CVE-2020-15211
Out of bounds access in tensorflow-lite
Em resumo
Modelos do TensorFlow Lite podem ser manipulados para acessar memória fora dos limites dos arrays usando um índice especial (-1) para referências de tensores. Isso permite que atacantes leiam ou escrevam dados em locais de memória não autorizados quando um modelo malicioso é carregado.
Detalhe técnico
Vulnerabilidade de leitura/escrita fora dos limites do array no validador de modelos flatbuffer do TensorFlow Lite, explorando a falha na validação do valor sentinela -1 usado para índices de tensores opcionais. O esquema de dupla indexação (subgrafo → operador → tensor) não restringe o uso de -1 apenas aos operadores que suportam entradas opcionais, permitindo overflow de buffer de heap via arquivos de modelo especialmente criados. Vetor de ataque é carregamento local/remoto de modelo com escrita limitada mas leitura arbitrária.
Resumo gerado e traduzido por IA a partir da descrição oficial.
In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices for the tensors, indexing into an array of tensors that is owned by the subgraph. This results in a pattern of double array indexing when trying to get the data of each tensor. However, some operators can have some tensors be optional. To handle this scenario, the flatbuffer model uses a negative `-1` value as index for these tensors. This results in special casing during validation at model loading time. Unfortunately, this means that the `-1` index is a valid tensor index for any operator, including those that don't expect optional inputs and including for output tensors. Thus, this allows writing and reading from outside the bounds of heap allocated arrays, although only at a specific offset from the start of these arrays. This results in both read and write gadgets, albeit very limited in scope. The issue is patched in several commits (46d5b0852, 00302787b7, e11f5558, cd31fd0ce, 1970c21, and fff2c83), and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that only operators which accept optional inputs use the `-1` special value and only for the tensors that they expect to be optional. Since this allow-list type approach is erro-prone, we advise upgrading to the patched code.
CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N