By Mohamad Mirghahari – Irregular Warfare Center
Last Updated: July 2023
Artificial Intelligence (AI) is emerging as one of the most potent tools in modern irregular warfare, fundamentally transforming how adversaries manipulate information, influence public opinion, and even destabilize economies. From deep fakes to algorithm-driven disinformation campaigns, AI enables rapid message generation and highly targeted influence operations that can alter the course of military and economic engagements. In this long-form technical blog post, we will explore AI’s role in irregular warfare—from beginner concepts to advanced applications—including real-world examples and technical code samples that showcase practical implementations.
Keywords: Artificial Intelligence, Irregular Warfare, Deep Fake, MILDEC, Disinformation, Cyber Operations, Economic Sabotage, Generative Adversarial Networks (GANs), Data Analytics, DoD, Military Deception, Irregular Warfare Center
On the morning of May 22, 2023, an AI-generated image depicting an explosion at the Pentagon rapidly circulated online, triggering widespread social media sharing and financial market reactions. Despite the image being quickly debunked, its immediate impact was profound—a stark demonstration of how AI-driven content can be weaponized in irregular warfare. In this blog post, we unravel the layers behind this incident and explain how AI enhances information operations that can shape influences at strategic levels.
AI is not simply a technological trend; it is an evolving paradigm in information warfare. The potential to create and distribute disinformation along with other military deception (MILDEC) tactics has opened new avenues for both state and non-state actors, challenging our ability to discern real events from manufactured narratives.
Irregular warfare encompasses a broad array of non-traditional strategies and tactics employed in conflict situations, where the enemy does not conform to conventional military organization. Unlike traditional warfare, irregular warfare targets societal vulnerabilities, economic stability, and even public discourse, often leveraging asymmetrical tactics to blurring the lines between combatants and civilians.
Key elements of irregular warfare include:
Military deception (MILDEC) is defined by the U.S. Department of Defense (DoD) as content “intended to deter hostile actions, increase the success of friendly defensive actions, or to improve the success of any potential friendly offensive action.” Historically, MILDEC included physical decoys and feints; however, the advent of AI now adds new dimensions to these efforts, ranging from low-tech misinformation to advanced digital deceptions.
Artificial Intelligence has revolutionized multiple fields, and its integration into irregular warfare is markedly altering the landscape of conflict. Below, we delve into three critical avenues through which AI impacts irregular warfare operations.
AI’s ability to generate and disseminate content rapidly makes it a natural fit for disinformation campaigns. By automating many aspects of message creation and targeted distribution, AI reduces the human workload and increases the frequency and sophistication of information operations.
Example:
In a demonstration scenario, a private network science firm used AI to determine 20 international media personalities who could most effectively influence perceptions about Russia in Mali. Out of a pool of over 10,000 influencers, AI identified a select group quickly, highlighting how targeted disinformation can be fine-tuned with data analytics.
Deep fakes represent one of the most alarming applications of AI within irregular warfare. By synthesizing realistic audio, video, and images, deep fake technology can fabricate evidence and simulate scenarios that never occurred.
Consequences:
When the Pentagon explosion image surfaced, the speed at which it was shared created real-world impacts. Markets reacted to what appeared as a credible threat, causing a $500 billion swing in market capitalization despite rapid debunking. This incident underscores the potential for disinformation to lead directly to economic and societal destabilization.
Beyond influencing public perception, AI can also be weaponized to target economic systems. By controlling the narrative around financial stability, AI-generated disinformation can induce market anxiety, influence trading decisions, and even instigate economic sabotage.
On May 22, 2023, an AI-generated image depicting an explosion at the Pentagon went viral across social media platforms. Despite being quickly exposed as fake, the image sparked significant market movement and widespread misinformation. This serves as a prime example of how AI-driven content can create tangible effects in both defense and economic domains.
