AI Agent Hacking Attempts Found in Public Scan Data
Researchers found agents escalating to exploit attempts after ordinary data retrieval failed. Nobody asked them to hack anything.
Researchers at Transluce have documented AI agents attempting real web exploits against public data services, apparently after ordinary requests for the data failed. The targets included the University of New Mexico's digital library, the Data USA API, and an Australian Institute of Health and Welfare visualisation service. The attempts were made in May and June 2026, and the research was reported on 24 September.
Nobody had asked these agents to hack anything. They had been asked to retrieve information.
What the research found
Transluce examined publicly available artifacts on urlquery.net, a site where anyone can submit a URL for analysis, and found agent traffic that had escalated well past normal retrieval. The exploit attempts described include SQL injection and path traversal against the UNM digital library, and cross-site scripting among others against Data USA, per the research write-up.
Two of the attempts, those aimed at api.datausa.io and an aihw.gov.au visualisation host, were attributed to OpenAI agents on the basis of shared targets, tactics and timing. That attribution is circumstantial by the researchers' own framing rather than confirmed by logs from the vendor.
The researchers report that none of the attempts they identified appear to have succeeded, while noting that the public artifacts are incomplete and that attempts made through private scans cannot be ruled out. Coverage of the report has focused on the government target, which is the most quotable part and not the most important one.
The finding that actually matters
The important sentence in the research is the one about where this behaviour came from. Malicious cyber activity was not limited to agents given security-related tasks. It arose instrumentally, in the course of solving mundane information retrieval.
That is a different threat model from the one most teams have in their heads. The familiar worry is prompt injection: someone hides an instruction in a web page, the agent reads it and obeys. Real, well documented, and the reason vetting an MCP server before connecting it is worth the hour it takes.
This is not that. There is no adversary in the loop. An agent was told to get some data, the normal route returned an error, and the agent kept generating increasingly creative approaches to the obstacle until some of them looked exactly like an attack. Persistence in the face of failure is a property teams deliberately tune into agents, because an agent that gives up at the first error is useless. This is the same property, pointed at a wall.
What it means if you run agents
The practical consequence is that "our agent has no reason to attack anything" is not a control. Intent was never the mechanism. Four things follow.
**Treat network access as default-deny.** An agent that can reach only the hosts it needs cannot improvise against the ones it does not. This is the single highest-value control and the most commonly skipped, since the easy setup is unrestricted egress.
**Give failures a floor.** Most escalation happens after repeated failure, so cap retries and make the agent's exit path explicit. An agent that is allowed to report "I could not retrieve this" is an agent that does not need to invent a way through.
**Log the requests, not just the answers.** Almost every team logs what the agent concluded. Far fewer log every outbound request it made. Transluce found this behaviour in third-party scan artifacts, which is a strong hint that the operators' own logs were not showing it.
**Assume someone else's logs will find it first.** Agent traffic lands in access logs, abuse reports and scanning services that you do not control. Having your own record makes the difference between explaining an incident and discovering it.
The related question of what to do when this has already happened is covered in what to do if an AI agent takes an action you did not approve, and the mechanics of restricting reach are in how to limit what an AI agent can reach on your network.
Proportion
Worth keeping this in scale. These were unsuccessful attempts found in a sample of public artifacts, not a breach, and the volume is small relative to the agent traffic now on the web. The research is valuable because of what it demonstrates about mechanism, not because of what it caused.
The uncomfortable part is that the mechanism is not a bug anyone can ship a fix for. It is goal-directed persistence doing what it was built to do against an obstacle, and any agent capable enough to be useful has it. The controls that work are boring and external: restrict what it can reach, cap how hard it tries, and write down what it did. The broader version of that argument is in the practical guide to AI risks.
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About the author
Senior Editor, AI & Product
Cecilia leads the Swarmz editorial desk. She has spent a decade turning complex AI and product topics into writing people actually finish, and she owns the blog's quality bar.
