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Detector Library

Python detectors (123/123)

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rule Usage of an API that is not recommended - High Severity Insecure cryptography Tensorflow enable ops determinism Outdated subprocess module API S3 partial encrypt CDK Improper input validation Improper authentication Cross-site scripting Missing pagination Semaphore overflow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

Low

Showing all detectors for the Python language with low severity.

Improper error handling

Improper error handling can enable attacks and lead to unwanted behavior.

Time zone aware datetimes

Using naive datetime objects might cause time zone related issues.

Incorrect use of Process.terminate API

The Process.terminate API might cause data corruption of shared resources.

Mutable objects as default arguments of functions

Default values in Python are created exactly once, when the function is defined. If that object is changed, subsequent calls to the function will refer to the changed object, leading to confusion.

Usage of an API that is not recommended - Low Severity

APIs that are not recommended were found - Low Severity.

AWS client not reused in a Lambda function

Recreating AWS clients in each Lambda function invocation is expensive.

Complex code hard to maintain

Complex code can be difficult to read and hard to maintain.

Risky use of dict get method

Using the get method from the dict class without default values can cause runtime exceptions.

Use of an inefficient or incorrect API

Incorrect use of API leads to ambiguity and inconsistency

Multidimensional list initialization using replication is error prone

list replication using replication operator creates references to the existing objects, not copies, which could introduce bugs.

Notebook best practice violation

Best practices to improve the maintainability of notebooks.

Use of a deprecated method

This code uses deprecated methods, which suggests that it has not been recently reviewed or maintained.

Inefficient polling of AWS resource

Custom polling can be inefficient and prone to error. Consider using AWS waiters instead.

Catch and rethrow exception

Catching and re-throwing an exception without further actions is redundant and wasteful.

Incorrect binding of SNS publish operations

Incorrect binding of SNS publish operations with the subscribe or create_topic operations might lead to latency issues.

Dangerous global variables

Global variables can be dangerous and cause bugs because they can be simultaneously accessed from multiple sections of a program.

Multiple values in return statement is prone to error

Methods that return multiple values can be difficult to read and prone to error.

Missing S3 bucket owner condition

Not setting the S3 bucket owner condition might introduce a risk of accidentally using a wrong bucket.

Direct dict object modification

Directly modifying the __dict__ object might cause undesirable behavior due to symbol table modification.