Prompt Injection 2.0: Hybrid AI Threats

Prompt injection attacks, where malicious input is designed to manipulate AI systems into ignoring their original instructions and following unauthorized commands instead, were first discovered by Preamble, Inc. in May 2022 and responsibly disclosed to OpenAI. Over the last three years, these attacks have continued to pose a critical security threat to LLM-integrated systems. The emergence of agentic AI systems, where LLMs autonomously perform multistep tasks through tools and coordination with other agents, has fundamentally transformed the threat landscape. Modern prompt injection attacks can now combine with traditional cybersecurity exploits to create hybrid threats that systematically evade traditional security controls. This paper presents a comprehensive analysis of Prompt Injection 2.0, examining how prompt injections integrate with Cross-Site Scripting (XSS), Cross-Site Request Forgery (CSRF), and other web security vulnerabilities to bypass traditional security measures. We build upon Preamble's foundational research and mitigation technologies, evaluating them against contemporary threats, including AI worms, multi-agent infections, and hybrid cyber-AI attacks. Our analysis incorporates recent benchmarks that demonstrate how traditional web application firewalls, XSS filters, and CSRF tokens fail against AI-enhanced attacks. We also present architectural solutions that combine prompt isolation, runtime security, and privilege separation with novel threat detection capabilities.

Evaluating the Susceptibility of Pre-Trained Language Models via Handcrafted Adversarial Examples

Recent advances in the development of large language models have resulted in public access to state-of-the-art pre-trained language models (PLMs), including Generative Pre-trained Transformer 3 (GPT-3) and Bidirectional Encoder Representations from Transformers (BERT). However, evaluations of PLMs, in practice, have shown their susceptibility to adversarial attacks during the training and fine-tuning stages of development. Such attacks can result in erroneous outputs, model-generated hate speech, and the exposure of users' sensitive information. While existing research has focused on adversarial attacks during either the training or the fine-tuning of PLMs, there is a deficit of information on attacks made between these two development phases. In this work, we highlight a major security vulnerability in the public release of GPT-3 and further investigate this vulnerability in other state-of-the-art PLMs. We restrict our work to pre-trained models that have not undergone fine-tuning. Further, we underscore token distance-minimized perturbations as an effective adversarial approach, bypassing both supervised and unsupervised quality measures. Following this approach, we observe a significant decrease in text classification quality when evaluating for semantic similarity.