In the rapidly evolving landscape of artificial intelligence, Large Language Models (LLMs) stand at the forefront, driving innovation and reshaping industries from automated customer service to sophisticated content creation tools. However, the rapid integration of these models into various facets of society has outpaced the understanding of their inherent security risks. "Danger in Dialogue" aims to bridge this gap by shedding light on the potential threats posed by LLMs and drawing attention to critical threats that stakeholders must navigate to leverage AI's full potential safely.

**Emerging Threats in LLMs**

Prompt Injection: A manipulation technique where attackers craft specific inputs to trigger unintended or harmful responses from the model, potentially leading to misinformation or exploitation of the system for malicious purposes.

Data Exfiltration: The risk that sensitive information, embedded in the training data of LLMs, can be extracted through carefully designed queries, posing significant privacy and security challenges.

Plagiarism: As LLMs generate content with increasing sophistication, the line between original creation and AI-assisted plagiarism becomes blurred, raising concerns over intellectual property rights and the authenticity of digital content.

Hallucinations: Instances where LLMs generate false or misleading information, presenting it as fact. These inaccuracies can undermine trust in AI systems and spread misinformation if not adequately addressed.

Tokenization Errors: Flaws in the process of breaking down text into manageable pieces for the model to understand can introduce biases or distort the intended meaning of the input, leading to erroneous outputs.

Reputation Management: The output of LLMs can influence public perception of individuals, organizations, and concepts, making it crucial to manage and mitigate any reputational damage caused by biased or inaccurate model responses.

The session will delve into each of these threats, offering insights into their mechanics, real-world implications, and the challenges they pose to the ethical and secure deployment of LLMs.