DECENTRALIZED SCIENCE LAB

Building comprehensive frameworks for evaluating security vulnerabilities in distributed Large Language Model deployments. Our research focuses on automated vulnerability detection across federated LLM networks, adversarial attack resistance in decentralized environments, and novel risk scoring methodologies for production systems operating in Web3 ecosystems.

Developing intelligent tools that leverage LLMs with annotated control flow analysis for automated smart contract security assessment. Our systems identify vulnerabilities, detect security patterns, and discover potential exploits in blockchain applications through advanced program analysis and machine learning techniques.

Investigating alignment faking and deceptive behaviors in distributed AI systems. We develop detection methods for strategic deception, goal misrepresentation, and sandbagging in federated learning environments. Our work includes behavioral consistency testing and robustness evaluation for decentralized AI deployments.

Creating defense mechanisms against jailbreak attacks, adversarial examples, and privacy leakage in federated AI systems. Our research addresses model backdoor detection, supply chain security for distributed training, and privacy-preserving techniques that maintain model integrity across decentralized networks.