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Detector Library

Python detectors (131/131)

Improper privilege management Spawning a process without main module Integer overflow Catch and swallow exception Insufficient Logging CDK Unauthenticated LDAP requests Path traversal Loose file permissions Exposure of Sensitive Information CDK File injection Incorrect use of Process.terminate API XML External Entity Pytorch use nondeterministic algoritm Set SNS Return Subscription ARN Tensorflow enable ops determinism Outdated subprocess module API Improper input validation Improper authentication Missing pagination Semaphore overflow prevention Insecure cookie Usage of an API that is not recommended - Low Severity Socket connection timeout AWS client not reused in a Lambda function Pytorch assign in place mod Leaky subprocess timeout Pytorch disable gradient calculation Risky use of dict get method XPath injection Missing authorization Multidimensional list initialization using replication is error prone SQL injection Pytorch miss call to eval AWS AppConfig Improper certificate validation URL redirection to untrusted site Mutually exclusive call Notebook best practice violation Stack trace exposure Use of a deprecated method AWS api logging disabled cdk OS command injection AWS credentials logged Missing Authorization CDK Zip bomb attack Sensitive data stored unencrypted due to partial encryption Synchronous publication of AWS Lambda metrics Unrestricted upload of dangerous file type Pytorch redundant softmax Insecure connection using unencrypted protocol Unauthenticated Amazon SNS unsubscribe requests might succeed Insecure Socket Bind Insecure CORS policy Cross-site request forgery Garbage collection prevention in multiprocessing Catch and rethrow exception Weak algorithm used for Password Hashing Missing none check on response metadata Sensitive information leak Client-side KMS reencryption Override of reserved variable names in a Lambda function Docker arbitrary container run Direct dict object modification Catastrophic backtracking regex Resource management errors cdk Resource leak Tensorflow redundant softmax AWS insecure transmission CDK Public method parameter validation Improper error handling Time zone aware datetimes Pytorch control sources of randomness Deadlocks caused by improper multiprocessing API usage Low maintainability with low class cohesion Untrusted AMI images Notebook invalid execution order Confusion between equality and identity in conditional expression Pytorch sigmoid before bceloss Pytorch data loader with multiple workers Pytorch avoid softmax with nllloss Pytorch miss call to zero grad Error prone sequence modification AWS missing encryption of sensitive data cdk Bad exception handling Use of Default Credentials CDK Notebook variable redefinition Do not pass generic exception rule Usage of an API that is not recommended - High Severity Insecure cryptography S3 partial encrypt CDK Cross-site scripting Mutable objects as default arguments of functions Improper Access Control CDK Violation of PEP8 programming recommendations Insecure temporary file or directory Complex code hard to maintain aws kmskey encryption cdk Usage of an API that is not recommended Enabling and overriding debug feature Deserialization of untrusted object Use of an inefficient or incorrect API Avoid using nondeterministic Tensorflow API Inefficient string concatenation inside loop Improper sanitization of wildcards or matching symbols Insecure hashing Using AutoAddPolicy or WarningPolicy Log injection Weak obfuscation of web request Socket close platform compatibility Unsanitized input is run as code Batch request with unchecked failures Inefficient polling of AWS resource Hardcoded interface binding Hardcoded IP address Hardcoded credentials Server-side request forgery Module injection Unnecessary iteration Tensorflow control sources of randomness Missing Authentication for Critical Function CDK Usage of an API that is not recommended - Medium Severity Unsafe Cloudpickle Load Incorrect binding of SNS publish operations PyTorch create tensors directly on device Inefficient new method from hashlib Dangerous global variables Multiple values in return statement is prone to error LDAP injection Clear text credentials Missing S3 bucket owner condition AWS missing encryption CDK

Tag: machine-learning

Pytorch use nondeterministic algoritm

APIs with nondeterministic algorithm are used

Tensorflow enable ops determinism

Non-deterministic ops might return different outputs when run with the same inputs.

Pytorch assign in place mod

Detects if a torch variable is modified in place inside an assignment.

Pytorch disable gradient calculation

Checks if gradient calculation is disabled during evaluation.

Pytorch miss call to eval

Checks if eval() is called before validating or testing a model.

Notebook best practice violation

Best practices to improve the maintainability of notebooks.

Pytorch redundant softmax

Detects if Softmax is used with CrossEntropyLoss.

Tensorflow redundant softmax

Detects if Softmax is explicitly computed.

Pytorch control sources of randomness

Not setting seeds for the random number generators in Pytorch can lead to reproducibility issues.

Notebook invalid execution order

Notebook has uninitialized variable usage given the execution order

Pytorch sigmoid before bceloss

The computation of the bceloss using sigmoid values as inputs can be replaced by a single BCEWithLogitsLoss which is numerically more stable.

Pytorch data loader with multiple workers

Using DataLoader with num_workers greater than 0 can cause increased memory consumption over time when iterating over native Python objects such as list or dict.

Pytorch avoid softmax with nllloss

Checks if Softmax is used with NLLLoss function.

Pytorch miss call to zero grad

Zero out the gradients before doing a backward pass

Notebook variable redefinition

A variable is re-defined in multiple cells with different types.

Avoid using nondeterministic Tensorflow API

Detects if nondeterministic tensorflow APIs are used.

Tensorflow control sources of randomness

Detects if a random seed is set before random number generation.

PyTorch create tensors directly on device

Creating PyTorch tensors on the CPU and then moving them to the device is inefficient.