Memory abuse in cybersecurity refers to the exploitation of an artificial intelligence (AI) system’s stored data - such as user preferences, interaction history, or learned behaviors - to manipulate or unduly influence its future actions and decisions. Attackers may leverage this persistent memory to inject malicious patterns, bias outputs, or gain unauthorized advantages over time. Unlike traditional memory-based attacks that target hardware or software vulnerabilities, memory abuse in AI focuses on the misuse of contextual or experiential data that the system retains. This can lead to privacy violations, compromised decision-making, and the erosion of trust in AI-driven applications. Mitigating memory abuse requires robust data governance, regular audits, and mechanisms to detect and limit the undue influence of historical data.