Pioneering the intersection of applied AI algorithms and offensive security operations to map risk surfaces dynamically.
Peer-reviewed research and whitepapers documenting automated validation and adversarial machine learning.
Analyzing decoupled large language model agents executing concurrent target discovery, vulnerability classification, and exploit payload generation cycles safely.
Utilizing Q-learning reward models to trace optimal, low-noise lateral movement paths across target Active Directory networks.
Synthesizing custom assembly decryptors dynamically using local model scripts to prevent signature detection by endpoint engines.
Showcasing research projects conducted by student interns from Assam Science and Technology University (ASTU).
Fine-tuning language models on Solidity code structures to discover potential reentrancy bugs and integer overflows automatically.
Building autonomous browser-agent scripts to trace target applications, identify input boxes, and chain SQL injection results.
Measuring action weights and decision parameters of LLM agents when encountering decoy LDAP accounts and honeytoken databases.