The team behind TensorFlow, Google's popular open source Python machine learning library, has revoked support for YAML due to an arbitrary code execution vulnerability. YAML is a general-purpose format used to store data and pass objects between processes and applications. Many Python applications use YAML to serialize and deserialize objects. According to an advisory on GitHub, TensorFlow and Keras, a wrapper library for TensorFlow, used an unsafe function to deserialize YAML-encoded machine learning models. "Given that YAML format support requires a significant amount of work, we have removed it for now," the maintainers of the library said in their advisory.

Given a Boolean formula $\varphi$ over the set of variables $X$ and a projection set $\mathcal{P} \subseteq X$, a subset of variables $\mathcal{I}$ is independent support of $\mathcal{P}$ if two solutions agree on $\mathcal{I}$, then they also agree on $\mathcal{P}$. The notion of independent support is related to the classical notion of definability dating back to 1901, and have been studied over the decades. Recently, the computational problem of determining independent support for a given formula has attained importance owing to the crucial importance of independent support for hashing-based counting and sampling techniques. In this paper, we design an efficient and scalable independent support computation technique that can handle formulas arising from real-world benchmarks. Our algorithmic framework, called Arjun, employs implicit and explicit definability notions, and is based on a tight integration of gate-identification techniques and assumption-based framework. We demonstrate that augmenting the state of the art model counter ApproxMC4 and sampler UniGen3 with Arjun leads to significant performance improvements. In particular, ApproxMC4 augmented with Arjun counts 387 more benchmarks out of 1896 while UniGen3 augmented with Arjun samples 319 more benchmarks within the same time limit.