Heap Out-of-Bounds Read in PyTorch Torchvision GIF Decoder

Executive Summary

This vulnerability is an out-of-bounds heap read in PyTorch torchvision's GIF decoder. When processing a maliciously crafted or truncated GIF file, the decoder incorrectly uses an unclamped length value in a memory copy operation. This causes it to read beyond the allocated buffer, leading to a segmentation fault or potential disclosure of adjacent heap memory.

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Detection Guidance

To detect this vulnerability, inspect PyTorch torchvision versions up to 0.28.0. Check for crashes or segmentation faults when processing GIF files. Use tools like `pip show torchvision` to verify installed versions. Monitor logs for SIGSEGV errors during image decoding operations.

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Impact Analysis

The primary impact is denial of service via application crashes (segmentation faults). Attackers could supply specially crafted GIF files to trigger these crashes. There is also a theoretical risk of information disclosure if adjacent memory contents are exposed, though this was not confirmed in testing.

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Compliance Impact

This vulnerability primarily causes denial of service via segmentation faults when processing malicious GIF files. While it may theoretically disclose adjacent heap memory, no confirmed cases of sensitive data exposure were found. Compliance impact depends on whether the affected system processes untrusted GIF inputs in regulated environments. For GDPR, this could pose risks if personal data is processed during the crash. For HIPAA, it may affect systems handling medical imaging if crashes disrupt operations.

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Mitigation Strategies

Upgrade PyTorch torchvision to a version containing commit 4e05dc2 or later. Avoid processing untrusted GIF files until patched. Implement input validation for GIF files before decoding. Monitor for crashes or memory corruption during image processing.

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