Weaponizing cognitive bias in autonomous systems: a framework for black-box inference attacks.

Autonomous systems operating in high-dimensional environments increasingly rely on prioritization heuristics to allocate attention and assess risk, yet these mechanisms can introduce cognitive biases such as salience, spatial framing, and temporal familiarity that influence decision-making without altering the input or accessing internal states. This study presents Priority Inversion via Operational Reasoning (PRIOR), a black-box, non-perturbative diagnostic framework that employs structurally biased but semantically neutral scenario cues to probe inference-level vulnerabilities without modifying pixel-level, statistical, or surface semantic properties. Given the limited accessibility of embodied vision-based systems, we evaluate PRIOR using large language models (LLMs) as abstract reasoning proxies to simulate cognitive prioritization in constrained textual surveillance scenarios inspired by Unmanned Aerial Vehicle (UAV) operations.

A CNT-Toughened Strategy for In-Situ Repair of Aircraft Composite Structures.

This study aimed to develop an in-situ field-repair approach, especially for aircraft composite structures, to enhance the interlaminar toughness of plain-woven composites (PWCs) by adding multi-walled carbon nanotubes (MWCNTs). MWCNTs were dispersed at each interface between prepreg layers by means of solvent spraying, with a density of 1.58 g/m.