Killer Apps: Low-Speed, Large-Scale AI Weapons

TL;DR

The paper investigates AI-enabled weapons that exploit information channels to influence behavior at scale, highlighting risks beyond traditional violent conflict. It proposes a methodology that combines historical sabotage concepts with retrieval-augmented generation to prompt LLMs toward organizational disruption. Through email and code manipulation demonstrations, the work shows that AI models can subtly alter content or obfuscate code while remaining difficult to detect. The findings underscore the need for guardrails, detection frameworks, and cross-disciplinary defense research to mitigate scalable AI-driven information warfare.

Abstract

The accelerating advancements in Artificial Intelligence (AI) and Machine Learning (ML), highlighted by the development of cutting-edge Generative Pre-trained Transformer (GPT) models by organizations such as OpenAI, Meta, and Anthropic, present new challenges and opportunities in warfare and security. Much of the current focus is on AI's integration within weapons systems and its role in rapid decision-making in kinetic conflict. However, an equally important but often overlooked aspect is the potential of AI-based psychological manipulation at internet scales within the information domain. These capabilities could pose significant threats to individuals, organizations, and societies globally. This paper explores the concept of AI weapons, their deployment, detection, and potential countermeasures.

Figures (3)

• Figure 1: ContextExplorer showing GPT-4-0314 extrapolating from the Simple Sabotage Manual
• Figure 2: Original Email
• Figure 3: Modified email. Modifications and additions are shown in red