fix: detect unsafe module references in .keras nested config objects#340
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scruge1 wants to merge 1 commit into
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fix: detect unsafe module references in .keras nested config objects#340scruge1 wants to merge 1 commit into
scruge1 wants to merge 1 commit into
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KerasLambdaDetectScan._get_keras_operator_names previously only checked top-level layers for class_name == "Lambda". This missed dangerous module references embedded in nested config objects (initializers, regularizers, constraints, dtype policies) which Keras resolves via importlib on load. Added _extract_unsafe_modules() which recursively traverses the entire config.json tree and flags any module/class_name pair where the module is not in the Keras/TensorFlow namespace. These are reported as "UnsafeModule" with CRITICAL severity. Added tests verifying: - Safe Keras configs return no issues - builtins.exec in kernel_initializer is detected - subprocess.Popen in top-level layer module is detected - tensorflow.* modules are treated as safe
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This PR fixes the huntr disclosure that was validated and awarded — PR #351 addresses the related nested-Lambda detection with a different framing/scope; the two are complementary rather than duplicate — this PR closes the awarded disclosure's exact unsafe-module resolution path. Happy to rebase if you'd like to land both. |
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Summary
_get_keras_operator_namesnow recursively traverses the entire config.json tree, not just top-level layersmodule/class_namepair where the module is outside the Keras/TensorFlow namespaceUnsafeModulewith CRITICAL severityWhy
The previous scanner only checked
class_name == "Lambda"in top-level layers. Keras config.json allows arbitrarymodulereferences in nested objects (initializers, regularizers, constraints, dtype policies) which Keras resolves viaimportlibon load. A malicious.kerasfile embeddingbuiltins.execas akernel_initializerpassed modelscan with 0 issues.Testing
builtins.exec, top-levelsubprocess.Popen, andtensorflow.*safe modulesbuiltins.exec