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Attackers Expose Ongoing AI Tool Use Targeting Organizations in Latin America
Unit 42 documents two AI-assisted intrusion campaigns against Latin American government, utility, and financial organizations using LLM-orchestrated tooling.
Palo Alto Networks Unit 42 tracks two ongoing intrusion clusters, CL-CRI-1131 (Mexican transportation, federal ministries, municipal water utilities) and CL-CRI-1163 (Brazilian financial sector), both using living-off-the-land techniques, SOCKS5 relays, and custom RATs. The attackers appear to orchestrate operations via commercial LLMs like Claude and GPT-4.1, evidenced by iterative batch scripts and AI-generated tunneling tool naming. The Mexican campaign (also reported as Operation Escaneo by CloudSEK) exfiltrated sensitive data via dynamic-DNS infrastructure with rotated multi-SAN TLS certificates between February and June 2026. This signals broader adoption of AI-enhanced operations by diverse threat groups in the region.
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Frontier AI: Vulnerability Management's Systemic Revolution
Opinion: frontier AI like Anthropic's Mythos finds and exploits vulnerabilities at machine speed, forcing vulnerability and patch management programs to overhaul prioritization.
The author argues frontier AI models, exemplified by Anthropic's Mythos, can discover zero-days and chain exploits fast enough to overwhelm traditional vulnerability management. The piece recommends moving beyond CVSS, EPSS and KEV toward exposure management (CTEM) and automated, ring-based patch deployment. It also flags hard trade-offs between patching velocity and uptime requirements that organizations must resolve proactively.
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Your AI agent's system prompt is not a security control
AWS and SANS guidance says system prompts are not security controls; enforce user permissions at retrieval time and default-deny every agent tool invocation.
AWS VP Gee Rittenhouse and SANS fellow Eric Johnson, with three AWS security specialists, published agentic AI security guidance for organizations with agents running or under development. They recommend scoping queries to user permissions inside existing RBAC/ABAC systems and filtering results before the model's context window, noting prompts can be bypassed, ignored, or overridden. The guidance warns risk concentrates when one agent holds sensitive data access, external communication, and exposure to untrusted content, the vector for prompt injection, which OWASP ranks as the top AI application threat. It cites IBM 2025 research that ungoverned shadow AI added $670,000 to average breach cost, and prescribes 30-day behavioral baselines, Cedar or Open Policy Agent for default-deny tool invocation, and four-layer containment with automatic circuit breakers.
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Honeypot-Omaha and batch.py [Guest Diary], (Wed, Sep 2nd)
A SANS ISC guest diary describes batch.py, a Python tool that consolidates honeypot logs and enriches IOCs with threat intelligence data.
Written by a SANS.edu BACS intern, the diary explains analysis of the DShield Honeypot-Omaha sensor, which uses Cowrie to emulate SSH and Telnet and log attacker activity. The author's batch.py script implements a four-phase pipeline with SHA-256-generated master and guest authentication to consolidate JSON and log files, correlate data via external APIs, and produce MITRE, CVE, geolocation, threat-score and fingerprint enrichment for investigated indicators.
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