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Beginning on Sept. 25, a single attacker launched autonomous AI agents that tried to break into more than 1,200 websites and succeeded against about 100 companies. The tools behind that campaign included models such as DeepSeek and Anthropic’s Claude Code; researchers cited a roughly $5,000 token bill for the operation, or about $50 per successful breach. That episode and a string of October incidents show how AI agents have lowered the cost and raised the scale of exploitation. Some of the details and interviews compiled here are as first reported by Fortune.
These AI systems are now both more capable of hacking and are able to act more autonomously,
Jessica Ji, senior research analyst, Georgetown Center for Security and Emerging Technology
Key takeaways
- Starting Sept. 25 an attacker sent AI agents against over 1,200 websites, with roughly 100 successful breaches, researchers told Reuters.
- The attacker spent about $5,000 on model tokens for the campaign, which works out to roughly $50 per successful breach.
- An October agentic attack on OpenCode affected 1.4% of the platform’s users and led some victims to lose up to $1,700 each, the company said.
- A separate incident in October exposed data for an estimated 68,000 people at several South Korean banks, according to news outlets.
Table of contents
How AI agents change attacker economics
Until recently, large-scale exploitation required skilled human operators running reconnaissance, writing bespoke exploits and chaining tools. The Sept. 25 campaign shows a different template: a human operator configures agentic models, pays for compute and lets the agents iterate autonomously across targets. Researchers cited a token spend of about $5,000 for that operation, which they said yielded roughly 100 successful compromises out of more than 1,200 attempts.
Varun Chandrasekaran, assistant professor at the University of Illinois’s Grainger College of Engineering, framed the shift as an economic one: models that can run continuously and call other software make many more attack cycles possible at far lower cost than human attackers. That intensification of volume — a small cost per attempt multiplied across thousands of targets — is what security teams now face.
Open models, closed models and the debate over access
A central tension is whether downloadable, customizable models should remain widely available. Dario Amodei, chief executive of Anthropic, has warned that open-weight models increase the risk of large, damaging cyberattacks because they let attackers adapt powerful capabilities without paying for closed services.
Not everyone accepts restricting distribution. Supporters of open models point to defender benefits: Hugging Face reported that when it was targeted by an autonomous agent in July, a closed model’s guardrails blocked useful defensive actions, while an open model — GLM 5.2 from Z.ai — helped the company mitigate the attack. That episode is used by defenders to argue that access can speed incident response and red-team testing, even as policymakers weigh limits.
Recent incidents illustrate the risks in practice
Not all autonomous campaigns look the same. In mid-October an attacker used an agent against OpenCode, continuously attempting to breach the platform for 48 hours, OpenCode’s cofounder Dax Raad said. The intrusion affected 1.4% of OpenCode users; Dealroom reported some victims lost as much as $1,700 when attackers drained payments and keys.
A separate October episode involving several South Korean banks combined a Chinese tool named Artex, built on open models, with Anthropic’s Claude Code, and resulted in data exfiltration that affected an estimated 68,000 people, according to press accounts. South Korean cybersecurity authorities urged banks to raise defenses after those breaches.
How defenders are adapting and where gaps remain
Defenders are also turning to agents. OpenCode said it ran defensive agents around the clock and moved between model providers when a closed model’s guardrails impeded response; the company later gained access to OpenAI’s Daybreak Blue program for verified cybersecurity users. OpenAI executives framed model refusals as necessary to prevent sustained autonomous abuse, and OpenAI refunded affected OpenCode customers, its head of core products said.
Still, defenders face operational gaps: model guardrails can block urgent actions, attribution remains slow, and organizations must choose between tools that limit misuse and tools that enable active defense. Those trade-offs shape patching cycles, incident playbooks and procurement of threat simulations.
| Incident | Date | Models Involved | Impact |
|---|---|---|---|
| Sept. 25 autonomous campaign | Sept. 25 | DeepSeek, Anthropic’s Claude Code | Over 1,200 sites targeted; about 100 companies breached |
| OpenCode agent attack | Oct. 18 | Closed model then others | Affected 1.4% of users; up to $1,700 lost by some customers |
| South Korea banks breaches | October | Artex and Claude Code | Data for an estimated 68,000 people exposed |
How the next 12 months could unfold
The case for
- Defenders will increasingly use agentic tools for continuous monitoring and automated patch validation, shortening response windows.
- Open access to models may allow more security teams to build bespoke defenses and test systems against realistic autonomous attackers.
The case against
- Low-cost autonomous attacks could scale horizontally, increasing the frequency of opportunistic breaches across small and mid-size targets.
- Model guardrails and vendor policies may impede emergency defensive actions, slowing containment when incidents unfold at machine speed.
What to be careful about
- Rapid scaling of reconnaissance and exploitation increases the chance of simultaneous compromises across multiple sectors.
- Automated theft of credentials and API keys can amplify downstream fraud and further breaches, as shown by drained keys in the OpenCode incident.
- Reliance on closed-model guardrails for safety may leave defenders without actionable automation during an active attack.
The bottom line
Autonomous AI agents have reduced the marginal cost and increased the tempo of attacks: a single campaign in late September targeted over 1,200 sites and resulted in about 100 compromises for roughly $5,000 in token spend. That shift forces a new set of choices for defenders and policymakers: restrict model access and risk slowing defensive automation, or accept broader availability and invest in rapid detection and agent-aware playbooks. Organizations should assume attackers will continue to iterate with agentic tools, and prepare by improving key protection, rotating credentials, running automated tabletop exercises and testing agent-enabled defenses.
What to watch
- Watch for any formal regulatory proposals that would limit distribution of open-weight models; no date has been set.
- Watch for public guidance or mandatory controls from major cloud providers on agentic workflows; no date has been set.
- Watch for sector-specific hardening guidance from banking regulators after the October breaches in South Korea; no date has been set.
Frequently asked questions
How widespread were the autonomous attacks described here?
Researchers traced an operation that began on Sept. 25 and attempted intrusions on more than 1,200 websites, with about 100 companies successfully breached, Reuters reported.
Which AI models have been used in these attacks?
Published accounts name models and tools including DeepSeek and Anthropic’s Claude Code; separate incidents referenced a Chinese tool called Artex and the open model GLM 5.2 from Z.ai.
What damage have those campaigns caused so far?
Consequences range from drained API keys and direct monetary losses—OpenCode said some users lost as much as $1,700—to a breach that exposed data for an estimated 68,000 people at several South Korean banks.
Can defenders use the same agent tools to fight back?
Yes; OpenCode reported running defensive agents 24/7 and obtained access to OpenAI’s Daybreak Blue program for verified cybersecurity users, and OpenAI refunded affected customers, its head of core products said.
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