Conversation
ResizeNearestNeighborPrepare() validates the size tensor's rank, type and constness but never compares its values against the declared output dims. Eval() then writes size[0]*size[1]*depth bytes into the output buffer, which the allocator sized from the declared shape, so a mismatch produces a linear out-of-bounds write. Reject such models at Prepare time and add a regression test.
Author
|
@veblush Could I have a review on this when you're free? It's a small fix that validates the size tensor. |
This branch was successfully deployed
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
3 participants
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
BUG=n/a
ResizeNearestNeighborPrepare() validates the size tensor's rank, type and constness but never compares its values against the declared output dimensions. Eval() then writes size[0]*size[1]*depth bytes sequentially into the output buffer, which the allocator sized from the declared shape, so a mismatch produces a linear out-of-bounds write (attacker-controlled length and content).
Reject such models at Prepare time and add a regression test that asserts a size/output mismatch fails allocation.
Verified locally: with this change, a model declaring output [1,1,1,1] with a size tensor {1024,1024} is rejected at AllocateTensors instead of overflowing the arena.