decision logic. That includes manipulating inputs to force misclassification (adversarial examples), recovering sensitive records from a deployed model (membership inference and model inversion), corrupting training data to plant backdoors (data poisoning), and bypassing the safety controls of generative systems (prompt injection and jailbreaking).

These attacks require expertise that conventional security testing doesn’t cover: an understanding of model architectures, training pipelines, and how AI systems behave under adversarial pressure. That is the gap VerSprite’s AI hacking services are built to close.

VerSprite assesses AI systems using PASTA (Process for Attack Simulation and Threat Analysis) — the risk-centric, seven-stage threat modeling methodology co-created by VerSprite CEO Tony UcedaVélez and documented in Risk Centric Threat Modeling (Wiley, 2015). Applying PASTA to AI means every test is tied to a business objective and a credible threat, not run as a generic checklist.

Adversarial Machine Learning

• Evasion attacks — crafting inputs that cause misclassification while appearing normal
• Data poisoning — testing whether training data can be manipulated to degrade performance or plant backdoors
• Model extraction — assessing whether a proprietary model can be reconstructed through strategic querying
• Membership inference — determining whether specific records can be identified as training data, exposing sensitive information

LLM & Generative AI Security

• Prompt injection — crafting inputs that bypass safety filters or extract restricted information
• Jailbreaking — testing alignment and safety mechanisms against adversarial prompts
• Context manipulation — exploiting context windows to influence model behavior
• API security — evaluating LLM API integrations for data leakage and abuse

AI Infrastructure & MLOps

• Pipeline security — assessing CI/CD for model training and deployment
• Model registry and storage — evaluating version control and access controls for model artifacts
• Data pipeline security — testing ingestion, preprocessing, and feature-engineering systems
• Container and orchestration — securing ML workloads and their runtime environments

AI systems are already making decisions that affect your customers, your data, and your operations. VerSprite helps you understand how an attacker would target them — and what to fix first.

Build a Tailored AI Security Engagement