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Researchers observe first ‘near-autonomous’ AI attack on government target in Taiwan
Dream researchers observed the first near-autonomous AI attack on a government target, with suspected Chinese hackers stealing 2,500+ Taiwan records.
Israeli firm Dream reported that suspected Chinese hackers used open-source AI models to run a near-autonomous cyberattack against Taiwan's government, extracting over 2,500 personnel records. The framework, built on Hermes and OpenClaw, adapted mid-operation without human intervention, ran autonomous 'Learning Cycles' researching applicable vulnerabilities, and expanded to supply chain vendors, a nuclear safety agency, a government email system, and seven-plus energy companies. Attackers bypassed safety guardrails by framing the work as authorized penetration testing. Dream discovered the operation via a 160MB online archive of nearly 1,400 files.
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Hackers Use Autonomous AI Agents to Harvest Thousands of Credentials in Under 6 Hours
Google Cloud documents a financially motivated actor using autonomous AI agents on a compromised cloud tenant to harvest 23,800+ credentials in under six hours.
Google Cloud reports that an attacker compromised a victim's cloud environment and deployed a multi-agent framework driven by preconfigured Markdown playbooks to autonomously handle vulnerability scanning, credential collection, error troubleshooting, and IP rotation. An exposed command-and-control server hosted the 'Recon' framework with a live dashboard managing over 23,800 harvested secrets, including cloud and AI-service API keys. The report also ties DUSTMAKER to UNC6780/TeamPCP, targeting AI development tools and CI/CD systems via trojanized MCP packages such as tiktoken_mcp. Google has disabled linked assets and updated protections after the actors' operational security failures.
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Hackers Deploy Agentic AI to Automate Exploitation and Mass Credential Harvesting
Google GTIG documents a financially motivated actor using a multi-agent AI framework to automate credential harvesting, compromising over 23,800 secrets within hours.
Google Threat Intelligence Group (GTIG) documented a financially motivated actor that compromised an unnamed organization's cloud infrastructure and used a multi-agent AI framework to automate vulnerability scanning, credential harvesting, troubleshooting, and IP rotation. The operation went from planning to mass credential compromise in under six hours, harvesting more than 23,800 secrets including cloud and AI-service API keys via an exposed C2 dashboard called 'Recon'. The actor directed specialized agents using an AI coding chatbot and Markdown instruction files such as AGENTS.KNOWLEDGE.md and agentic_vuln_research.md. Google has not observed fully autonomous zero-day exploitation; the shift automates labor-intensive tasks like reconnaissance, account validation, and infrastructure management, sharply shrinking detection windows.
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Hackers build AI frameworks for widescale credential theft
Google GTIG details threat actors using autonomous multi-agent AI frameworks to automate attacks, including a six-hour credential-harvesting campaign and a 23,800-secret Recon panel.
Google Threat Intelligence Group (GTIG), drawing on Mandiant telemetry, reports threat actors are moving from AI coding assistants to autonomous multi-agent frameworks that automate vulnerability scanning, credential harvesting, troubleshooting, and IP rotation. In one incident, a financially motivated attacker compromised cloud infrastructure and deployed such a framework, harvesting thousands of third-party credentials in under six hours. An exposed C2 server hosted the 'Recon' framework, managing over 23,800 harvested secrets including API keys, with OpenClaw artifacts. GTIG also documents China-linked espionage actors building AI-assisted exploitation pipelines and Russia-based UNC5792 automating Telegram monitoring, while noting fully autonomous zero-day discovery is not yet widespread.
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AI-Generated Exploit Scripts Target Siemens S7 PLCs in U.S. Critical Infrastructure
NSA, CISA, FBI, DOE and EPA warn that actors use AI-generated Python scripts to exploit internet-exposed Siemens S7 PLCs at U.S. critical infrastructure.
A joint advisory from NSA, CISA, FBI, DOE and EPA describes an active threat in which AI-generated exploit scripts disguised as legitimate monitoring tools target Siemens S7 Series PLCs (S7-200 through S7-1500, including F-series safety controllers). The actors use Censys and ZoomEye scanning to find internet-exposed, outdated PLCs and a custom Python script using snap7 libraries over the S7comm protocol for initial access, credential access and denial of service; no attribution was given. Targeted sectors include Critical Manufacturing, Energy, Water and Wastewater, Chemical, Food and Agriculture, and Commercial Facilities, with potential for process disruption and cascading impacts. A related Dream report details a near-autonomous AI-agent campaign (July 1-4, 2026, 12 waves) against Taiwan government entities, using Hermes and OpenClaw agents with eight parallel sub-agents to crack 85 accounts via password spraying and exfiltrate over 2,564 personnel records, SSO client secrets and database credentials.
