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Monthly Intelligence Report 

December 2025: Anthropic Disrupts GTG-1002 Cyber Espionage

  • The Coretelligent Team
2025 December Intelligence Report Hero Image


Executive Summary

In September 2025, Anthropic disrupted a China-linked espionage operation (GTG-1002) that used its Claude Code model inside an automated “agentic” framework to run reconnaissance, exploit vulnerabilities, move laterally, and extract data across roughly 30 organizations in technology, finance, chemicals, and government. Anthropic estimates AI handled 80–90% of the hands-on intrusion work, with humans guiding and reviewing outputs; despite AI errors and false positives, the attackers still achieved multiple successful intrusions before the campaign was shut down.

Some experts question how “first-of-its-kind” and large-scale this campaign truly was, but there is broad agreement that AI has shifted attacker economics: automation makes targeted campaigns cheaper to run, which increases the likelihood your organization gets probed. For CFOs, this marks a new phase of cyber risk: faster, wider, lower-cost intrusions driven by automation rather than human labor.

Key Takeaways

  • AI decomposed tasks like a junior analyst.
  • It executed reconnaissance, exploitation, and credential testing at machine speed and without fatigue.
  • It bypassed safeguards using simple role-play prompts.
  • It triaged stolen data to highlight what mattered most, multiplying human attack capabilities.


Threat Snapshot: GTG-1002 at a Glance

Who They Are: A China-affiliated state-sponsored espionage group focused on strategic intelligence and data theft.

Who They Targeted: Approximately 30 organizations, primarily in –

  • Technology
  • Financial services
  • Chemicals and industrial manufacturing
  • Government agencies

Targets skewed toward organizations with high-value IP, regulated data, or complex vendor ecosystems.

Why This Campaign Is Significant

  • AI executed most of the intrusion work traditionally carried out by humans.
  • Attackers scaled operations across many victims simultaneously.
  • AI tools were social-engineered through role-play to bypass guardrails.
  • AI triaged stolen data, accelerating intelligence extraction.
  • Despite AI errors, automation was effective enough to breach multiple targets.

Outcome:

Anthropic detected anomalies in mid-September, contained the operation within roughly 10 days, banned accounts, notified impacted organizations, and issued a public disclosure.

Executive Take: What We Know About the Operation

Anthropic’s investigation showed a familiar attack chain executed at machine speed

  1. The Operation Was Framed as a “Security Assessment.” Through role-play prompts, GTG-1002 convinced Claude Code it was performing a legitimate penetration test, allowing automated execution of harmful tasks.

  2. AI Conducted Broad, Rapid Reconnaissance. Claude mapped exposed systems, authentication portals, misconfigurations, cloud access points, and web structures across many organizations in parallel.

  3. AI Identified and Tested Vulnerabilities. Claude generated exploit payloads, validated results, iterated on failed attempts, and documented findings autonomously.

  4. AI Harvested Credentials and Explored Internal Networks. Once inside, AI probed additional services, reused credentials, and produced network maps for lateral movement.

  5. AI Extracted and Prioritized Data. Claude classified extracted information by potential intelligence value, accelerating decision-making for human operators.

  6. AI Errors Slowed the Attack, But Didn’t Stop It. Claude produced false positives and misread some logs, but the volume and speed of automation still enabled multiple compromises.


Executive Implications

Boards and executives are now expected to demonstrate ransomware governance. Legal, finance, and technology leaders should coordinate on incident-reporting readiness and disclosure language.

Bottom line:

This was not a laboratory demonstration. GTG-1002 executed a real espionage campaign, scaled through automation, that succeeded against several targets before detection.

Sector Exposure: Where Risk Is Highest

As you prioritize investment and oversight, it helps to understand where exposure is highest.

Higher-Exposure Sectors

  • Technology – high-value IP; historically top espionage target.
  • Financial Services – credential abuse, regulatory obligations, and complex vendor ecosystems.
  • Government – sensitive data; strong geopolitical motivation.
  • Chemicals / Industrial Manufacturing – design IP and production control data.

