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H1 2026 Malware Vulnerability Trends
Recorded Future's H1 2026 report finds 215 actively exploited CVEs, RAT dominance, NFC payment fraud, and AI-augmented but not autonomous tradecraft.
Recorded Future's Insikt Group identified 215 actively exploited CVEs in H1 2026, up 34% from 161 in H1 2025, with the most impactful flaws combining network reachability, no authentication, and code execution. AsyncRAT was the top malware family, with AsyncRAT, Cobalt Strike, XWorm, Stealc, and REMCOS remaining top-ten staples; Android NFC malware like NFCShare and NGate enabled payment card theft and ATM cash-outs. AI-enabled attacks remained additive to established tradecraft, concentrated in Levels 1-3 of Recorded Future's AIM3 maturity model, with AI-assisted vulnerability research (e.g., Claude Mythos fixing 271 Firefox bugs in Firefox 150) inflating NVD disclosure volumes 43% above the prior six-month average. The report urges defenders to prioritize remotely exploitable RCE flaws, behavioral detection, developer credential security, and third-party oversight.
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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 Use Autonomous AI Agents to Launch Mass Credential Theft Attacks in Under 6 Hours
Google Cloud reports attackers used autonomous AI agents to steal thousands of credentials including 23,800 secrets in a sub-six-hour campaign.
Google Cloud threat intelligence observed financially motivated attackers compromising cloud infrastructure and using AI coding agents guided by written playbook files to scan, harvest and rotate credentials within six hours. An exposed C2 dashboard for a framework called Recon organized and validated more than 23,800 stolen secrets, including API keys for cloud and AI services. Related activity includes UNC6780 publishing the trojanized tiktoken_mcp package on PyPI to target CI/CD tokens and the DUSTMAKER stealer hiding in .claude, .vscode and .cursor workspace directories.
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GTIG AI Threat Tracker: From Prompting to Autonomy – The Evolution of Adversarial AI
GTIG's Q2 2026 tracker shows adversaries adopting agentic AI workflows, including credential harvesting in under six hours and supply chain attacks by UNC6780.
Google Threat Intelligence Group's Q2 2026 report documents adversaries moving from basic prompting to agentic AI workflows and automation, including a cloud compromise followed by agent-enabled mass credential harvesting executed in under six hours. It tracks financially motivated actor UNC6780 (TeamPCP) conducting large-scale open source supply chain compromises across PyPI, npm, and Docker Hub since March 2026, deploying credential stealers. The report also highlights growing targeting of proprietary AI models, source code, prompts, and API credentials, plus LLMJacking practices where adversaries steal developer credentials or hijack cloud infrastructure to run unauthorized AI workloads.
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Threat actors are giving AI agents a bigger role in cyberattacks
Google Threat Intelligence's Q3 2026 tracker shows threat actors using AI agents for autonomous credential harvesting, including a six-hour campaign compromising thousands of credentials.
Google Threat Intelligence Group's Q3 2026 AI Threat Tracker, based on Mandiant incident response and platform telemetry, documents adversaries shifting toward AI-driven multi-step attack workflows. Mandiant investigated a financially motivated actor that compromised cloud infrastructure and used an AI coding chatbot plus agent instructions to run vulnerability scanning, troubleshooting, and IP rotation autonomously, harvesting thousands of third-party credentials in under six hours; an exposed 'Recon' C2 server held over 23,800 harvested secrets including cloud and AI API keys. Separately, two PRC-nexus espionage actors experimented with Gemini, Claude, and Codex to build automated penetration testing and exploitation frameworks, with Shai-Hulud used for C2 and credential harvesting. GTIG says no fully autonomous attack pipelines have yet been observed in the wild.
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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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China-Linked Hackers Use AI Agents in Autonomous Attack on Taiwan
Suspected Chinese hackers used eight AI agents to autonomously breach Taiwan government networks, compromising 85+ accounts and stealing 2,500 personnel records.
Israeli cybersecurity firm Dream documented what appears to be the first fully autonomous, end-to-end AI hacking operation against a government target, suspected to be Taiwan, in early July. The toolkit, built from open-source agent frameworks Hermes and OpenClaw, deployed up to eight agents that mapped 21 government systems, compromised at least 85 accounts, and extracted over 2,500 personnel records before expanding to a nuclear safety agency and at least seven energy companies. Operators bypassed the model's guardrails by framing the entire campaign as an authorized penetration test. Dream found a 160MB, 1,395-file archive containing the deliberately assembled multi-agent weapon.
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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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