Generative AI as a Cyberthreat: Autonomous Attacks Without Operator Control

The Carnegie Endowment's 2026 report documents a crisis in information security: generative models are turning into standalone cyberthreats.

The core problem

Generative models are no longer just a tool that an operator picks up, feeds a command and runs. They are becoming independent entities on networks, and they can:

• Automatically generate phishing emails tailored to each target
• Write and optimize malware on the fly
• Adapt to defense systems
• Run for hours without human involvement

Why this is new

There is a big difference between "a hacker wrote malware" and "an LLM writes malware that adapts itself to the defenses." In the second case, even if the human is caught, the system keeps attacking and evolving.

The rapid evolution of LLM services (new versions every month) widens the range of threats. What was impossible a month ago can be done today with a single line.

Implications for monitoring

Established network monitoring systems look for signatures and anomalies. But AI attacks produce new anomalies that signature-based tools have not seen before. What's needed are systems that learn the behavior of LLMs under attack on their own and detect them without explicit signatures.

What this means

Security is entering a new era. While companies invested in WAFs, SIEMs and antivirus software, the cyberthreat evolved toward autonomy and generativity. Defense now requires more than blocking known attacks: it means learning to see the behavior of AI agents in real time.