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Extensive research has been conducted to explore cryptographic API misuse in Java. However, despite the tremendous popularity of the Python language, uncovering similar issues has not been fully explored. The current static code analysis tools for Python are unable to scan the increasing complexity of the source code. This limitation decreases the analysis depth, resulting in more undetected cryptographic misuses. In this research, we propose Cryptolation, a Static Code Analysis (SCA) tool that provides security guarantees for complex Python cryptographic code. Most existing analysis tools for Python solely focus on specific Frameworks such as Django or Flask. However, using a SCA approach, Cryptolation focuses on the language and not any framework. Cryptolation performs an inter-procedural data-flow analysis to handle many Python language features through variable inference (statically predicting what the variable value is) and SCA. Cryptolation covers 59 Python cryptographic modules and can identify 18 potential cryptographic misuses that involve complex language features. In this paper, we also provide a comprehensive analysis and a state-of-the-art benchmark for understanding the Python cryptographic Application Program Interface (API) misuses and their detection. Our state-of-the-art benchmark PyCryptoBench includes 1,836 Python cryptographic test cases that cover both 18 cryptographic rules and five language features. PyCryptoBench also provides a framework for evaluating and comparing different cryptographic scanners for Python. To evaluate the performance of our proposed cryptographic Python scanner, we evaluated Cryptolation against three other state-of-the-art tools: Bandit, Semgrep, and Dlint. We evaluated these four tools using our benchmark PyCryptoBench and manual evaluation of (four Top-Ranked and 939 Un-Ranked) real-world projects. Our results reveal that, overall, Cryptolation achieved the highest precision throughout our testing; and the highest accuracy on our benchmark. Cryptolation had 100% precision on PyCryptoBench, and the highest precision on real-world projects. 

In this article, we conduct a measurement study to comprehensively compare the accuracy impacts of multiple embedding options in cryptographic API completion tasks. Embedding is the process of automatically learning vector representations of program elements. Our measurement focuses on design choices of three important aspects,program analysis preprocessing,token-level embedding, andsequence-level embedding. Our findings show that program analysis is necessary even under advanced embedding. The results show 36.20% accuracy improvement, on average, when program analysis preprocessing is applied to transfer bytecode sequences into API dependence paths. With program analysis and the token-level embedding training, the embeddingdep2vecimproves the task accuracy from 55.80% to 92.04%. Moreover, only a slight accuracy advantage (0.55%, on average) is observed by training the expensive sequence-level embedding compared with the token-level embedding. Our experiments also suggest the differences made by the data. In the cross-app learning setup and a data scarcity scenario, sequence-level embedding is more necessary and results in a more obvious accuracy improvement (5.10%).