Using CNN, GAN, and RNN for red team operations

Learn about the use cases of CNN, GAN and RNN machine learning models for red team purposes.

Apr 02, 2025

Advanced machine learning techniques, such as Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), and Recurrent Neural Networks (RNNs), can be used to improve red team tactics. These techniques can help red team operators bypass security measures, generate realistic phishing emails, and analyze patterns.

Convolutional Neural Networks (CNNs)

CNNs are mainly used in image recognition and processing. They work by analyzing an image’s features through multiple layers, identifying patterns that help classify or detect objects. CNNs are widely used in facial recognition and other vision-based AI systems.

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In red team operations, CNNs can be used for various tasks:

Automated Object Detection – CNNs can be trained to detect security cameras, biometric scanners, or other physical security controls in an environment. This helps attackers identify vulnerabilities in physical security systems.
Pattern Recognition for Reconnaissance – CNNs can analyze aerial or satellite images of target buildings to identify security perimeters, entry points, or guard patrol patterns, helping with pre-attack planning.

For example, a red team operation can utilize a CNN model trained on satellite imagery to analyze a corporate facility's layout. The model can be used to identify weak points in perimeter security and blind spots in camera coverage, helping the team plan an undetected physical intrusion.

Generative Adversarial Networks (GANs)

GANs consist of two competing neural networks: a generator and a discriminator. The generator creates fake images or data, while the discriminator tries to distinguish between real and fake inputs. This back-and-forth process results in highly realistic outputs.

Red teams can use GANs to generate realistic phishing emails, deepfake audio or video, and synthetic identities. GANs can also create synthetic fingerprints to bypass biometric security.

Recently, a well-known case involved attackers using deepfake voice technology to imitate a company CEO, instructing an employee to wire millions of dollars to a fraudulent account. The voice was realistic enough that the employee followed the instructions without suspicion, demonstrating the potential of GANs in red team operations.

Recurrent Neural Networks (RNNs)

RNNs are designed to analyze sequential data, making them useful for language modeling and pattern recognition. They remember previous inputs and use that context to predict future data points, making them ideal for text and speech-related tasks.

Red teams can use RNNs to generate convincing phishing emails and predict network activity patterns. An RNN-based model can learn from past phishing attacks and generate new, highly personalized emails that increase the likelihood of a successful attack. Similarly, red team operators can analyze network traffic logs to predict security team responses and time their attacks accordingly.

For example, an RNN can be trained on a dataset of successful phishing emails. The AI-generated messages will be more effective in tricking users than manually crafted ones because they would be more closre to the recipient’s language patterns and writing style.

Red Team Notes
Machine learning techniques like CNNs, GANs, and RNNs provide new ways for red teams to challenge security defenses. CNNs can be used for object detection, steganography, and reconnaissance, GANs enable realistic deepfake attacks, and RNNs enhance phishing and network analysis.