GenAI models are transforming business processes by enabling intelligent content creation, predictive insights, and automated decision-making. Yet, as enterprise adoption accelerates, these models possess GenAI-specific vulnerabilities that could lead to security breaches, inappropriate outputs, and compliance failure – all of which can erode trust in AI-driven products, as well as cause harm to the business and the customers. This whitepaper presents a structured methodology for evaluating GenAI model vulnerabilities, comprising threat modelling, attack surface definition, vulnerability testing, and evaluation.
Using over 900 test cases spanning 30+ attack techniques, eight mainstream foundatoin models were assessed to identify common failure modes and compare resilience.Key findings indicate that larger models with advanced guardrails—like Claude 3.5 Sonnet v1 and Phi4—demonstrate lower fail rates, whereas Mistral exhibits the highest overall risk (52.8% success rate for the Vulcan adversarial attacks). Role-playing remains a persistent weakness across multiple models, and specialized tokens or punctuation can be used to bypass content filters.
Enterprises seeking to harness GenAI must adopt a vigilant, security-focused stance. Comprehensive red teaming, layered content moderation, and context-aware monitoring can mitigate risks, while ongoing model patching addresses evolving threats. By proactively identifying vulnerabilities and refining guardrails, enterprises can protect data integrity, ensure regulatory compliance, and sustain user confidence in AI-enabled services.
Read the full whitepaper here.
