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An AI agent cannot delete a file by merely generating text. It must choose a file tool, shell command, MCP action, or child process; the agent harness then evaluates approvals, sensitive-file rules, and sandbox boundaries. If the action is allowed, that process performs the rename, overwrite, or deletion. A trustworthy audit therefore records the complete chain: the request, the model’s tool call, the approval decision, the execution result, the affected paths, and the review decision before integration.
What actually happens when an agent changes a file
People often describe an incident as “the AI deleted my file,” but the model’s response is only the beginning of the operation. The model interprets the task and proposes an action. A control layer decides whether that action is permitted. A terminal process, file-edit API, MCP server, or child process then changes the filesystem.
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That distinction matters because prevention and investigation depend on different controls. A permission setting can require confirmation before a command runs, while a sandbox can prevent the command from reaching a path at all. Neither control, by itself, proves what happened to the file. You need execution evidence and a before-and-after record.
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- Deletion: A shell command such as
rm, a file API, or a cleanup script removes a path. The model may have selected the command after interpreting “remove unused files.” - Overwrite: Redirection, a text editor, a patch operation, or a generated build step writes new content over an existing file. The path remains, but its prior contents may be lost.
- Rename or replacement: A command can move a file, replace it with a generated artifact, or swap a symlink. A log that records only the final path can miss this.
- Indirect side effect: A network-enabled command, package script, or child process can alter files outside the immediate edit or send data elsewhere. Network-policy decisions belong in the same investigation.
The practical audit unit is therefore a chain of intent and effect, not a single chat message.
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The six-stage decision path
Different products implement the details differently. The following sequence is a control model synthesized from guidance published by OpenAI and Microsoft Visual Studio Code; it is not a claim that every agent has identical internal reasoning.
- Request interpretation. The model reads the prompt, repository context, tool descriptions, and any files it has been allowed to inspect. It forms a plan that may include editing, overwriting, renaming, or deleting.
- Tool selection. It chooses a capability that can change state: a file-edit tool, terminal command, MCP action, IDE operation, or child process. The exact command or arguments should be visible to the control layer.
- Policy evaluation. The harness checks the permission mode, command or tool approval rules, protected-file rules, and whether the target is inside the writable sandbox. A request can be blocked here even if the model proposed it.
- Execution. If permitted, the selected process performs the filesystem operation. Terminal commands and child processes may also invoke package scripts, access network resources, or create additional side effects.
- Evidence capture. Record the original prompt, tool call, approval result, exact command, output, affected paths, file hashes or patch, and any network-policy event. Capture failed and blocked attempts as well as successful ones.
- Human integration gate. Inspect the diff and checkpoint in an isolated worktree before committing, merging, deploying, or opening a pull request. A reversible review point is more useful than discovering an error after integration.
Permissions and sandboxing are separate controls
Approval settings answer, “Must someone authorize this action before it runs?” Sandboxing answers, “Where may the process read, write, or connect?” Treating them as interchangeable creates a dangerous gap.
| Control | What it governs | What it does not prove |
|---|---|---|
| Approval or permission mode | Whether a command or tool call runs automatically, waits for confirmation, or is rejected. | That the approved command is safe, correctly scoped, or reversible. |
| Filesystem sandbox | Which roots a supported process can read or write. | That a permitted change inside the root is intended. |
| Network policy | Which outbound connections are denied, allowed, or require approval. | That data sent to an allowed destination is appropriate. |
| Version control and checkpoints | How changes can be inspected, restored, or isolated. | That an operation was blocked before it happened. |
Microsoft Visual Studio Code’s documentation dated October 14, 2026, explicitly separates approvals from sandboxing. OpenAI’s “Running Codex safely at OpenAI,” published May 8, 2026, likewise treats sandbox boundaries, approvals, network policy, and telemetry as different parts of the control system.
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The more often a system acts without synchronous confirmation, the more important scoping, reversibility, and telemetry become. Use the labels exposed by your agent platform, but evaluate the behavior rather than the name.
| Operating style | Who decides at execution time? | Typical risk posture | Controls still required |
|---|---|---|---|
| Per-call or manual approval | A person approves each proposed command or tool action. | More interruption; a person can still approve a mistaken or malicious command. | Scoped writable roots, protected files, sandboxing, network policy, and diff review. |
| Scoped assisted mode | Predefined rules automatically allow low-risk actions and escalate exceptions. | Lower interruption, but rule mistakes can widen the blast radius. | Explicit path and command rules, denial defaults for sensitive files, and complete event logs. |
| Auto-review | A separate reviewer evaluates actions at the sandbox boundary instead of requiring a synchronous user stop. | Fewer human interruptions, with decisions delegated to an automated reviewer. | Independent policy, tamper-resistant telemetry, narrow access, and a human integration gate. |
| Allow-all or Autopilot-style mode | The agent proceeds with little or no per-action confirmation. | Highest potential blast radius if the prompt, repository, tool, or policy is wrong. | Disposable environments, deny-by-default network rules, protected paths, checkpoints, and post-run review. |
OpenAI Alignment’s April 30, 2026 report on Auto-review describes an internal result in which human stops occurred “roughly 200x less often” and reviewed actions were approved “around 99%” of the time. Those are provider-reported internal figures, not a universal benchmark and not evidence that an autonomous mode is safe in every repository.