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Extortion crews have their eyes on high-value AI data, Google warns
Google's Mandiant warns extortion crews now steal proprietary AI models, prompts, and research for ransom, while threat actors automate attacks with agentic AI.
Google's AI Threat Tracker details intrusions in which extortion crews exfiltrated healthcare drug research and a proprietary AI model, and stole an AI media company's source code, prompts, skills, model scripts, and secrets, threatening leaks unless paid. TeamPCP (UNC6780), behind large open-source supply chain attacks on PyPI, npm, and Docker Hub since March, used a malicious GitHub Actions workflow to exfiltrate a company's proprietary AI repository. Mandiant also observed a fully autonomous multi-agent credential-harvesting cloud intrusion completed in under six hours, and a China-linked espionage group using Gemini to build a dynamic automated penetration-testing framework.
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Hackers Weaponize Agentic AI to Automate Reconnaissance, Exploitation and Post-Exploitation
Google GTIG reports threat actors using agentic AI to automate reconnaissance, exploit selection, and credential harvesting, compromising thousands of secrets.
Google Threat Intelligence Group's Q3 2026 AI Threat Tracker documents threat actors operationalizing agentic AI: in one Mandiant investigation, a financially motivated actor built and executed a credential-harvesting operation in under six hours, with an exposed 'Recon' framework managing more than 23,800 harvested secrets including cloud and AI-service API keys. A Chinese-speaking actor tracked as knaithe used a DeepSeek-powered Hermes Agent for automated reconnaissance and vulnerability enumeration, pivoting from Langflow to n8n and enabling manual exploitation of exposed Citrix NetScaler, Marimo, Apache Tomcat, and VPN infrastructure. Operators harvested Citrix session cookies from process memory to bypass MFA, obtained AWS credentials from compromised Marimo instances, and deployed the Go-based NKAbuse backdoor, with reported RCE and data exfiltration. Google notes fully autonomous end-to-end AI attack pipelines have not yet been observed in the wild.
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Threat Actors Use Claude AI Agents to Automate Cyberattacks and Steal Sensitive Data
Anthropic reports state-linked and criminal actors used Claude AI agents to automate espionage, extortion, and exploit development, stealing 300,000+ identity records and drone IP.
Anthropic's threat report details AI-driven campaigns disrupted between December 2025 and August 2026, including suspected Russian cluster GTG-20006, which targeted Ukrainian and European governments, defense firms, and drone supply chains using fully automated attack chains. The group stole over 300,000 national identity records and commercial-registry data on 500,000+ companies from a North African government technology authority, plus a proprietary drone-vision SDK. Suspected ShinyHunters affiliates decompiled 1.8 million Android APKs on 10 Amazon EC2 workers to find hardcoded secrets and abused stolen AI API keys, while the Chinese-speaking GTG-10007 cluster generated over a dozen potential zero-day findings against network appliances in one month.
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Srsly Risky Biz: Data Theft Extortion Is Booming! Hooray!
Google's Threat Intelligence Group reports data theft extortion is surging, with Silent Ransom extracting $10M and $18M from two law firms and BlackFile taking $10M.
Risky Business News, citing Google Threat Intelligence Group (GTIG), reports that cybercriminals are shifting from encrypting ransomware to data theft extortion. Law firms Goodwin Procter and WilmerHale paid Silent Ransom (Luna Moth) ransoms of $10 million and $18 million respectively; GTIG says the group often completes contact-to-extortion in a single day, now sometimes compromising systems in person posing as IT staff. BlackFile, now calling itself Redact, used high-volume vishing to steal credentials and pivot through OneDrive, SharePoint and other SaaS apps, collecting more than $10 million between February and mid-May with an average ransom of $750,000, including attempted attacks on Wall Street hedge funds and private equity firms. The piece argues governments should keep pressure on encrypting ransomware gangs while lower-impact extortion absorbs criminal energy.
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Threat actors are coming for your AI assets to operationalize their use of AI
Google GTIG reports espionage and crime groups stealing AI models, prompts, and API credentials, plus distillation campaigns and agentic AI attack automation.
Google Threat Intelligence Group's quarterly AI Threat Tracker reports adversaries stealing proprietary models, source code, prompts, and API credentials from government, healthcare, and media targets, including China-based UNC6508 compromising clouds to run unauthorized LLM workloads. Distillation campaigns against Google's models exceeded 100 million prompts launched via thousands of stolen account credentials through proxy networks. Mandiant also observed a financially motivated actor deploy an autonomous multi-agent framework that harvested thousands of third-party credentials in under 6 hours, and a 'Recon' framework on a live C2 server managing over 23,000 stolen credentials including cloud and AI API keys.
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