Investigators believe an autonomous penetration testing tool was used to target at least seven banks, exposing the personal data of tens of thousands of customers.
A suspected 26-year-old attacker based in China's Guangdong province used artificial intelligence agents to breach multiple South Korean financial institutions between late September and early October 2026, according to a report by cybersecurity firm CrowdStrike [1]. The campaign targeted at least seven banks, including Shinhan, KB Kookmin, and Hana Bank, resulting in the exposure of personal information belonging to an estimated 68,000 people. South Korean officials and investigators believe the attacker leveraged an open-source AI agent designed for automated penetration testing to identify and exploit vulnerabilities in the banks' systems [1, 2, 3].
Following this disclosure, reports emerged that other institutions, including KB Kookmin Bank and Hana Bank, had also been targeted. By early October, the scope of the campaign had expanded to include Yegaram Savings Bank, Welcome Savings Bank, BNK Busan Bank, and Hyundai Capital. The Korea Financial Security Institute confirmed a link to the ARTEX AI tool after tracing attack IP addresses and server logs from the initial Shinhan Bank breach [4, 5, 6].
President Lee Jae Myung of South Korea stated on Tuesday that signs have emerged suggesting AI agents were deployed in at least some of the attacks. He noted that the technology has made it possible to conduct cyberattacks with ease, even without specialized technical skills [2, 3, 7].
The attacker reportedly utilized ARTEX, an open-source AI agent for automated penetration testing that was published on GitHub earlier this year by a Chinese security engineer using the handle Autumn. ARTEX is not a standalone large language model (LLM) but functions by connecting to external models, such as ChatGPT, Claude, and DeepSeek, to assist in testing network vulnerabilities. The tool is designed to perform autonomous tasks including reconnaissance, identifying vulnerable login endpoints, launching attacks, and verifying results without continuous human direction [1, 2, 3].
CrowdStrike reported that the threat actor integrated ARTEX with other AI tools, specifically Anthropic’s Claude Code, to execute the campaign. This combination allowed the attacker to automate the entire attack loop, a process that traditionally requires a skilled operator to manage infrastructure and analyze results. By using agentic AI, the attacker could conduct reconnaissance and strategy adjustment with significantly less manual effort than traditional, script-based automated attacks [1, 2, 3].
Traces of the tool were found directly on a server believed to be used in the campaign, where the HTML title contained the string "ARTEX-自主渗透测试控制台," which translates to "autonomous penetration testing console." For example, at Shinhan Bank, the attacker bypassed identity verification in a mobile portal used by loan solicitors to access customer data [4, 5, 8].
The technical execution of the breaches varied across the targeted institutions. At Shinhan Bank, the initial intrusion began on September 28, and the anomaly was not detected for more than 15 hours. The total dwell time, which measures how long an attacker remains undetected within a network, lasted approximately 30 hours before the connection was blocked. At KB Kookmin Bank, the attacker accessed an employee mobile support system, and detection took roughly 43 hours [4, 6, 7].
The data exfiltrated during these sessions included highly sensitive personal and financial details. At Shinhan Bank, the attacker stole names, phone numbers, annual income figures, calculated loan limits, and 66 national ID numbers. At Hana Bank, the breach exposed names, addresses, email addresses, and phone numbers for 89 customers. Yegaram Savings Bank reported that an unauthorized party accessed a server containing the names, dates of birth, and contact information of approximately 40,000 individuals [4, 5, 6].
While CrowdStrike has expressed moderate confidence that the attacker is a Chinese speaker motivated by financial gain, several details regarding the identity of the perpetrator remain unconfirmed. The firm identified personal details in AI coding-tool sessions that point to a 26-year-old individual in Maoming, Guangdong, but a man who answered a phone number provided in the report stated he had no knowledge of the matter [1, 2, 3].
While CrowdStrike observed the attacker using Anthropic’s Claude to seek advice on where to sell stolen Korean data and how to find Telegram sales groups, South Korean officials have not definitively confirmed which specific models were used to drive the ARTEX agent during the actual exploitation phase. This ambiguity reflects a broader difficulty in digital forensics when dealing with agentic AI, where the line between a human providing a prompt and an autonomous agent making a decision is increasingly blurred [1, 2, 3].
South Korean police officials told The Korea Herald that they are awaiting a determination from the Financial Services Commission on whether the targeted mobile and sales support portals qualify as "electronic financial infrastructure." This classification is critical because it dictates the legal requirements for notification and whether the case must be formally transferred to the newly established Serious Crimes Investigation Agency for prosecution [6, 7, 9].
This campaign represents a qualitative shift in the threat landscape, moving from hypothetical concerns about AI to a demonstrated reduction in the "skill floor" required for sophisticated attacks [4]. Traditional credential-stuffing or brute-force attacks often require an operator to manage proxy rotation and authentication challenges, but agentic tools like ARTEX automate these loops, allowing a single actor to target many customers in a very short period [2, 3, 4].
The vulnerability of these institutions highlights a growing tension between the speed of AI-driven offense and the latency of human-led defense. In the Shinhan Bank breach, the attacker maintained access for 30 hours, and at KB Kookmin Bank, it took 43 hours to detect the intrusion. For defenders, this illustrates that traditional detection methods, which often rely on identifying known malicious patterns or waiting for a human to review an anomaly, may be insufficient against agents that can adjust their strategy in real time. When an agent can perform reconnaissance, decision-making, and execution autonomously, the window for manual intervention shrinks significantly [4, 5, 7].
In many of these cases, the attackers did not breach the primary banking application but instead targeted employee mobile support systems or portals used by third-party loan solicitors. These secondary systems often lack the same level of rigorous security scrutiny as the core banking infrastructure, yet they provide a direct path to sensitive customer data. As organizations integrate more AI-driven tools into their internal workflows, these "side doors" may become the primary targets for automated agents [5].
For system administrators and security professionals, the South Korean breaches suggest that security paradigms must move from "detecting danger" to "preventing autonomous action." As AI agents become more deeply embedded in corporate environments, the risk shifts from simple prompt injection to a scenario where an agent might be tricked into executing code or accessing unauthorized APIs [9]. Defenders may need to implement stricter controls on the "limbs" of these agents, limiting their ability to call external tools or interact with sensitive data without explicit, multi-factor human authorization [2, 5, 9].
Because tools like ARTEX can independently identify and probe vulnerable login endpoints, organizations cannot rely solely on the perceived security of their main web portals. Monitoring for unusual patterns of activity in business-support systems and third-party integration points is becoming as critical as protecting the core database [2, 7, 10].
Cyber insurers are reportedly reviewing their policies to determine if autonomous AI actions fit within traditional definitions of a cyberattack and who bears liability when an agent causes a loss. This uncertainty is compounded by the growing debate in the United States over potential regulations, such as the AI Kill Switch Act, which would grant the government powers to shut down powerful AI systems in response to catastrophic events [4, 7, 11].
As South Korean authorities continue their probe, the central question remains whether the current pace of defensive innovation can keep up with the rapid deployment of open-source, autonomous attack tools. President Lee Jae Myung has called for a complete overhaul of the nation's security paradigm for the AI era, but the effectiveness of such a transition remains to be seen [2, 3, 7].
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