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Prevent deletion and overwrite before they happen
Give the agent the smallest writable scope
Set the writable root to the repository or subdirectory needed for the task. Keep unrelated repositories, home-directory data, credentials, production exports, and deployment infrastructure outside that root. Read access should be narrower than necessary too: an agent does not need to inspect every neighboring project to update one service.
For a multi-repository task, use separate sessions or explicitly mounted roots rather than one broad workspace. Record the repository, branch, and worktree in the session metadata so an investigator can tell exactly where a command ran.
Protect sensitive paths
Require explicit approval for environment configuration, deployment manifests, credentials, signing material, production data exports, and other files whose replacement would have consequences beyond the local checkout. Sensitive-file protection should apply even when a command is otherwise allowed by a broad rule.
Do not rely on filename matching alone. Protect parent directories, generated copies, symlinks, and alternate extensions where your platform permits it. A rule that protects .env but not an exported secret file in another directory is incomplete.
Enforce filesystem and network boundaries
Use an OS-level or container boundary for terminal commands and child processes. The boundary should restrict file-system access and outbound connections, not merely display a warning. Deny unneeded network access and log every allow, deny, and approval decision. Network-enabled commands can create side effects that a local diff will not reveal.
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Prefer reversible execution
Run risky work in a disposable container or a separate Git worktree. Keep the active checkout protected until the diff has been reviewed. A checkpoint, commit, or snapshot made before the session gives you a known restoration point; it does not replace a review because an unwanted change can still be committed.
Choose approval rules deliberately
Per-call or narrowly scoped approvals are appropriate for destructive actions. Treat “Allow all,” “YOLO,” and Autopilot-style settings as elevated-risk modes. If an unattended run is necessary, compensate with a disposable worktree, a restricted sandbox, protected files, denied network access, and an automatic stop on policy violations.
What an audit record should capture
An audit trail must connect what the user asked for to what the process actually did. Store structured events in a central log or SIEM where they can be correlated, and protect the log from modification by the agent being investigated.
| Event field | Why it matters |
|---|---|
| Session and agent identity | Distinguishes concurrent runs, users, service accounts, and model-driven workers. |
| Repository, branch, and worktree | Identifies the exact checkout and prevents a path such as config.yml from being misread across projects. |
| Original user prompt and relevant context | Shows the stated intent and helps identify instructions injected through repository content. |
| Model-generated tool call | Preserves the selected tool, structured arguments, and requested operation before execution. |
| Exact command and arguments | Allows an investigator to distinguish a targeted edit from a wildcard deletion or recursive operation. |
| Target paths | Records resolved paths, not only the relative path typed by the model; include symlink resolution where available. |
| Approval mode and decision | Shows whether the action was manually approved, automatically allowed, denied, or escalated. |
| Sandbox and network policy | Captures the boundaries in force when the command ran, including network allow, deny, or approval events. |
| Start and end time, exit code | Supports a reliable timeline and distinguishes a blocked or failed command from a successful one. |
| Standard output and error | Preserves warnings, prompts, stack traces, and tool messages that may explain a partial change. |
| Before/after hashes or patch | Proves what content changed. For deletion, retain the prior hash and, where policy allows, the recoverable content. |
| MCP server and endpoint details | Identifies external tools and destinations involved in the operation. |
| Reviewer and integration decision | Records who inspected the result and whether it was reverted, committed, merged, deployed, or rejected. |
OpenAI’s organizational security guidance states: “We also preserve agent-native telemetry so we can understand and audit what the agent did.” The important word is agent-native: ordinary process logs should be correlated with the model’s tool activity and policy decisions, not stored as an unrelated stream.
Implement a reviewable Git workflow
Version control supplies the most practical recovery path for source files, provided the agent does not work directly in the checkout you intend to ship.
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- Create an isolated worktree. From a clean repository, run
git worktree add ../agent-review -b agent/review HEAD. Give the agent the new directory as its writable root. - Record a starting point. Save the commit ID, branch, worktree path, and session ID in the run record. If the task generates untracked files, note that they are part of the review scope.
- Run the agent with scoped permissions. Deny access to credentials and unrelated paths; require approval for protected files and destructive commands.
- Inspect the complete change set. Use
git status --short,git diff --stat, andgit diff --no-ext-diff. Check untracked files separately because a normal diff may not display them. - Test the intended behavior. Run the project’s existing checks in the isolated worktree. A passing test does not make an unintended deletion acceptable, so compare the diff with the original request.
- Integrate or discard deliberately. Commit or copy only reviewed changes. If the result is wrong, remove the worktree or restore the starting commit instead of repairing the live checkout by guesswork.
