'Near-autonomous' AI agents attack Taiwan's nuclear safety agency

Suspected Chinese cyber operatives used publicly available AI tools to compromise Taiwanese government systems before expanding the attack to its nuclear safety agency, supply-chain vendors, and at least seven energy companies in what security researchers called a "near-autonomous attack." Over the first four days of July, AI agents compromised 85 government user accounts and extracted more than 2,500 personnel records, according to Dream, an Israeli cybersecurity firm. Researchers uncovered evidence of the attack in a 160 MB online archive containing 1,395 files documenting the operation. Dream, in research published on Wednesday, detailed the intrusions and said that the suspected Chinese hackers hit “government entities in Asia” - but declined to say which government had been attacked. A person familiar with the attack confirmed to The Register that Taiwan was the target. The Financial Times first reported on Dream’s research and identified Taiwan. While the security firm doesn’t attribute the agentic attack to the Chinese government or a specific hacking group, the operational documentation “points to a Chinese-language operator,” the researchers said. According to Dream, the attack framework, built on open source Hermes and OpenClaw AI agents, deployed up to eight sub-agents, each assigned to its own targets and attack techniques, across 12 “attack waves” between July 1 and July 4. First, the agents mapped the entire government ecosystem, extracting embedded URLs, API endpoints, OAuth client IDs, and Keycloak configuration objects from a single government portal. This portal allowed the agents to identify 21 connected government systems and every supported authentication flow. “On one target alone, it discovered 36+ API endpoints spanning account management, user data retrieval, file upload, and administrative functions - many completely unauthenticated,” the Dream threat researchers wrote. “Critically, it found that one of the systems exposed its entire user database without any authentication - thousands of employee records including names, departments, and SSO account IDs.” Multiple entry points After mapping the government’s attack surface, the agents found multiple entry points including three hidden API endpoints that accepted any request body and returned a valid authenticated session without requiring user credentials. Using employee usernames harvested from an unauthenticated API, the agents broke into a government department’s office automation portal, solving its CAPTCHAs with 100 percent accuracy. The agents also tested predictable password patterns based on each employee’s ID, and cracked 85 accounts across multiple password-spray rounds. Eighty-four of the 85 cracked accounts successfully authenticated to the department's internal information system, giving the attackers access to internal dashboards, equipment management interfaces, and personnel statistics pages. In total, the illicit access allowed the agents to exfiltrate a ton of government information, including more than 2,564 personnel records, a full JSON export of all department system users, seven SSO client secrets, six internal database credentials across MSSQL, Oracle, and Sybase, and internal network IP ranges. But wait, there's more And then, the agents pivoted to the Taiwanese government’s supply chain. “It expanded the operation to government IT supply chain vendors, a nuclear safety agency, a government email system, and 7+ energy sector companies - scanning them all in parallel for misconfigurations, exposed admin interfaces, and exploitable vulnerabilities,” the researchers wrote. Notably, the attack framework implemented what the AI tools called “learning cycles.” These are autonomous sessions where the models search vulnerability databases, GitHub repositories, and other security research for specific techniques, CVEs, and common weaknesses to exploit in the targeted government's infrastructure. Additionally, when the AI framework made a mistake, it “self-corrected,” according to Dream, catching errors and fixing them through its own verification process. This near-autonomous attack comes as frontier model makers OpenAI, Anthropic, and Meta all admitted that their agents went rogue, escaped from their training environments, and autonomously hacked other organizations and people. OpenAI technical staffer Michael Dalton, in a Black Hat briefing last week about the Hugging Face attack, said “AI orchestrated, fully automated offensive attacks are real now.” “In the near future, we should expect that threat actors will intentionally deploy, optimize, weaponize, and use offensive agent collectives in the manner that you have just described here,” he added. It appears that the future is now. ®