Static Analysis of Shape in TensorFlow Programs

Machine learning has been widely adopted in diverse science and engineering domains, aided by reusable libraries and quick development patterns. The TensorFlow library is probably the best-known representative of this trend and most users employ the Python API to its powerful back-end. TensorFlow programs are susceptible to several systematic errors, especially in the dynamic typing setting of Python. We present Pythia, a static analysis that tracks the shapes of tensors across Python library calls and warns of several possible mismatches. The key technical aspects are a close modeling of library semantics with respect to tensor shape, and an identification of violations and error-prone patterns. Pythia is powerful enough to statically detect (with 84.62% precision) 11 of the 14 shape-related TensorFlow bugs in the recent Zhang et al. empirical study - an independent slice of real-world bugs

Ethainter: A Smart Contract Security Analyzer for Composite Vulnerabilities

Smart contracts on permissionless blockchains are exposed to inherent security risks due to interactions with untrusted entities. Static analyzers are essential for identifying security risks and avoiding millions of dollars worth of damage. We introduce Ethainter, a security analyzer checking information flow with data sanitization in smart contracts. Ethainter identifies composite attacks that involve an escalation of tainted information, through multiple transactions, leading to severe violations. The analysis scales to the entire blockchain, consisting of hundreds of thousands of unique smart contracts, deployed over millions of accounts. Ethainter is more precise than previous approaches, as we confirm by automatic exploit generation (e.g., destroying over 800 contracts on the Ropsten network) and by manual inspection, showing a very high precision of 82.5% valid warnings for end-to-end vulnerabilities. Ethainter’s balance of precision and completeness offers significant advantages over other tools such as Securify, Securify2, and teEther

Symbolic Value-Flow Static Analysis: Deep, Precise, Complete Modeling of Ethereum Smart Contracts

We present a static analysis approach that combines concrete values and symbolic expressions. This symbolic value-flow (”symvalic”) analysis models program behavior with high precision, e.g., full path sensitivity. To achieve deep modeling of program semantics, the analysis relies on a symbiotic relationship between a traditional static analysis fixpoint computation and a symbolic solver: the solver does not merely receive a complex “path condition” to solve, but is instead invoked repeatedly (often tens or hundreds of thousands of times), in close cooperation with the flow computation of the analysis. The result of the symvalic analysis architecture is a static modeling of program behavior that is much more complete than symbolic execution, much more precise than conventional static analysis, and domain-agnostic: no special-purpose definition of anti-patterns is necessary in order to compute violations of safety conditions with high precision. We apply the analysis to the domain of Ethereum smart contracts. This domain represents a fundamental challenge for program analysis approaches: despite numerous publications, research work has not been effective at uncovering vulnerabilities of high real-world value. In systematic comparison of symvalic analysis with past tools, we find significantly increased completeness (shown as 83-96% statement coverage and more true error reports) combined with much higher precision, as measured by rate of true positive reports. In terms of real-world impact, since the beginning of 2021, the analysis has resulted in the discovery and disclosure of several critical vulnerabilities, over funds in the many millions of dollars. Six separate bug bounties totaling over $350K have been awarded for these disclosures

ponder-lab/ML: 0.10.0

<h2>What's Changed</h2> <ul> <li>Add model call tests by @tatianacv in https://github.com/ponder-lab/ML/pull/53</li> <li>Handle global writes in the Python ModRef analysis by @khatchad in https://github.com/ponder-lab/ML/pull/55</li> </ul> <p><strong>Full Changelog</strong>: https://github.com/ponder-lab/ML/compare/0.9.0...0.10.0</p&gt