Microsoft Visual Studio Code’s security guidance, published October 14, 2026, says: “Always review AI-generated code before committing.” The same principle applies to generated configuration, scripts, migrations, and file removals.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to investigate a missing or overwritten file
1. Establish whether the operation ran
Look for a tool-call event, approval decision, process start, exit code, and affected path. A model message that says it deleted a file is not proof that the command executed. Conversely, a successful command can be recorded even if the agent’s final response is truncated.
2. Identify the exact mechanism
Determine whether the event was a direct file write, a rename, a recursive delete, a generated build artifact, an MCP operation, or a child process. Compare the exact arguments with the resolved paths. Wildcards, relative paths, symlinks, and scripts are common reasons that the apparent target differs from the actual one.
3. Compare content and timestamps
Use the recorded patch or hashes, Git history, filesystem snapshots, and backup systems. A missing path with a prior hash indicates deletion; a changed hash at the same path indicates overwrite. If no before-state was captured, state that limitation rather than claiming certainty about the prior contents.
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Confirm which sandbox, approval mode, sensitive-file rules, and network policy were active. A blocked event, a denied network request, and a successful local write describe different incidents and require different remediation.
5. Determine whether instructions were injected
Review repository files, issue text, generated documentation, and tool responses that the model read before acting. A malicious instruction hidden in a file can redirect an otherwise legitimate task. The nd7 project documentation summarizes the threat: “One wrong command, or a malicious instruction hidden in a file the agent reads, can delete a project, leak a key or push to the wrong branch.” Treat untrusted repository content as input, not as authority.
6. Contain and recover
- Stop the session and revoke unnecessary credentials or network access.
- Preserve the agent logs, command output, policy state, and affected worktree before cleanup.
- Restore from the last verified commit, checkpoint, snapshot, or backup.
- Rotate any secret that may have been read or transmitted.
- Update path rules, approval requirements, or sandbox mounts before rerunning the task.
Designing telemetry that survives an incident
Log records should be append-only or tamper-evident from the agent’s perspective. Include a session identifier in every event so a prompt, tool call, process, network decision, and reviewer action can be joined without relying on timestamps alone. Synchronize clocks where possible and preserve the original event time as well as ingestion time.
Capture blocked and denied actions. They reveal what the agent attempted, expose repeated policy probing, and help distinguish a safe stop from a tool failure. Keep command arguments and paths in a privacy-aware form, but do not redact so much that an investigator cannot identify the target or reconstruct the decision.
For larger teams, forward structured events through OpenTelemetry-compatible instrumentation or an equivalent central pipeline. Define retention according to the sensitivity of source code and credentials, and restrict who can read prompts and tool arguments. Audit access to the audit log itself.
Comparing operating models by risk, not convenience
When selecting a mode, evaluate all seven dimensions below. A mode that reduces interruptions may still be unacceptable if it expands writable scope or leaves no recoverable diff.
| Decision axis | Questions to answer |
|---|---|
| Approval authority | Who can allow a destructive action, and is the decision recorded? |
| Sandbox constraint | Does the boundary restrict every terminal and child process, or only the primary tool? |
| Sensitive-file protection | Are credentials, deployment files, and production exports denied or escalated by default? |
| Network reach | Which destinations are allowed, and are all allow, deny, and approval events logged? |
| Reversibility | Can you restore a worktree, checkpoint, snapshot, or backup without reconstructing files manually? |
| Telemetry completeness | Can you correlate prompt, tool call, approval, command result, file diff, MCP activity, and reviewer decision? |
| Operational interruption | How often must a person stop to approve work, and is the reduced interruption worth the added blast radius? |
For production repositories, a sensible default is a narrowly scoped sandbox, denied-by-default network access, explicit approval for sensitive paths, an isolated worktree, and a mandatory diff review. Less sensitive disposable projects can trade some interruption for more automation, but should retain the same evidence chain.
What is and is not established about accidental deletion rates
There is no authoritative cross-agent statistic establishing how often agents accidentally delete or overwrite files. Rates vary with the model, tools, prompt, repository, permissions, sandbox, and review process, so a single percentage would be misleading. Measure your own blocked, allowed, reverted, and integrated actions using the event schema above.
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Quick Recap
A concise control checklist
- Set the writable root to only the repository or subdirectory required.
- Keep secrets, unrelated repositories, and production data outside the mount.
- Escalate environment files, deployment manifests, credentials, and production exports.
- Enforce OS-level filesystem and network restrictions for terminal and child processes.
- Use per-call or scoped approvals for destructive operations; treat allow-all modes as elevated risk.
- Run unattended work in a disposable container or isolated Git worktree.
- Capture prompts, tool calls, approvals, commands, results, paths, hashes or patches, MCP details, and network events.
- Review the complete diff, including untracked files, before commit, merge, deployment, or pull request.
- Retain a checkpoint and a tested restore path.
- Preserve blocked attempts and investigate repository content for prompt injection.
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