Securing file and cloud storage tools used by AI agents means treating file paths and bucket references with the same suspicion you'd apply to any user-controlled input that determines what a system reads or writes, because a model-generated path is exactly that: input the model produced, which means it can be manipulated the same way any other tool parameter can. A tool that accepts an arbitrary path string and reads or writes whatever's there gives a manipulated or mistaken call a direct route to files well outside its intended scope, including configuration files, credentials, or other users' data sitting in the same storage system.
Scope access to specific paths or buckets, not the whole file system
Define the exact directory, bucket, or prefix a given tool can touch, and enforce that boundary at the tool level rather than relying on the model to only request paths within an intended scope. A document-summarization tool doesn't need access to an entire shared drive when it only ever needs to read files a specific user uploaded.
Validate and normalize paths strictly before use
Reject path traversal patterns, symbolic links that escape the intended directory, and any path that resolves outside the tool's defined scope, even after normalization. A parameter that looks like a simple relative path can still be crafted to climb out of an intended directory if the underlying file access logic doesn't defend against it explicitly.
Separate read access from write and delete access clearly
A tool that only needs to read a file shouldn't share a credential capable of writing or deleting, since a manipulated read request has far less potential damage than a manipulated write or delete request against the same storage location. Treat these as genuinely separate permission tiers, not a convenience toggle on the same broad access.
Treat file content returned to the model as untrusted, the same as any tool output
A file the model reads through a tool can contain text crafted to manipulate the model's next decision, the same indirect injection risk that applies to any other tool response. This matters especially for document types that commonly get shared with external parties, since that's exactly where a deliberately crafted malicious file is most likely to originate.
Watch for unusual bulk access patterns
A tool that normally reads one file per request suddenly being asked to enumerate or download an entire directory is a meaningful deviation worth flagging, since bulk file access is one of the more direct paths to a large-scale data exposure if a call ever gets manipulated into requesting far more than a typical legitimate use case would.
Apply the same rigor to temporary and cache storage
It's easy to focus scoping and validation effort on a tool's primary storage location while overlooking the temporary files, caches, or intermediate outputs a tool creates along the way. Those locations often end up with looser permissions simply because nobody thought of them as the "real" storage path, which makes them an easy target for a manipulated call looking for a path that isn't as carefully locked down as the main one.
Consider signed URLs instead of direct credential access
Rather than giving a tool a persistent credential capable of reaching a whole bucket or share, consider generating short-lived, narrowly scoped signed URLs for the specific object a given call actually needs. This limits both how long access remains valid and exactly what it covers, so a leaked or misused URL exposes one object for a short window instead of an entire storage location indefinitely.
Where TELEON fits
TELEON enforces policy at the tool-call boundary, which can apply scope restrictions to file and storage tool parameters and flag unusual access patterns for calls that pass through the gateway. The audit trail supports reconstructing exactly what was read, written, or deleted after the fact.
Configuring the actual storage-level permissions, bucket policies, and path restrictions that back a given tool is infrastructure work specific to each organization's storage setup and existing access control model.
A short checklist
- Scope each file or storage tool to a specific directory, bucket, or prefix.
- Validate and normalize paths strictly, rejecting traversal patterns and unexpected symlinks.
- Separate read permissions from write and delete permissions clearly.
- Treat file content returned to the model as untrusted input, not verified data.
- Watch for bulk access patterns that deviate from a tool's typical single-file usage.
- Log every file operation with the path, action, and requesting identity.
- Test path parameters deliberately with traversal and escape attempts.
File and storage tools tend to get less scrutiny than database or messaging tools, mostly because "reading a file" sounds inherently less dangerous than sending a message or querying a database. That intuition breaks down quickly once you consider what a manipulated path parameter can actually reach, and it's worth applying the same rigor here that you'd apply to any tool with a direct line to your underlying data.