LLM Forensics: Mitigating Fact Injection Attacks on Large Language Models

A Joint Initiative to Secure the Future of AI

The rapid deployment of Large Language Models (LLMs) across critical industries necessitates rigorous security and robust factual integrity. This research project addresses a crucial and emerging vulnerability: Fact Injection Attacks, which threaten the trustworthiness of AI systems.

This project is a cooperative endeavor between industry leadership and academic expertise, representing a joint research collaboration between Red Hat and the Security@FIT research group at Brno University of Technology, Czechia. This partnership combines Red Hat’s deep experience in securing enterprise AI deployments with Security@FIT’s cutting-edge academic research in advanced threat analysis and mitigation. Together, we are dedicated to securing the next generation of AI systems against manipulation and ensuring their reliability in the global digital infrastructure.

The project began in 2025 and is actively building foundational strategies to protect AI integrity.

When AI Lies Confidently

Fact injection attacks represent a subtle and dangerous form of manipulation. They involve an adversary secretly tampering with a language model’s internal knowledge state (its stored ‘knowledge’ derived from training) to embed false, biased, or fabricated information. The goal is straightforward: to make the LLM confidently generate fabricated data as truth, thereby compromising its foundational reliability and deliberately misleading users.

LLMs face a significant threat from fact injection attacks, which fundamentally erode the reliability and trustworthiness of their outputs.

A key concern stems from sophisticated techniques that leverage methods originally designed for beneficial purposes, such as Rank-One Model Editing (ROME) (Meng et al., 2022). As demonstrated by recent work like the PoisonGPT research (Huynh and Hardouin 2023), attackers can adapt ROME-like approaches to perform highly localized, surgical edits to the model’s parameters. This allows for the injection of malicious ‘facts’ into the model’s memory.

A critical challenge is the inherent difficulty in differentiating these malicious injections from legitimate, benign model updates or fine-tuning processes. The real-world impact of successful fact injection in deployed LLMs is severe, including:

• Widespread dissemination of high-quality misinformation.
• The potential for flawed, AI-driven decisions in sensitive areas (e.g., finance, healthcare).
• Catastrophic damage to an organization’s reputation and the total erosion of user trust in AI systems.

Research Focus and Objectives

This project proposes a focused, defensive investigation into the technical nature of ROME-based fact injection attacks to develop robust and practical strategies for their detection and mitigation. Our research objectives are the folloowing:

• Signature Analysis: Analyze the precise technical mechanisms of ROME-like methods used for fact injection and identify their unique, detectable digital footprint within the model’s parameters.
• Security Evaluation: Systematically evaluate the effectiveness of current LLM security, auditing, and monitoring tools against these highly specific, localized injection techniques.
• Malice Detection: Define clear, actionable criteria and develop foundational methods to reliably distinguish malicious fact injections from legitimate, benign changes to model parameters.
• Defensive Strategy: Conceptualize, prototype, and propose strategies for enhancing the detection and building effective mitigation and remediation defenses against ROME-based fact injection attacks.

This research is positioned to provide critical insights and foundational strategies for safeguarding LLMs against advanced, state-of-the-art fact injection attacks, ensuring their trustworthiness and reliability in all sensitive, real-world applications.