Biggest risks in the PyTorch 2.6 release for framework selection

I favor the direction of the PyTorch 2.6.0 patch because it makes the weights_only=True flag effective against the bypass identified in CVE-2025-32434. This vulnerability allowed a crafted file to bypass the recommended safe loading in PyTorch 2.5.1 and earlier. The decision to use pickle as the default serialization format allows any .pt or .bin file to act as an executable binary that runs native code at the moment of loading before any human validation occurs. In February 2024, researchers found 100 backdoored models on the Hugging Face Hub that used pickle payloads to open reverse shells. In April 2024, Wiz Research demonstrated a cross-tenant case where a malicious model with a reduce payload executed code inside a container. JFrog later published 22 vulnerabilities in MLflow, PyTorch, and MLeap in December 2024. These vulnerabilities often involve the framework running native code when it loads a model. While the 2.6.0 patch addresses the bypass of the weights_only=True flag, the underlying reliance on pickle remains a risk for teams evaluating JAX or TensorFlow.

Hardware stability risks in ROCm environments

I find the stability of the 2.6.0 release on specific hardware to be a significant risk. The bug in the 2.6.0 release specifically affects users on ROCm 6.2.4 who attempt to use the mem-efficient attention backend with a custom attention mask for their scaled dot product attention operations. When using the aotriton 0.8.0 backend, the median absolute difference for padded sequences reaches 0.0991 compared to the math backend, while the maximum absolute difference hits 0.6846. These errors occur when the SDPA with custom attn_mask is used with a mem-efficient backend in the stable ROCm release. Such errors occur during batched generation in Transformers, which can lead to incorrect model outputs in production environments. I would avoid this specific configuration if you are using an AMD Instinct MI250X or MI250 GPU. To resolve this, you must replace the torch/lib/aotriton.images/ directory with the 0.8.2 release assets from the ROCm GitHub repository. You should consider the hardware implications before committing to the 2.6.0 update if your workflow relies on ROCm.

The trade-off between JAX, TensorFlow, and PyTorch

I see the choice between JAX, TensorFlow, and PyTorch as a trade-off between performance, production tools, and research speed. JAX provides high performance because it uses the XLA compiler to optimize computations for GPUs and TPUs. It uses primitives like jit, grad, and vmap to provide composable transformations. However, JAX is still experimental and can be unstable, making it less ideal for building production systems. PyTorch dominates research, as 92% of the top 30 models on Hugging Face use PyTorch exclusively. PyTorch also powers over 70% of papers on arXiv and has over 82,000 stars on GitHub. TensorFlow remains the leader for large-scale deployment, with a 38% market share and tools like TFX and TFLite that run on 4 billion devices. Keras 3 provides a way to use PyTorch, JAX, or TensorFlow backends by changing an environment variable. For organizations on TensorFlow 2.x, migrating to Keras 3 with a PyTorch backend offers a way to access the PyTorch ecosystem while preserving existing model investments. The decision to switch is often driven by whether a team prioritizes the simplicity of PyTorch’s Pythonic syntax or the performance ceiling of JAX’s functional engine.

Will the industry finally move away from pickle entirely?