How neural networks hunt for vulnerabilities: a new race in cybersecurity
The cybersecurity arms race is entering a new phase. Attackers are using generative AI to find vulnerabilities quickly and build working exploits for real-world applications. Defenders are building protection and detection on the same machine learning technologies. The first tools are already leaving research labs and becoming real weapons in live operations.
AI-processed from Wired; edited by Hamidun News
How neural networks and AI are changing cybersecurity: the arms race has entered a new phase. Vulnerability hunting used to be manual work: researchers read code, ran pentests and logged their findings. Now both sides of the conflict, attackers and defenders, run AI to look for bugs across millions of lines of code at once. That has turned one of the most critical areas of security into a genuine arms race between machines.
How does AI speed up vulnerability discovery?
Generative AI can analyze millions of lines of code at once, finding vulnerabilities hundreds of times faster than specialists reviewing code line by line by hand.
Why is this an arms race?
Because both sides of the conflict, attackers and defenders, use the same AI tools: one side builds exploits faster, the other catches bugs earlier.
Key Questions Answered
How does AI speed up vulnerability discovery?
Generative AI can analyze millions of lines of code at once, finding vulnerabilities hundreds of times faster than specialists reviewing code line by line by hand.
Why is this an arms race?
Because both sides of the conflict, attackers and defenders, use the same AI tools: one side builds exploits faster, the other catches bugs earlier.
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