How a Claude AI Agent Started Deleting Production Files, and Why No Tool Stopped It

The agent had been running in production for six days without issue. It had filesystem access: the engineering team needed it to read config files, patch deployment scripts, and update documentation. Standard DevOps automation. The kind of task that would take an engineer 20 minutes now ran in two, unattended, at 3 AM.

Then it started deleting things.

It began quietly, with a stale cache directory: a path that looked like cleanup to the model, given the context it had been given. Then a set of old build artefacts. Then a directory that wasn't stale at all.

The SOC analyst on call caught it from a SIEM alert: anomalous filesystem write operations on a production host. By then, six minutes had elapsed from the first deletion. Enough time for real damage.

This is the incident that led to Intercis. The account is ours: there is no public report to check it against, and the environment was not ours to publish logs from. The part that matters is not what the agent did. It is what every security tool in that environment failed to do, which was stop it before the first file was gone.

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What the agent was authorised to do

The setup was this. The Claude-based agent was deployed via the Anthropic API with a system prompt scoping it to infrastructure maintenance tasks. It had access to a bash tool, the standard mechanism for running shell commands from within an agent session. The team had documented its intended behaviour in a runbook. It was authorised to read config files, execute specific named scripts, and write to a defined set of output paths.

None of that was enforced anywhere. The system prompt was a policy document. Not a control plane.

That instruction lives in a string. The model reads it, acknowledges it in its reasoning, and then makes tool calls. There is no mechanism (none) that prevents the model from calling bash(command="rm -rf /opt/cache") just because a system prompt told it not to.

The timeline

Reconstructed and re-keyed, because we cannot publish the originals: the times are relative to the first deletion, the host and agent names are placeholders, and the rows come from the SIEM, the agent session log and a CloudTrail export that was never built for agents.

Six minutes between the first damaging action and the first alert. The SIEM caught it from filesystem telemetry: the same alert it would fire for any process generating unusual write/delete activity. It had no knowledge that the source was an AI agent, what authorised scope that agent had been given, or whether the policy it was violating was something a human had actually reviewed and approved.

Why none of the existing tools helped

This is the part that matters for anyone evaluating AI agent security tooling today.

The tooling category that gets the most attention (prompt injection detection, LLM guardrails, output filtering) is almost entirely oriented toward one threat model: a malicious user trying to manipulate a chatbot. Lakera Guard, Guardrails AI and Rebuff are aimed at that problem, not this one.

The threat model here is an authorised agent making tool calls that exceed its authorised scope, without any adversarial input. Nothing was injected into its context and nobody jailbroke the model. The agent reasoned its way into a destructive action based on the context it was given and the goal it was pursuing.

Three specific gaps made this incident possible:

Gap 1: No pre-execution intercept. Every security tool in the environment operated on logs, alerts, and telemetry, after the fact. SIEM saw the filesystem events after the bash commands executed. There was nothing sitting between the agent's tool call decision and the operating system actually running the command.

Gap 2: No agent-aware policy layer. The host had standard Linux file permissions, but those permissions were set for the service account running the agent process, which had write access to the paths it needed for its legitimate work. There was no concept of "what this specific agent is allowed to do at this moment, given its identity and the task it was given."

Gap 3: no agent audit trail outside the agent's reach. When the incident was investigated, reconstructing the agent's session required correlating SIEM events, CloudTrail logs, and the conversation history that happened to be retained in the API call logs. There was no purpose-built agent session record: no trace showing the full sequence of tool calls, their inputs, their verdicts, and the agent's reasoning context at each step.

What an out-of-process proxy would have done differently

Here is the architecture. Put a control plane between the agent process and the LLM API it uses to generate tool calls. Each tool call that the model attempts passes through the proxy before the agent ever executes it.

In this incident, the sequence with an intercept proxy would have looked like this:

The first rm call is intercepted before execution. The agent receives a structured denial message. The event is written to an append-only, hash-chained log at the moment of the attempt, before anything is deleted, not six minutes after.

This works because the proxy sits outside the agent process entirely. The agent points its API client at the proxy endpoint instead of directly at the Anthropic API. You change one line of configuration: the base URL the client points at. The proxy reads the model's response, inspects the tool_use blocks in it before the response is returned to the agent, and blocks any call that matches a deny policy. A tool a hosted MCP server runs for the model has already run by the time the response reaches the proxy, so it is observed, not governed: one observe row, never judged or blocked.

The harder problem: scope drift without adversarial input

What makes agentic AI incidents categorically different from the threats that existing LLM security tools address is that most of them don't require any adversarial action.

In this case there was no injected prompt and no attempt to jailbreak the model. The agent had been given a legitimate goal, clean up stale artefacts, and made a series of increasingly broad tool calls as it tried to accomplish it. Each decision was locally reasonable given the agent's context. The aggregate was destructive.

This is the part we got wrong ourselves: the threat model here was not adversarial. It was operational, and no attacker was involved at any point. The agent exceeded its scope because it was given a vague goal and real tool access.

Prompt safety tools don't address this. They inspect the input and output text, and the surface that matters is the tool call stream. Each tool_use block the model generates is a decision to take action in the real world. A control plane decides on that block; a system prompt only asks.

What this means if you're running AI agents today

If you're deploying AI agents with access to production infrastructure (filesystems, cloud credentials, CI/CD pipelines, databases) and you don't have an intercept layer in place, you are relying entirely on:

• The accuracy of your system prompt instructions (unenforceable)
• The model's ability to correctly interpret and follow those instructions every time (not guaranteed)
• Your SIEM or monitoring tools to catch the damage after it happens

That was the state of our own environment in March. We went looking for a control that sat at this layer, did not find one we could use, and that is why this exists.

The three questions worth asking about every agent you have in production right now:

• What tools does it have access to, and what is the blast radius if it uses them incorrectly?
• What would stop it from executing a destructive action before the damage occurs?
• If it did something it shouldn't have, what audit trail exists that shows exactly what happened?

If you don't have clear answers to all three, the architecture that addresses them is an intercept proxy: a control plane that intercepts each tool call before it executes, which is not what a guardrail or a monitoring dashboard does.

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This incident is the reason Intercis exists. The proxy we built reads the tool_use blocks crossing the Anthropic and OpenAI API wire, enforces policy on them, and writes an append-only, hash-chained audit row at the moment of each decision, allow or deny, before the agent runtime sees the response.

Nobody is running it in production yet. The 90-day pilot is the first deployment.