Hacker Uses Claude and ChatGPT to Breach Multiple Government Agencies

A single threat actor compromised nine Mexican government agencies and stole hundreds of millions of citizen records in a highly sophisticated cyberattack.

The campaign, which ran from late December 2025 through mid-February 2026, highlights a dangerous shift in the modern threat landscape.

Researchers at Gambit Security recently released a full technical report detailing how the attacker relied on two major commercial artificial intelligence platforms. The publication was initially delayed to allow the affected agencies time to complete their incident response efforts.

AI Models Power the Breach

The attacker used Anthropic’s Claude Code and OpenAI’s GPT-4.1 not just for planning, but as core operational tools that drastically accelerated the attack.

According to forensic evidence recovered, Claude Code generated and executed approximately 75% of all remote commands during the intrusion.

Across 34 active sessions on live victim infrastructure, the hacker logged 1,088 individual prompts. These prompts translated into 5,317 AI-executed commands, demonstrating how deeply the AI was integrated into the exploitation phase.

Simultaneously, the attacker leveraged OpenAI’s GPT-4.1 for rapid reconnaissance and data processing. The hacker developed a custom 17,550-line Python script designed to pipe raw data harvested from compromised servers directly through the OpenAI API.

This automated system analyzed information across 305 internal servers, rapidly producing 2,597 structured intelligence reports. By automating the data analysis phase, a single operator successfully processed an intelligence volume that would traditionally require an entire team.

The integration of artificial intelligence allowed the attacker to turn unfamiliar networks into mapped targets in hours rather than days. Recovered materials showed the attacker possessed over 400 custom attack scripts.

Furthermore, the hacker used AI to quickly develop 20 tailored exploits targeting 20 specific Common Vulnerabilities and Exposures (CVEs). This high-speed capability compressed the attack timeline, allowing the threat actor to operate well below standard detection and response windows.

Despite the advanced methods used in the campaign, the actual vulnerabilities exploited were highly conventional. The targeted government agencies had basic security gaps that enabled the attacker to gain initial access and move laterally.

The underlying issues were addressable through standard security controls, highlighting a severe accumulation of technical debt within mission-critical infrastructure.

While artificial intelligence has significantly lowered the cost and complexity of executing widespread cyberattacks, the defense strategy remains rooted in foundational security practices.

Organizations must urgently address unpatched software and implement strict credential rotation policies. Enforcing network segmentation is also critical to restrict lateral movement once a perimeter is breached.

Finally, deploying robust endpoint detection and response tools is necessary to identify these rapidly compressed attack timelines before data exfiltration occurs.

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