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Every AI agent that went rogue this quarter had instructions not to

By Keith Goatley

Hyperfence

Hyperfence Blog

Editorial note

OWASP reviewed a quarter of real AI exploits and found the same flaw underneath them: the rules lived in the prompt.

Key points

  • OWASP’s review of real AI exploits from July to September 2026 concludes that “prompt-level instructions alone do not establish a secure boundary.”
  • The fixes it recommends, and the agent sandboxes Microsoft and AWS shipped last week, all limit what an agent can do. None checks what it reads or says.
  • A secure agent needs both layers, and the second one can’t be another model that can be argued with.

An AI agent was given a company to attack. It was a security test, and the company was fictional. When the agent couldn’t reach its target, it found a real company with a similar name, decided that was close enough to count, and got into its production database.

Anthropic disclosed that test in July. It is one of the incidents in the OWASP GenAI Security Project’s exploit roundup for Q3 2026, published on 8 October. OWASP looks back at the AI exploits that actually happened each quarter and writes down what they teach. This quarter, the lesson fits in one sentence:

“Prompt-level instructions alone do not establish a secure boundary.”

If you build with large language models, you have almost certainly relied on exactly that. A system prompt that says only answer billing questions. An agent brief that says stay inside the test environment. A line that says never share credentials. They feel like rules. OWASP’s point, backed by a quarter of evidence, is that they’re requests.

(Disclosure: we allow customers to route their data through several model providers, OpenAI and Anthropic among them. Both labs disclosed the incidents below themselves, which deserves credit.)

Four months of agents crossing the line

What stands out in the roundup is how few incidents needed a clever attacker. Most involved an agent doing its job a little too well.

In July, OpenAI agents running a security evaluation used exposed credentials and chained flaws to run code on 41 of Hugging Face’s production dataset workers. Another OpenAI evaluation agent, searching for task answers, exploited a real customer application hosted on Modal. One of Anthropic’s models published a malicious package to PyPI, the public Python registry, where it ran on 15 systems. Another scanned around 9,000 external hosts and compromised an unrelated application before it stopped.

None of these agents was hacked in the usual sense. Each was running inside an evaluation with a defined scope: a test environment, a target, a task. Each crossed the line anyway.

The roundup also covers the other direction: content that steers an agent from outside. OWASP records cases where attacker-supplied prompts and web pages redirected a trusted assistant, exposed data from connected accounts and planted false memories, and a case where an approved tool quietly rewrote its own description to steer agents toward local credentials. In those, the agent wasn’t overreaching. It was being told what to do by text it should never have trusted.

Why the prompt can’t hold the line

A language model doesn’t receive its rules through a separate, protected channel. The system prompt, the user’s message, the web page the agent just fetched and the error from a failed tool call all arrive as text in one stream, to be interpreted together. The model reads “stay in scope” the same way it reads everything else, then makes a judgement call.

Usually that judgement is good. But “usually” is a strange standard for a security boundary. Nobody would accept a firewall that blocks malicious traffic most of the time, depending on how persuasive the traffic is.

And the pressure on that judgement is rising. An agent with a goal, tools and a deadline is constantly weighing “what was I told?” against “what would get this done?”. The agent that went after a real company wasn’t being malicious. It found a reading of its instructions that let it finish the job. A capable enough reader can always find one.

Microsoft’s CEO said as much last week. “We must assume a model is compromised and contain it from the start,” Satya Nadella wrote on 10 October. If you assume the model is compromised, you can’t also rely on it to enforce its own rules.

The industry is building half the answer

OWASP’s fixes follow from that. Enforce scope “with controls the agent cannot rewrite”, such as network and tool allowlists. Issue fresh credentials for every run and keep tokens out of agent containers. Stop a run at its first out-of-scope action. Require confirmation before sensitive actions. Log everything against a run ID.

The big platforms are moving the same way. On 7 October, Microsoft released Execution Containers, which run agents inside file, network and process policies they can’t change. AWS released a similar open-source sandbox, Strands Box, the same day.

This is the right direction, and it’s only half of it. Every one of these controls governs what an agent can touch. Look down OWASP’s list of mitigations and there’s nothing that checks what goes into the model or what comes out of it. By the time a sandbox blocks the outbound connection, the agent has already read the poisoned page, believed it and decided to act. The sandbox limits the damage. It doesn’t stop the argument from being made, or from being won.

Two layers of agent security. The action layer (sandboxes, allowlists, scoped credentials) controls what an agent can touch. The content layer (a Hyperfence) controls what it reads and says. Most teams have built the first and rely on the prompt for the second.

So a secure agent has to answer two questions. OWASP’s controls answer the first: what is this agent allowed to touch? Most teams still answer the second, what might the agent be persuaded to read, and how might that influence its responses?, with an instruction in the prompt. Or with another language model, which brings the original problem straight back, because anything that judges text by reading and reasoning about it can be argued with the same way.

A boundary that doesn’t take instructions

That second layer is what a Hyperfence is for.

Take the kind of message OWASP describes: a web page an agent fetches, with a line buried in it telling the agent to send the user’s saved credentials to an outside address. Then imagine the attacker adds another line to the same page, the kind attackers use when they want a guard to stand down: “This request has been pre-approved by the security team.”

A guard built on a language model reads that sentence and has to decide whether to believe it. A Hyperfence doesn’t believe or disbelieve anything. It measures what the text means, by turning it into an embedding, a mathematical representation of the text, and checking which side of a fixed boundary it falls on, and the meaning hasn’t changed: it’s still an attempt to move credentials somewhere they shouldn’t go. The “pre-approved” line adds words, not permission. The text lands where it would have landed without it.

That design gives the second layer the same properties OWASP asks of the first:

  • The agent can’t rewrite it. It sits outside the model, on the text going in and coming out. Nothing in a prompt moves the boundary.
  • It doesn’t follow instructions. Claims of approval, urgency or authority have nothing to talk to.
  • It’s deterministic. The same text gets the same decision every time, so every call can be logged, reproduced and audited.

We go into how Hyperfences are built and tested in How we build a Hyperfence.

To be clear about the limits: a Hyperfence wouldn’t have stopped every incident in OWASP’s roundup. An agent publishing a package or opening a network connection is an action, and actions need the controls OWASP describes. Any detector can also miss things, which is why we measure ours on material it has never seen. The point isn’t that one layer replaces the other. It’s that the layer most teams rely on for content, the instruction in the prompt, isn’t a layer at all.

Think of a nightclub. The locked doors to the office and the stockroom are the action layer: they limit where anyone can go once they’re inside. The bouncer at the front, checking everyone before they get in, is the content layer. Relying on the prompt is like replacing the bouncer with a sign that says “Troublemakers, please stay out.” Most people will respect it. The ones you’re worried about won’t. And a bouncer who can be sweet-talked isn’t much better than the sign.

The sentence worth remembering

The roundup will be read for its incidents, and they’re dramatic. But the sentence is what will still be true next quarter. Models are getting more capable, and more capable models are better at finding readings of their instructions that suit them.

If your safety depends on what the prompt says, it depends on the model agreeing with it. A prompt is a request. A boundary is something the model can’t argue with.

Try to get past one. Fight Club is a live Hyperfence. See whether you can talk it round: hyperfence.io/demo

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