The Generative AI Security project addresses the unprecedented security challenges introduced by large language models, image generators, and other generative AI systems. As these powerful models become ubiquitous, they create new attack surfaces including prompt injection attacks, model inversion techniques, data poisoning during training, and the generation of deceptive content like deepfakes and synthetic media. Our research develops comprehensive security frameworks that protect generative AI systems throughout their lifecycle, from training data protection and model hardening to runtime monitoring and output verification. We focus on preventing adversarial manipulation of AI systems while maintaining their creative and productive capabilities. By combining advanced machine learning techniques, cryptographic protections, and formal verification methods, we create defense mechanisms that enable the safe deployment of generative AI in critical applications while mitigating the risks of misuse and malicious exploitation.
Objectives
Generative AI Security pursues critical objectives to establish security foundations for the safe deployment of generative AI technologies, protecting against emerging threats while preserving the benefits of these powerful systems.
Adversarial Prompt Protection
Develop robust defense mechanisms against prompt injection attacks, jailbreak attempts, and adversarial prompt engineering that manipulate generative AI models into producing harmful, biased, or unintended outputs.
Model Intellectual Property Protection
Create comprehensive protections against model extraction attacks, watermarking techniques for AI-generated content, and secure deployment frameworks that prevent unauthorized access to proprietary generative models.
Content Authenticity & Verification
Implement advanced detection and verification systems for AI-generated content including deepfake detection, synthetic media identification, and provenance tracking for digital artifacts.
Training Data Poisoning Prevention
Develop defenses against data poisoning attacks during model training, including outlier detection, data sanitization, and robust training algorithms resistant to adversarial data manipulation.
Runtime Security Monitoring
Create continuous monitoring systems that detect anomalous behavior in generative AI systems, including unusual output patterns, potential misuse indicators, and security policy violations.
Methodology
Our research methodology combines adversarial machine learning, formal verification, and empirical security evaluation to develop comprehensive protection frameworks for generative AI systems.
Phase 1: Generative AI Threat Intelligence
Comprehensive analysis of attack vectors across generative AI architectures including LLMs, diffusion models, GANs, and multimodal systems. Development of threat models, attack taxonomies, and vulnerability assessment frameworks.
Phase 2: Defense Mechanism Development
Implementation of multi-layered security approaches including prompt sanitization, output filtering algorithms, model watermarking techniques, and adversarial training methods for robust generative models.
Phase 3: Content Authentication Systems
Development of digital watermarking, cryptographic signatures, and blockchain-based provenance systems for verifying the authenticity and origin of AI-generated content.
Phase 4: Runtime Security Monitoring
Design and implementation of continuous monitoring systems that detect anomalous behavior, potential misuse, and security policy violations in deployed generative AI systems.
Phase 5: Empirical Evaluation & Red Teaming
Rigorous testing against known attacks, red team exercises to discover novel vulnerabilities, and performance benchmarking to ensure security mechanisms don't compromise generative capabilities.
Phase 6: Standards & Deployment Frameworks
Development of security standards, best practices, and deployment frameworks for secure generative AI adoption across different application domains and organizational contexts.
Expected Results & Impact
Generative AI Security will deliver foundational security capabilities for the safe and responsible deployment of generative AI technologies, establishing new standards for trustworthy AI content generation.
Technical Achievements
- Prompt Attack Mitigation: 95%+ reduction in successful prompt injection and jailbreak attacks
- Model Protection: Robust defenses against model extraction and intellectual property theft
- Content Verification: 90%+ accuracy in detecting AI-generated synthetic media
- Runtime Security: Real-time monitoring with sub-second threat detection
Industry Impact
- Content Platforms: Protection against misinformation and synthetic media manipulation
- Creative Industries: Secure AI tools for content creation with IP protection
- Financial Services: Fraud detection and secure AI-driven financial analysis
- Education: Authentic assessment systems resistant to AI-generated cheating
Research Contributions
- Publication of novel generative AI security techniques in top AI and security conferences
- Open-source security frameworks for protecting generative AI systems
- Development of standards for secure AI content generation and verification
- Establishment of benchmarks for evaluating generative AI security
Societal Impact
The project will enable the safe adoption of generative AI technologies while preventing their misuse for disinformation, fraud, and intellectual property violations, fostering trust in AI-generated content and supporting the responsible development of AI applications.