When evolution stops being random … Over 80 years ago, Alexander Fleming witnessed what he feared most - bacteria evolving resistance to penicillin within a decade of its mass introduction; yet, despite his public warnings, the cascade of resistance emerged precisely as foreseen - triggering an evolutionary arms race where successive antibiotic deployments accelerated rather than slowed resistance emergence, collapsing therapeutic horizons across bacterial species.

In leading edge research released this month, Google's Threat Intelligence Group identified regenerative AI-native malware families employing just-in-time code regeneration, via LLMs, to rewrite their entire source code hourly to evade detection - rendering traditional incident responses fundamentally misaligned with threat velocity.

Whilst biological pathogens evolve through random mutations constrained by metabolic costs and generational timescales, AI-powered malware achieves goal-directed adaptation orders of magnitude faster through intentional queries to LLMs - not merely another arms race escalation, but a fundamental phase transition toward autonomous threat design.​

Can we build governance frameworks to anticipate threats that design their own evolution in real time?

Profiled research:

AI-POWERED MALWARE: AUTONOMOUS ADAPTATION:

https://cloud.google.com/blog/topics/threat-intelligence/threat-actor-usage-of-ai-tools;

ADVANCED JAILBREAK TECHNIQUES: ECHO CHAMBER CONTEXT-POISONING:

https://neuraltrust.ai/blog/echo-chamber-context-poisoning-jailbreak;

AI AGENT SECURITY FRAMEWORKS: B3 BENCHMARK:

https://securitybrief.com.au/story/open-source-b3-framework-to-benchmark-ai-agent-security-unveiled;

DIFFERENTIAL PRIVACY IN ENTERPRISE AI:

https://arxiv.org/pdf/2501.18914;

MULTI-AGENT DEFENCE PIPELINE AGAINST PROMPT INJECTION:

https://arxiv.org/abs/2509.14285

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