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AI-powered cyber threats

AI is making attacks cheaper, and deepfakes and automated social engineering are growing fast. The clearest recorded case is Chinese: in 2025 Anthropic found a Chinese state group letting AI do 80 to 90% of the work against about 30 targets, with people making the key decisions. The evidence does not yet show attackers pulling ahead: VulnCheck's data for the first half of 2026 show no rise in early exploitation.

high conviction growth

Research snapshot · updated Nov 10, 2026

State of the art (2026)

The threshold moment was Anthropic's November 2025 disclosure of GTG-1002, a Chinese state-sponsored group that turned Claude Code into an autonomous espionage operator against roughly thirty targets — the model executed an estimated 80–90% of the campaign with human operators intervening for only minutes. That collapses the old comfort that AI merely advises attackers; it now runs the operation. The commodity layer has moved in parallel: agentic tooling automates reconnaissance, payload delivery and privilege escalation, the bulk of phishing is now AI-generated, and deepfake voice and video impersonation has become routine in fraud after the Arup case. Defensive AI — XBOW-style autonomous pen-testing, Microsoft Security Copilot, agentic SOC tooling — is real but still trails offensive capability, leaving enterprises exposed during the lag.

Core thesis

Attackers now use AI to do much of the work of a break-in: finding weaknesses, writing malicious code and moving through a network. Anthropic says a Chinese state group used its Claude Code tool to carry out 80 to 90% of a 2025 spying campaign against about 30 targets, with people stepping in at a handful of decisions in each attack. People still choose the targets and decide what to do with what is stolen. An attack that runs from start to finish with nobody directing it has not been shown in public. AI helps attackers sooner than defenders because finding a flaw is faster than getting every affected organisation to fix it.

Explore the deeper analysis in CanaryIQ

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Signal stack

Evidence stacked leading → lagging

11 signals
talent
research
patent
expert
operational
regulatory
market

Technology-native KPIs

Metrics that predict trajectory, tracked over time

1 tracked
Machine identities vs human employees

Landscape map

Who builds what, and who depends on whom

72 players · 6 layers

Catalyst calendar

Dated events that will move the position

4 ahead

Technology roadmap

Milestones on the path to maturity

8 milestones

Watchlists

Companies, people and papers, each with a condition for removing it

20 · 20
Companies · 20
People · 20

Decision frameworks

What our view means for your job

Locked
Public Equity
PE / VC
Corporate Leader

Thesis changelog

When our view changed, and why

7 updates

Change our mind

2 disconfirming conditions

You've read the verdict. The file is much deeper.

The full signal stack, technology-native KPIs tracked over time, the landscape of who depends on whom, the dated catalyst calendar, decision frameworks for every desk, live watchlists and a record of every time our view on AI-powered cyber threats has changed. All of it is in CanaryIQ.