refers to an automated penetration testing framework that leverages Deep Reinforcement Learning (DRL) to identify and exploit vulnerabilities in target systems. By modeling the network environment as a state space and potential attack actions as an agent's movement, the system learns optimal attack paths through trial and error without relying on a static database of known exploits. This approach allows the tool to adapt to complex, changing network topologies and discover multi-stage attack vectors that traditional automated scanners might miss, ultimately providing a more dynamic assessment of security posture. Bollywood Actress Genelia Fake Videos Upd Apr 2026