Model Security

Evaluating AI model robustness against adversarial inputs, jailbreak attempts and unintended behavior in production environments.

Prompt Injection Defense

Detecting and mitigating prompt injection attacks across direct, indirect and cross-modal vectors in AI-powered applications.

Data Leakage Prevention

Preventing sensitive data extraction from AI systems through training data memorization, model inversion and side-channel attacks.

AI Guardrails & Policy

Implementing safety guardrails, content filtering and output monitoring to enforce organizational AI usage policies.

Adversarial Robustness in Multi-Modal AI Systems

Generic AI Safety Lab (fictional)

Study examines how adversarial inputs in one modality can influence model behavior in another, revealing cross-modal attack surfaces in multimodal AI deployments.

Fictional research summary for illustration.

Measuring Jailbreak Resistance Across Frontier Models

Dragons Community Research (fictional)

Comparative evaluation of jailbreak resistance across major commercial language models using standardized prompt injection benchmarks.

Fictional research summary. No real benchmark data.

Data Poisoning Defenses in Federated Learning

European AI Security Institute (fictional)

Proposes defensive techniques for detecting and mitigating data poisoning attacks in federated learning environments used for collaborative threat detection.

Fictional research summary.