Advanced diagnostic scanning to identify systemic failure modes, hallucinations, and critical data leakage across Large Language Models.
Traditional cybersecurity assessment tools are insufficient for the non-deterministic nature of Generative AI. Our scanning engine provides **automated red-teaming** by simulating adversarial intent to uncover risks in model alignment and prompt processing.
Evaluating scenarios where malformed inputs hijack instructions, leading to unauthorized actions or restricted data access within connected environments.
Scanning for the inadvertent memorization of sensitive training data that could be extracted through direct or indirect probing techniques.
Rigorous testing against complex "jailbreak" patterns and linguistic obfuscation designed to bypass model safety filters.
Quantifying the model's tendency to produce factually incorrect or nonsensical information under adversarial pressure.
Our architecture operates on a high-velocity feedback loop. We generate a library of **adversarial probes** designed to trigger specific failure modes. Automated **evaluators** then analyze the model's output via NLP and statistical checks to score resilience.
Thousands of curated adversarial prompts across diverse threat categories including toxicity and logic-bypass.
Seamlessly integrates with cloud-based LLMs, open-source models, and locally hosted private instances.
Architected for massive parallel processing to vet models during the pre-deployment CI/CD phase.
Generate the technical proof of model robustness required for high-risk AI system conformity assessments.
Map every discovered vulnerability directly to the MITRE ATLAS framework for enterprise threat intelligence.
Receive technical guidance on adjusting parameters, dropout rates, and noise-injection to mitigate discovered risks.