Real Intelligence Against Weaponized AI by David D Geer

In 2025, cybercriminals weaponized AI to automate adaptive attacks, from smart malware to deepfakes. Defenders must combine AI detection with human oversight, unifying telemetry, auditing models, and training staff to prevent breaches, making security predictive rather than reactive.

1. Real Intelligence Against Weaponized AI

By David D. Geer

Summary:

Artificial intelligence has become both shield and sword. In 2025, cybercriminals
weaponized AI to automate attacks faster than humans can react—forcing
defenders to combine real intelligence with machine precision.

The shifting landscape of AI malware

Attackers once had to hand‑craft phishing lures and malware code. Now they use
large language models and automation frameworks to generate them in minutes.
In 2025, AI‑driven breaches increased, and many ransomware families now
contain AI components. IBM and Fortinet confirm that attackers embed machine
learning inside malware to adapt instantly once inside a victim’s network.

This new breed of malware—often called “smart malware” or weaponized AI—
mimics human decision‑making. It studies defenses, hides in ordinary traffic, and
triggers only when it detects its intended target. Some systems even recognize
facial patterns, voice samples, or GPS data before launching an attack.
DeepLocker and newer variants illustrate how AI cloaking can bypass antivirus
tools altogether.

Why this threat is accelerating

Generative AI has lowered the technical barrier to entry. Scripts, polymorphic
code, and phishing kits that used to require advanced skill are now available
through automated platforms on the dark web. Cybercriminal groups use these
tools to scale operations globally. North America and Europe lead current regional
losses. Still, the Asia‑Pacific region has recorded the fastest growth rate of
AI‑enabled attacks.

Automation allows small teams—or even lone actors—to orchestrate campaigns
once limited to state intelligence agencies. A malicious AI can probe networks for
exposed systems, generate adaptive emails, rewrite its own code when detected,

2. and negotiate with victims through chatbots posing as humans. Every stage of the
attack chain can now run autonomously.

The defender’s dilemma

Legacy cybersecurity models fail when confronted by self‑modifying code.
Firewalls tuned to fixed signatures cannot stop malware that changes its
appearance on every execution. Prompt injection and model poisoning can
manipulate even traditional machine‑learning defenses.

Defenders must respond in kind. Modern security systems are now embedding AI
to detect pattern anomalies and flag unusual behavior. Behavioral analytics can
identify minute deviations—an employee logging in from a new time zone or a
process renaming files too quickly. Yet automation alone cannot fully grasp
context. Without human oversight, an AI defense tool may ignore faint precursors
of an evolving breach or flag false positives that exhaust response teams.

Building smarter, balanced defenses

The strongest environments integrate both human and artificial intelligence.
Organizations can begin by unifying threat telemetry across endpoints, networks,
and cloud instances, then feeding that data into adaptive learning engines.
Coupling algorithmic detection with analysts trained to interpret alerts creates a
resilient feedback loop.

Key practices include:

Conduct AI‑assisted red‑team exercises to test detection speed against simulated
automated malware.

Require vendors to demonstrate AI integrity testing, for example, by providing
proof of models they have hardened against adversarial manipulation.

Audit how AI tools handle sensitive data to prevent accidental leak exposure in
prompts or logs.

3. Evaluate cybersecurity insurers to ensure coverage extends to deepfake‑related
business fraud and AI‑driven ransomware.

Maintain “kill switch” procedures that allow for the instant isolation of automated
systems that are behaving abnormally.

The human element remains decisive

Real defense depends on human judgment. People can evaluate motive, intent,
and nuance that algorithms miss. When security professionals understand how
adversarial AI operates, they recognize that these systems rely on predictable
human responses. Adaptive education for employees—especially around
manipulated audio, realistic phishing, and synthetic identities—closes many of the
gaps attackers exploit.

Looking forward

Weaponized AI marks the next phase of cyber conflict. It is faster, cheaper, and
more scalable than any previous intrusion tool. Yet its success depends on the
same weakness that fuels every digital threat: human complacency. Combining
automation with disciplined human oversight transforms defense from reactive to
predictive.

Artificial intelligence will continue to shape both sides of cybersecurity. The
winners will not be those with the most algorithms—but those who keep humans
in charge of them.

By 2026, autonomous AI warfare will launch adaptive attacks and trade exploits.
Risks include social engineering and deepfakes. Countermeasures require human-
verified AI.