As AI assistants and agentic systems increasingly interact with emails, websites, social media, online reviews and digital documents, a new attack surface is emerging: the manipulation of AI systems through seemingly harmless external content. Malicious actors can exploit indirect prompt injection by embedding instructions in content that an AI system is asked to read, analyse or process. Unlike traditional scams that target people directly, these attacks attempt to manipulate the AI assistant acting on a user's behalf, potentially influencing its decisions, revealing sensitive information or triggering unintended actions. This talk explores how everyday online interactions can become attack vectors for AI-powered systems. It examines why these threats become particularly concerning when AI systems have access to personal information, communication channels and external tools, and discusses practical approaches to reducing risk through trust boundaries, permission controls, input handling and human oversight.
Al Engineer focused on adapting and fine-tuning LLMs for specialized domain tasks. Experienced in building custom training setups, practical LLM applications, optimizing PEFT pipelines, improving downstream model evaluation, and evaluating Al safety. Driven by technical innovation and integrating raw research architectures into reliable production systems.
