The AI-Assisted Breach of Mexico's Government Infrastructure is a campaign where human operators used Claude Code AI and OpenAI GPT GPT-4.1 AI to perform reconnaissance, vulnerability exploitation, privilege escalation, discovery, credential harvesting, and exfiltration operations. The attack impacted several Mexican government institutions including the Federal Tax Authority (Servicio de Administracion Tributaria), Mexico State Government (Estado de Mexico), Mexico City Civil Registry (Registro Civil de CDMX), Jalisco State Government, National Electoral Institute (Instituto Nacional Electoral), Michoacan State Government, SADM Monterrey Municipal Water Utility (Agua y Drenaje), Tamaulipas State Government, and the Mexico City Health Department (Salud CDMX). The attack resulted in the theft of large amounts of sensitive government data and civilian records. Please note that due to the breadth of this campaign, public reporting only provides select details and as a result this campaign should not be considered comprehensive.[1]
66
TechniquesTop 4%
13
TacticsTop 11%
1
Tool or malware46th pct
0
Threat groupsNone recorded
Enterprise ATT&CK only. Any Mobile or ICS rows on the same ATT&CK page are not carried.
AI-Assisted Breach of Mexico's Government Infrastructure MITRE reference C0064
During the AI-Assisted Breach of Mexico's Government Infrastructure, the adversary requested Claude Code gather credentials against the victim environment and Claude Code utilized DCSync to pull password data from the Active Directory.6
Reach is how much of ATT&CK this campaign touches. Coverage is how well defended each thing it does is, as a median per technique rather than a total — a total would just restate the reach. Each figure is ranked against all 57 ATT&CK campaigns only where that population actually spreads. Where most of the population shares one value, a percentile would rank the tie instead of the entity, so the raw value is shown and no rank is claimed.
Reach
Techniques66Top 4%
96th percentile · 96% of 57 ATT&CK campaigns have this many Enterprise techniques or fewer.
Tactics13Top 11%
89th percentile · 89% of 57 ATT&CK campaigns have this many tactics spanned or fewer.
Tool or malware146th pct
46th percentile · 54% of 57 ATT&CK campaigns have more tools and malware.
Threat groups056% tie · 3 values
56% of the population shares a single value across only 3 distinct values, so a percentile here would rank the tie, not the entity.
Coverage
Rules / technique11.5med35th pct
35th percentile · 65% of 57 ATT&CK campaigns have more detection rules per technique.
Tests / technique1.5med37th pct
37th percentile · 63% of 57 ATT&CK campaigns have more atomic tests per technique.
Data comps / technique4med44% tie · 7 values
44% of the population shares a single value across only 7 distinct values, so a percentile here would rank the tie, not the entity.
Mitigations / technique2med40% tie · 8 values
40% of the population shares a single value across only 8 distinct values, so a percentile here would rank the tie, not the entity.
5 of 8 axes carry an honest rank on this page. Coverage medians are taken across this campaign’s 66 Enterprise techniques.
Range
Technique Campaign use over its cumulative rule count.
Detects the execution of a text-based file access or inspection utilities to read the content of /etc/sudoers in order to potentially list all users that have sudo rights.
The following analytic identifies access to the /api/v1/configuration/users/user-roles/user-role/rest-userrole1/web/web-bookmarks/bookmark endpoint, which is associated with CVE-2023-46805 and CVE-2024-21887 vulnerabilities. (...)
This method uses uncommon error codes on failed logons to determine suspicious activity and tampering with accounts that have been disabled or somehow restricted.
The following analytic detects potential exploitation of the Adobe ColdFusion vulnerability, CVE-2023-26360, which allows unauthenticated arbitrary file read. (...)
The following analytic detects unauthenticated requests to an Amazon EKS Kubernetes cluster, specifically identifying actions by the "system:anonymous" user. (...)
Mark Morowczynski '@markmorow', Bailey Bercik '@baileybercik'
2022-06-01
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248649b7-d64f-46f0-9fb2-a52774166fb5
Resource owner password credentials (ROPC) should be avoided if at all possible as this requires the user to expose their current password credentials to the application directly. (...)
Mark Morowczynski '@markmorow', Bailey Bercik '@baileybercik'
2022-06-01
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55695bc0-c8cf-461f-a379-2535f563c854
Detects when AppLocker "Audit only" enforcement mode reports that an Application, DLL, Script, MSI, or Packaged-App would have been blocked if AppLocker "Enforce rules" enforcement mode was enabled.
Identifies two sign-ins originating from geographically distant locations, where at least one of the locations may also be atypical for the user, given past behavior.