Cybersecurity is locked in a perpetual arms race. For every new defense, a new attack method emerges. Now, Artificial Intelligence (AI) has entered the battlefield, not as a defender, but as a formidable weapon for cybercriminals. Professional hacking groups are leveraging AI to discover vulnerabilities and automate attacks at a scale and speed that humans can’t match. This post explores how they do it and how a modern defense, Runtime Application Self-Protection (RASP), can stop these AI-generated threats in their tracks.
Rise of the Machines: AI as an Exploit Factory
Cybercriminals are now using AI, specifically Large Language Models (LLMs) and machine learning algorithms, as a force multiplier. An AI can be trained on a massive dataset of publicly available source code from platforms like GitHub, combined with databases of known vulnerabilities like the Common Vulnerabilities and Exposures (CVE) list.
- Known Vulnerabilities (CVEs): By analyzing the code of a patched vulnerability, the AI learns to recognize the specific patterns and flaws that created it. It can then scan a target organization’s codebase or applications with surgical precision, looking for the same unpatched vulnerabilities. It can identify a target running an old version of Apache Struts, for example, and instantly flag it as vulnerable to an exploit like the infamous CVE−2017−5638.
- Zero-Day Vulnerabilities: This is where AI becomes truly dangerous. Instead of just looking for known patterns, it can identify anomalous code structures—subtle deviations from secure coding practices that don’t match any known CVE. By recognizing the characteristics of vulnerable code (e.g., improper handling of memory, potential for buffer overflows), the AI can predict and pinpoint brand-new, zero-day vulnerabilities that human researchers haven’t found yet.
Automating the Attack
Once a vulnerability is found, the AI doesn’t stop. It can then write the exploit code itself. It understands the context of the vulnerability and can generate the precise payload needed to exploit it. This entire process, from discovery to weaponization, can be fully automated, creating a relentless, high-speed attack pipeline.
A real-world parallel to this level of automation can be seen in the WannaCry and NotPetya attacks. While not AI-generated, their worm-like capabilities allowed them to spread automatically and encrypt entire networks in hours. They exploited a known vulnerability (MS17−010, also known as EternalBlue) with devastating speed. An AI-driven attack would operate on the same principle but could potentially discover the vulnerability and launch the attack autonomously.
Imagine a future attack where an AI is tasked with breaching a specific company. It could:
- Continuously scan the company’s public-facing applications.
- Discover a novel zero-day flaw in a third-party library the company uses.
- Write a unique, polymorphic malware that changes its signature with each execution to evade detection.
- Launch a targeted attack, exploit the vulnerability, and exfiltrate data.
This entire sequence could occur in minutes, long before a human security team could even register an alert.
RASP as a Countermeasure: Fighting Code with Code
How do you defend against an automated, AI-driven attack that uses exploits no one has ever seen before? Traditional defenses like Web Application Firewalls (WAFs) and signature-based antivirus are too slow and reactive. They rely on knowing what an attack looks like. The answer lies in protecting the application from the inside out during execution. This is Runtime Application Self-Protection (RASP).
RASP is integrated directly into an application’s runtime environment (e.g., the Java Virtual Machine or .NET CLR). Think of it not as a gatekeeper standing outside the city walls, but as an intelligent security agent embedded within the application itself.
Because it operates from within, RASP has deep context into how the application is supposed to behave. It doesn’t rely on matching attack signatures. Instead, it monitors the application’s execution in real-time. When an exploit—even a zero-day—forces the application to do something dangerous or illegitimate, RASP immediately intervenes.
For example, if an AI-generated exploit attempts to:
- Execute a shell command: An attack might try to force the application to spawn a command shell (e.g.,/bin/sh) to take over the server. RASP sees this illegitimate request and terminates it.
- Perform SQL Injection: If an attacker tries to manipulate a database query to dump sensitive information, RASP identifies the malicious query structure and blocks it before it reaches the database.
- Carry out a Path Traversal Attack: If an exploit tries to access restricted files outside of the web root (e.g., ../../etc/passwd), RASP recognizes the illegal file system request and stops it.
RASP neutralizes the technique, not the specific exploit. It doesn’t need to know about the vulnerability (CVE−2025−XXXX) or the AI that wrote the malware. It only needs to know that a secure application should never execute arbitrary commands or access restricted files. By blocking the malicious behavior itself, RASP effectively renders both known and zero-day attacks harmless, even when they are AI-generated and fully automated.
In the escalating conflict between attackers and defenders, AI is the ultimate offensive weapon. It grants criminals unprecedented speed and scale. But security that operates at the point of impact—at runtime—provides the most robust and future-proof defense. By focusing on an application’s correct behavior rather than an attacker’s signature, RASP offers a powerful way to ensure that even the most advanced AI-powered threats are stopped dead in their tracks.
To learn more about how Waratek RASP defends against AI-driven attacks, request a demo today.
