Offensive security (OffSec) is the new gold standard for cybersecurity. This proactive approach helps organizations uncover network, application, and infrastructure vulnerabilities before attackers can exploit them.
Traditional penetration testing is a standard OffSec tool that, while effective, still requires manual effort. Fortunately, advances in OffSec technology allow pentesting to be faster and more automated than ever before.
However, attackers are now leveraging artificial intelligence (AI) tools to execute more sophisticated attacks and, in some cases, exploit organizations’ own large language models (LLMs) for nefarious purposes.
This evolution demands a fresh approach to security testing, one that goes beyond firewalls and endpoints to probe the very logic that powers AI. In this guide, you’ll learn how traditional pentesting works, why it isn’t enough, and how AI can improve OffSec pentesting.
Traditional offensive security pentesting follows a structured, methodical process aimed at identifying and safely exploiting vulnerabilities in systems, networks, and applications.
Unlike red team offensive security, where a group of ethical hackers uses real-world strategies to exploit weaknesses, penetration testing examines the vulnerabilities of specific applications or infrastructure.
Traditional pentesting occurs manually, where a designated pentester follows a rules of engagement (RoE) document that outlines exactly what to test, when, and how. This approach isn’t as creative or holistic as red teaming.
Still, regular pentesting is crucial to double-check post-red teaming mitigations, as well as checking patches and other critical updates.
Furthermore, the manual nature of traditional pentesting can make it slow to respond to new threats. It’s also cumbersome for smaller organizations to execute, especially if they lack internal cybersecurity resources.
That’s why more organizations are embracing a new way of preparing for cyber attacks, thanks to AI-powered attack simulations.
Traditional pentesting focuses on testing human-built applications, networks, and infrastructure. It also requires manual, human-led testing to look for vulnerabilities.
The amount of time required for this type of testing is not only inefficient but also exposes organizations to an increasing number of advanced threats against their systems.
With AI systems now making critical decisions in finance, healthcare, national security, and beyond, cybersecurity teams are now tasking AI with testing itself with AI pentesting tools and other solutions.
Unlike conventional systems, AI models—especially LLMs and machine learning algorithms—have unique vulnerabilities. These include adversarial attacks (tricking models into making wrong predictions), data poisoning, model inversion, and prompt injection attacks.
These are risks that traditional pentesting alone isn't equipped to handle.
Not only can AI pentesting execute traditional attack simulations like port scanning and phishing, but it can also execute realistic attack simulations for:
The best part about this new approach is that it happens automatically, with little input from human cybersecurity teams. In an environment where attacks target your organization from all angles, this always-on approach to security provides organizations with a new method for preparing for cyber threats.
Traditional approaches to offensive security penetration testing are still excellent for spotting potential vulnerabilities. However, this approach doesn’t allow organizations to move with agility, which is essential for staying ahead of today’s evolving threats.
Traditional pentesting isn’t enough on its own; businesses must also embrace automated tools to not only identify issues with traditional systems, but also with AI-powered tools. By expanding security strategies to include AI attack simulations, businesses aren't just protecting their technology—they're securing their future.
To meet the challenges of modern threats, many businesses are now turning to offensive security service providers that offer specialized expertise and automated solutions for AI and traditional systems alike.
It’s time to put your AI security on autopilot with Mindgard’s Offensive Security solution. Request a Mindgard demo to set a new security standard for your business.
Pentesting AI systems goes beyond finding code vulnerabilities. It focuses on how models behave when given adversarial inputs, how they process data, and their resilience to manipulation or unexpected prompts.
Traditional application security primarily focuses on system flaws, while AI pentesting examines logical, behavioral, and training-based vulnerabilities.
Yes, many adversarial attacks can now be automated using tools like Mindgard and other specialized frameworks. Automation enables faster, scalable testing across models, which is crucial as AI systems become increasingly complex and deeply embedded in critical workflows.
Signs can include abnormal outputs like unexpected or harmful recommendations, information leaks, unauthorized access to internal model logic, or subtle performance degradation. Because attacks can be subtle, robust monitoring and periodic adversarial testing are crucial for detecting compromises early.
