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Python detectors (123/123)

Improper privilege management Spawning a process without main module Integer overflow Catch and swallow exception 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External Entity Pytorch use nondeterministic algoritm Set SNS Return Subscription ARN Tensorflow enable ops determinism Outdated subprocess module API Improper input validation Improper authentication Missing pagination Semaphore overflow prevention Insecure cookie Usage of an API that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 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 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 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 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 Violation of PEP8 programming recommendations Insecure temporary file or directory Complex code hard to maintain 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 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

Inefficient polling of AWS resource Low

Custom polling can be inefficient and prone to error. Consider using AWS waiters instead. A waiter is an abstraction used to poll AWS resources, such as DynamoDB tables or Amazon S3 buckets, until a desired state is reached.

Detector ID

python/aws-polling-instead-of-waiter@v1.0

Category

Common Weakness Enumeration (CWE)

Tags

# aws-python-sdk # efficiency

Noncompliant example

1def polling_vs_waiters_noncompliant(response):
2    import boto3
3    ec2_client = boto3.client('ec2', region_name='us-east-1')
4    ec2_instance_id = response['Instances'][0]['InstanceId']
5
6    attempts = 0
7    while True:
8        print("Waiting for EC2 instance to be up")
9        # Noncompliant: uses custom polling instead of waiters feature.
10        rsp = ec2_client.describe_instance_status(
11            InstanceIds=[
12                str(ec2_instance_id)
13            ],
14            IncludeAllInstances=True
15        )
16
17        instance_status = rsp['Statuses'][0]['InstanceStatus']['Status']
18        system_status = rsp['Statuses'][0]['SystemStatus']['Status']
19
20        if str(instance_status) == 'ok' and str(system_status) == 'ok':
21            break
22        if str(instance_status) == 'impaired' or \
23                str(instance_status) == 'insufficient-data' or \
24                str(system_status) == 'failed' or \
25                str(system_status) == 'insufficient-data':
26            print('Instance status is ' + str(instance_status))
27            print('System status is ' + str(system_status))
28            tear_down()
29            exit(1)
30
31        attempts = attempts + 1
32        if attempts >= MAX_ATTEMPTS:
33            print("MAX wait time for EC2 instance to be up reached.")
34            print("Tearing down")
35            tear_down()
36            exit(1)
37
38        time.sleep(10)

Compliant example

1def polling_vs_waiters_compliant():
2    import boto3
3    client = boto3.client('kinesis', region_name='us-east-1')
4    # Setup the Kinesis with 1 shard.
5    stream_name = "tf_kinesis_test_1"
6    client.create_stream(StreamName=stream_name, ShardCount=1)
7    # Wait until stream exists, default is 10 * 18 seconds.
8    # Compliant: uses waiters feature.
9    client.get_waiter('stream_exists').wait(StreamName=stream_name)
10    for i in range(10):
11        data = "D" + str(i)
12        client.put_record(
13            StreamName=stream_name,
14            Data=data,
15            PartitionKey="TensorFlow" + str(i)
16            )