New research from AI cybersecurity firm Darktrace revealed that most security leaders are preparing for AI-powered cyberattacks. According to the research paper titled, "The Emergence Of Offensive AI," conducted by Forrester Consulting on behalf of Darktrace, 88% of decision makers in the security industry believe offensive AI is inevitable, with 50% of them expecting the industry to see these attacks in coming years. The research also highlighted that 77% of respondents expect weaponized AI to lead to an increase in the scale of cyberattacks, while 66% of them felt that it would lead to new attacks. Over 80% of security decision-makers opined that organizations require advanced cybersecurity defenses to combat offensive AI, and 75% of security leaders are concerned over business disruption. The findings are based on the responses from security leaders across different industries, including retail, financial services, and manufacturing sectors.

Real-time agent-centered heuristic search is a well-studied problem where an agent that can only reason locally about the world must travel to a goal location using bounded computation and memory at each step. Many algorithms have been proposed for this problem, and theoretical results have also been derived for the worst-case performance. Assuming sufficiently poor tie-breaking, among other conditions, we derive theoretical best-case bounds for reaching the goal using LRTA*, a canonical example of a real-time agent-centered heuristic search algorithm. We show that the number of steps required to reach the goal can grow asymptotically faster than the state space, resulting in a "scrubbing" when the agent repeatedly visits the same state. This theoretical result, supported by experimental data, encourages recent work in the field that uses novel tie-breaking schemas and/or perform different types of learning.