Why LLM Hallucinations Are a Critical Cyber Risk in C2ISR Operations

Introduction

In modern military and intelligence operations, Command, Control, Intelligence, Surveillance, and Reconnaissance (C2ISR) systems are increasingly augmented by artificial intelligence. Large language models (LLMs) assist in synthesising intelligence reports, generating threat assessments, and supporting mission planning.

However, a subtle, often underestimated flaw in these AI systems—LLM hallucinations—has emerged as a critical cyber risk. Hallucinations occur when an AI generates false or misleading information that appears plausible. In the high-stakes world of defence, the consequences can be catastrophic.

1. How LLM Hallucinations Emerge in C2ISR

Hallucinations stem from the way LLMs generate responses—predicting the “most likely” next word or phrase based on training data. Under certain conditions—poorly curated data, ambiguous prompts, adversarial inputs—the model can fabricate intelligence details.

In C2ISR environments, this could mean:

• Misidentifying enemy positions based on non-existent reconnaissance data
• Creating false communications intercepts
• Inventing plausible but fake geospatial markers.

2. Cybersecurity Risks in Military Contexts

Misidentification of Threats or Friendly Forces

An LLM-generated intelligence report might wrongly classify a friendly UAV as hostile, triggering escalation or even kinetic engagement.

False Intelligence Assessments

A hallucinated narrative about enemy troop movements can lead to redeployments that leave other sectors exposed.

Operational Security Breaches

Hallucinations may embed sensitive but incorrect data—making it easier for adversaries to deduce real capabilities or locations.

Adversarial Manipulation

Attackers who understand an LLM’s weaknesses can design prompts or inject poisoned data to trigger specific hallucinations, shaping strategic decisions in their favour.

3. Cascading Failures in Automated Decision Systems

C2ISR increasingly integrates AI-generated intelligence directly into automated decision pipelines—drone swarm control, missile targeting prioritisation, or real-time threat response.

A hallucination at the input stage can ripple through the system, causing:

Misdirected assets

Delayed threat neutralisation

Inadvertent engagement with civilian assets

4. Mitigation Strategies

Human-in-the-Loop Oversight – Every AI-generated assessment must be validated by trained intelligence officers.

Adversarial Testing – Simulate hallucination-triggering attacks to strengthen system defences.

Data Provenance Verification – Tag and verify every input to track information sources.

Red Teaming – Use internal cyber teams to probe AI systems for vulnerabilities.