Trusted execution environments like Intel SGX provide enclaves, which offer strong security guarantees for applications. Running entire applications inside enclaves is possible, but this approach leads to a large trusted computing base (TCB). As such, various tools have been developed to partition programs written in languages such as C or Java into trusted and untrusted parts, which are run in and out of enclaves respectively. However, those tools depend on language-specific taint-analysis and partitioning techniques. They cannot be reused for other languages and there is thus a need for tools that transcend this language barrier. We address this challenge by proposing a multi-language technique to specify sensitive code or data, as well as a multilanguage tool to analyse and partition the resulting programs for trusted execution environments like Intel SGX. We leverage GraalVM's Truffle framework, which provides a language-agnostic abstract syntax tree (AST) representation for programs, to provide special AST nodes called secure nodes that encapsulate sensitive program information. Secure nodes can easily be embedded into the ASTs of a wide range of languages via Truffle's polyglot API. Our technique includes a multi-language dynamic taint tracking tool to analyse and partition applications based on our generic secure nodes. Our extensive evaluation with microand macro-benchmarks shows that we can use our technique for two languages (Javascript and Python), and that partitioned programs can obtain up to 14.5% performance improvement as compared to unpartitioned versions.