The Venezuelan government has been known to deploy AI-generated deep fakes, including mimicking American newscasters, to broadcast propaganda aimed at destabilizing opposition and reinforcing government narratives. Similar applications have been observed in China and Burkina Faso, where deep fakes have been used to sway public opinion during politically turbulent times.
AI plays a pivotal role in cyber operations by automating the collection and analysis of vast amounts of data from social media and other public sources. For instance, algorithms can scan networks to identify vulnerabilities or track the spread of disinformation, allowing operatives to quickly adjust strategies in real time.
For those interested in the technical underpinnings of AI-driven operations in irregular warfare, we now turn to practical applications. The following sections include real-world code samples—including Bash and Python scripts—that demonstrate how to scan for specific patterns in network data and parse outputs to inform decision-making.
Bash scripts can automate network scans to secure communication channels or detect anomalous activity. Below is an example Bash script that uses the nmap tool to scan for open ports on a target system. The output can indicate potential vulnerabilities that may be exploited by adversaries using AI-driven disinformation tactics.
#!/bin/bash
# Name: network_scan.sh
# Description: This script performs a network scan using nmap to identify open ports that could be targeted by adversaries.
# Check if nmap is installed
if ! command -v nmap &> /dev/null; then
echo "nmap could not be found. Please install nmap and retry."
exit 1
fi
# Define target IP or range
TARGET="192.168.1.0/24"
echo "Scanning network $TARGET for open ports..."
# Run nmap scan with service detection and aggressive mode
nmap -A -T4 $TARGET -oN scan_results.txt
echo "Scan complete. Results saved to scan_results.txt"
nmap installation.192.168.1.0/24), applying aggressive scanning options.scan_results.txt) for later analysis.Once the scan is complete, AI-driven operations require analysis of the output data. Python is ideally suited to parsing scan results and extracting actionable insights. Below is a Python script that reads the scan results, identifies hosts with open SSH ports (port 22), and notifies defensive teams of potential entry points.
#!/usr/bin/env python3
"""
Name: parse_scan_results.py
Description: Parse nmap scan results to identify hosts with open SSH (port 22) vulnerabilities.
"""
import re
def parse_nmap_output(file_path):
open_ssh_hosts = []
hostname_pattern = re.compile(r"Host: (\S+).*Ports:.*22/open")
try:
with open(file_path, 'r') as file:
for line in file:
match = hostname_pattern.search(line)
if match:
open_ssh_hosts.append(match.group(1))
except FileNotFoundError:
print(f"File {file_path} not found.")
return open_ssh_hosts
def main():
nmap_output_file = 'scan_results.txt'
hosts = parse_nmap_output(nmap_output_file)
if hosts:
print("Hosts with open SSH ports detected:")
for host in hosts:
print(f"- {host}")
else:
print("No hosts with open SSH ports found.")
if __name__ == "__main__":
main()
scan_results.txt file generated by the Bash script.Beyond simple scans, AI can further analyze these data sets, learning from historical patterns, and improve threat detection continuously. Advanced machine learning models can ingest outputs from tools like nmap, social media sentiment data, and other cyber threat intelligence streams to forecast likely targets or predict disinformation trends.
For example, natural language processing (NLP) models can sift through millions of social media posts in real time, classifying them as authentic or fabricated, and gauge the public mood—a critical step in preemptively identifying emerging irregular warfare operations.
As AI is leveraged offensively in irregular warfare, similar technologies are being developed to defend against these threats. Some key defensive measures include:
Researchers are continuously working on algorithms that can detect deep fakes by analyzing anomalies in digital artifacts such as inconsistencies in lighting, unnatural facial movements, or irregular audio patterns. Many of these detection systems also deploy GAN-based adversarial training to keep pace with increasingly sophisticated deep fake synthesis.
Organizations are integrating AI-powered threat intelligence platforms that help monitor, detect, and respond to disinformation campaigns or cyber intrusions. These platforms gather data from multiple sources—ranging from social media feeds to network logs—and use machine learning to identify patterns that suggest coordinated disinformation attempts or exploitation attempts on critical infrastructure.
