This research introduces an innovative approach to defend Large Language Models against malicious inputs that attempt to manipulate model outputs.

• Identifies attack patterns through residual activation analysis
• Enables real-time detection of adversarial prompts
• Enhances model resilience without compromising performance
• Provides a white-box security solution for enterprise LLM deployments

As organizations increasingly rely on LLMs for critical operations, this defensive framework addresses a key security vulnerability, helping maintain both model integrity and user trust in AI systems.

Defending Large Language Models Against Attacks With Residual Stream Activation Analysis