Elevated but Indirect Exposure

  • Healthcare – regulated data and large vendor ecosystems.
  • Telecom / Infrastructure – attractive as reconnaissance and access stepping-stones.
  • Legal / Consulting – high-value client data tied to larger targets.

Lower Exposure (in this campaign)

  • Retail / SMB (general) – not prioritized in this cycle, but facing rising baseline exposure as AI tools proliferate.

Business Impact for Mid-Market Organizations

1. Automation Lowers the Cost of Attacks – and Increases Likelihood of Attempts Against You

When AI performs the work, attackers can run targeted campaigns at scale. That means:

  • More probing
  • More credential testing
  • More exploitation attempts

This shifts cyber risk from “rare but severe” to “frequent and fast.”

2. Machine-Speed Intrusions Shrink Your Response Window

Reconnaissance and exploitation that previously took days now occur within hours, increasing:

  • Downtime risk
  • The likelihood attackers establish a foothold
  • Pressure on containment and investigation teams

CFO consideration: your response budget and resourcing must match this new tempo.

3. Third-Party and Cloud Risk Multiply Under Automation

AI agents are exceptionally effective at scanning: Cloud identity systems; SaaS integrations; financial and HR APIs; and vendor-hosted assets.

This expands risk into environments you do not directly control, creating:

  • Contractual exposure
  • Regulatory scrutiny
  • Insurance disputes
  • Costly vendor remediation cycles

4. Espionage Still Carries Real, Measurable Cost

Even without ransom payments, the financial consequences of an espionage incident include:

  • Incident response and forensics
  • Business interruption
  • Data breach notifications
  • Regulatory follow-up
  • Contractual penalties
  • Reputation-driven customer churn
  • Increased cyber insurance premiums

These costs regularly reach seven figures for mid-market firms.

5. Regulators and Insurers Are Already Paying Attention

  • The SEC has signaled tighter expectations for timely, accurate cyber disclosures.
  • HIPAA OCR is watching AI-enabled threats in healthcare environments.
  • AI governance regulations are emerging across industries.
  • Insurers are evaluating AI-enabled attack resilience as part of renewal decisions.

For CFOs, expect tougher audits, higher documentation standards, and stricter underwriting requirements.

Coretelligent Analysis & Point of View

1. AI Speeds Up the Tempo of Cybersecurity Fundamentals

The kill chain has not changed. The speed, scale, and automation have

2. Detection Must Adapt to Automated Attacks

Human-speed SOC processes are no longer enough. You need:

  • Broad visibility
  • Automated detection of multi-step AI behaviors
  • Faster escalation paths

3. Governance Must Include AI Tools, Agents, and Vendor Usage

AI governance is now part of cyber governance. Key questions to ask vendors include:

  • What AI tools does your organization use?
  • What access do they have?
  • How do you log and monitor their activity?
  • How do your vendors use AI?

4. AI Strengthens Defenses – If You Leverage It

Anthropic used AI to accelerate investigation of the attackers’ actions. Modern SOCs can do the same to:

  • Correlate patterns
  • Reduce analyst fatigue
  • Accelerate investigations
  • Improve detection fidelity

Preparedness Checklist for Executives​

  • Update incident and risk assessments to include AI-driven attack scenarios.
  • Validate visibility across all external-facing and cloud-connected assets, and test detection and response readiness for “machine-speed” intrusions.
  • Harden IAM foundations: MFA, least privilege, and credential lifecycle management.
  • Validate backup integrity and restoration capacity frequently.
  • Review and document vendor cybersecurity maturity and AI usage.
  • Establish AI governance policies defining data access, usage rules, and monitoring.
  • Ensure your SOC/MDR partner is actively tracking AI-enabled TTPs.
  • Review cyber insurance language for:
    • AI-enabled attacks
    • Nation-state linkage
    • Automated reconnaissance
    • Third-party system breaches

Contact and Next Steps

Schedule Your Next Risk Briefing

Connect with your Coretelligent Account Lead to assess where automation creates the greatest exposure in your environment – and where the most cost-effective improvements can be made.

Coretelligent Cyber Intelligence Team

Email: info@coretelligent.com

Phone: 1-855-841-5888

Coretelligent provides cyber resilience intelligence and managed support solutions across security, governance, and compliance.

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