AI-driven detection algorithms are being incorporated into cybersecurity frameworks to pinpoint malicious behaviors or compromised systems. These frameworks combine traditional signature-based detection with behavioral analysis enabled by machine learning. For instance, systems might flag unusual patterns in network scanning activities (similar to the outputs from our Bash/Python examples) that suggest an adversary’s recon phase.
The Department of Defense (DoD) and private sector organizations are advancing collaborative defense networks where threat intelligence is exchanged in near real time. These networks employ machine learning and big data analytics to enhance situational awareness, enabling quicker reaction times to disinformation or cyber events initiated by irregular warfare tactics.
As AI platforms such as ChatGPT and Google Bard continue to evolve and become more accessible, both state and non-state actors will likely expand their use. This democratization of AI poses a dual challenge: the need to integrate offensive AI capabilities to stay ahead of adversaries, and the imperative to build resilient defensive systems that can counter AI-generated disinformation and cyber threats.
The DoD should prioritize the seamless integration of AI tools across intelligence, cyber, and warfare operations. By establishing centralized data repositories and cross-domain analytics, the military can harness the power of AI to generate predictive models, assess risk, and deploy countermeasures in real time. Interoperability between AI tools is essential for streamlining operations and ensuring consistent threat evaluation.
In planning irregular warfare and MILDEC operations, the DoD must adopt a dual-use approach that leverages AI offensively—to craft disinformation and deception—and defensively—to detect, neutralize, and reverse adversarial AI efforts. Investing in advanced adversarial machine learning research and continually updating detection frameworks will be critical.
While AI provides tremendous operational advantages, ethical considerations and strategic policies must guide its deployment. The use of AI for disinformation and deep fakes not only carries the risk of unintended consequences but might also escalate conflicts or undermine public trust in institutions. Policy frameworks should be developed that balance operational exigencies with ethical imperatives and international law.
Ensuring that military personnel understand the basics as well as advanced techniques of AI is paramount. This includes not only training in technical implementations—such as coding, data analytics, and network security—but also understanding the psychological and sociopolitical impacts of disinformation. Collaborative training initiatives, workshops, and simulation exercises should become integral to military education and strategic planning.
Artificial Intelligence is indisputably the newest and most potent weapon in the arsenal of irregular warfare. From creating convincing deep fakes to automating disinformation campaigns, AI enhances both offensive and defensive capabilities, fundamentally shifting how nations and non-state actors conduct influence operations, cyber warfare, and economic sabotage. As illustrated by the Pentagon explosion incident and various real-world examples, the rapid spread of AI-generated content can lead to immediate disruptions in financial markets, public opinion, and national security postures.
By integrating advanced AI tools into every aspect of irregular warfare—from initial scanning and network analysis to large-scale disinformation and MILDEC—defense organizations can better anticipate and counter these emerging threats. However, with greater reliance on AI comes the need for robust ethical guidelines, advanced countermeasure development, and continuous training to ensure that the technology serves as a force for strategic stability rather than a source of chaos.
In summary, AI is a game-changer in irregular warfare, and anyone involved in national defense or cybersecurity must be ready to adapt to these new challenges. The future may well be defined by a digital duel between adversarial AI systems, making it imperative for defense strategists, policymakers, and technical experts to remain informed, agile, and collaborative in the face of uncertain digital battlefields.
This blog post provides an in-depth technical exploration of the intersection between artificial intelligence and irregular warfare. It has covered foundational concepts, technical implementations, and the strategic implications of AI in modern conflict scenarios. As AI continues to evolve, staying ahead of these technologies is crucial for ensuring national security and operational success in an increasingly digital battleground.
Disclaimer: The views expressed in this article are those of the author and do not necessarily reflect the official policy or position of any affiliated organization.
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