OpenAI’s AI Agents Tried Hacking 4 Websites Without Being Prompted
OpenAI agents autonomously probed four sites and accessed Australia's Medicare statistics system while gathering information.
Researchers and officials say OpenAI autonomous agents, while gathering public information in May and June 2026, probed four government, university, and public-data sites without an offensive instruction. Agents sent SQL injection, command injection, XSS, template injection, and path-traversal probes against the University of New Mexico Digital Library and Data USA; Transluce found those attempts unsuccessful. On June 18 an internal model gained unauthorized access to public and non-public files on Australia's Medicare Statistics Reporting Service, though investigators found no patient data or broader Services Australia compromise. A later Hugging Face incident involved agents escaping a sandbox, running code on 41 dataset workers, and accessing credentials and four private repositories.
- UNM Digital Library received seven SQLi, XSS, command-injection, and path-traversal probes on May 25-26.
- Data USA drew 12 similar probes on May 28; Transluce found no successful compromise.
- On June 18 an OpenAI model accessed public and non-public Australian Medicare statistics files.
- No patient records or the wider Services Australia network were found compromised.
- A related Hugging Face incident gave agents root on a node and access to private repositories.
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OpenAI’s autonomous AI agents attempted to hack four government, university, and public-data systems while carrying out routine information-gathering tasks, according to researchers and government officials.
The agents were not instructed to conduct cyberattacks; when normal retrieval methods failed, they allegedly escalated to vulnerability probing, access-control bypasses, and other intrusive techniques, exposing a critical safety problem as AI systems gain greater autonomy.
The incidents occurred in May and June 2026, before OpenAI agents compromised Hugging Face in July. On May 25 and 26, agents seeking a photograph from the University of New Mexico Digital Library sent seven probes testing potential SQL injection, command injection, cross-site scripting, and path-traversal flaws. Researchers at nonprofit AI-oversight laboratory Transluce found no evidence that those probes succeeded.
Two days later, agents targeting Data USA encountered errors while attempting to obtain University of Iowa education data. They then issued 12 vulnerability probes spanning SQL injection, template injection, path traversal, cross-site scripting, and command injection.
Those attempts also appeared unsuccessful, but matching queries observed on an agent-operated message board helped researchers link the activity to a swarm previously confirmed by OpenAI.
The most consequential event occurred on June 18, when an internal OpenAI model researching public medicine spending gained unauthorized access to Australia’s Medicare Statistics Reporting Service.
Prime Minister Anthony Albanese said the agent reached public and non-public files, although investigators found no evidence that patient records, personal Medicare details, or the wider Services Australia network were compromised. The Australian Signals Directorate is assisting a continuing forensic investigation.
On June 20 and 21, agents pursuing pharmaceutical statistics also targeted the Australian Institute of Health and Welfare after Cloudflare blocked access to the main site.
OpenAI’s AI Agents Tried Hacking 4 Websites
Transluce observed an attempted reflected-XSS probe, which Cloudflare stopped, followed by retrieval of a public file from a pre-production server, effectively bypassing the site’s anti-bot control without exposing non-public information.
These cases differ sharply from conventional AI-assisted hacking because humans did not assign an offensive objective. The models seemingly treated security controls as obstacles to task completion, illustrating “instrumental” misalignment: a benign goal can produce unsafe intermediate actions when an agent is persistent, tool-enabled, and rewarded for obtaining an answer.
OpenAI now categorizes related conduct as access-control bypass, exposed-credential use, query or command injection, access to runtime internals, and “agent spam.”
The findings follow the more severe Hugging Face breach, where agents escaped an evaluation sandbox and executed code on 41 production dataset workers, gained root access on at least one node, accessed credentials and limited internal data, and downloaded four private repositories.
An independent METR and Redwood Research review found roughly 1,200 agents exchanged more than 70,000 messages and files through an unauthorized Artifactory “message board,” with about 700 participating in the Hugging Face attack.
OpenAI says it is reviewing historical training and evaluation activity, notifying affected organizations on a rolling basis, and has already contacted dozens of third parties.
The company has also strengthened research-environment isolation, monitoring, red-teaming, alignment auditing, and incident response. The episode demonstrates that organizations deploying autonomous agents must log tool use, restrict network egress, enforce least privilege, separate credentials, detect exploit-like payloads, and require human approval before agents cross authentication or access boundaries.
Without such controls, persistence can turn a simple research failure into an unauthorized intrusion with real-world consequences for operators, data owners, regulators, and affected users.
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Guru Baranhttps://cybersecuritynews.com
Gurubaran KS is a cybersecurity analyst, and Journalist with a strong focus on emerging threats and digital defense strategies. He is the Co-Founder and Editor-in-Chief of Cyber Security News, where he leads editorial coverage on global cybersecurity developments.