A North Korea-linked threat actor (UNC1069) hijacked the npm account of an axios maintainer and published two backdoored versions. They were live for ~3 hours before npm removed them.
axios@1.14.1 published (latest dist-tag)axios@0.30.4 published (legacy dist-tag)
| import java.util.concurrent.{ExecutorService, Executors} | |
| import cats.effect.{ContextShift, IO, Resource} | |
| import scala.concurrent.{ExecutionContext, ExecutionContextExecutor} | |
| class KafkaContext(cs: ContextShift[IO]) { | |
| // A thread pool with exactly 1 thread | |
| private val threadPool = Executors.newFixedThreadPool(1) | |
| protected val synchronousExecutionContext = ExecutionContext.fromExecutor(threadPool) | |
| def execute[A](f: => A): IO[A] = cs.evalOn(synchronousExecutionContext)(IO(f)) |
| import cats.effect.{ConcurrentEffect, ExitCode, IO, IOApp} | |
| import scala.concurrent.{ExecutionContext, ExecutionContextExecutor} | |
| object ConsumerApplication extends IOApp { | |
| implicit private val ec: ExecutionContextExecutor = ExecutionContext.global | |
| private val cs = IO.contextShift(ec) | |
| implicit private val concurrentEffect: ConcurrentEffect[IO] = IO.ioConcurrentEffect(cs) | |
| val config = ConsumerConfig("localhost:9092", "test-topic", "consumer-group-1") |
| import java.time.Duration | |
| import java.util.{Collections, Properties} | |
| import cats.effect.{IO, Resource, Timer} | |
| import org.apache.kafka.clients.consumer.{KafkaConsumer, ConsumerConfig => KafkaConsumerConfig} | |
| import org.apache.kafka.common.serialization.StringDeserializer | |
| import scala.collection.JavaConverters._ | |
| import scala.concurrent.ExecutionContext |
| def consumeFromKafka(topic: String) = { | |
| val props = new Properties() | |
| props.put("bootstrap.servers", "localhost:9094") | |
| props.put("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer") | |
| props.put("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer") | |
| props.put("auto.offset.reset", "latest") | |
| props.put("group.id", "consumer-group") | |
| val consumer: KafkaConsumer[String, String] = new KafkaConsumer[String, String](props) | |
| consumer.subscribe(util.Arrays.asList(topic)) | |
| while (true) { |
| PROMPT='$(kube_ps1) '$PROMPT |
| import sys | |
| import signal | |
| def signal_handler(sig, frame): | |
| print(f"EMERGENCY LANDING BY USER!") | |
| scf.cf.commander.send_setpoint(0, 0, 0, 0) | |
| sys.exit(0) |
| package exampels | |
| import com.datastax.spark.connector.cql.CassandraConnector | |
| import org.apache.spark.{ SparkConf, SparkContext } | |
| import org.apache.spark.sql.cassandra.CassandraSQLContext | |
| import org.apache.spark.SparkContext._ | |
| import com.datastax.spark.connector._ | |
| object CassandraSparkExamples { | |
| // Connection |
| import org.deeplearning4j.nn.conf.NeuralNetConfiguration; | |
| import org.deeplearning4j.nn.conf.layers.RBM; | |
| import org.deeplearning4j.nn.weights.WeightInit; | |
| import org.deeplearning4j.nn.conf.distribution.UniformDistribution; | |
| import org.deeplearning4j.nn.api.OptimizationAlgorithm; | |
| import org.nd4j.linalg.lossfunctions.LossFunctions; | |
| import org.deeplearning4j.nn.conf.`override`.ClassifierOverride; | |
| import org.deeplearning4j.nn.multilayer.MultiLayerNetwork; | |
| import org.deeplearning4j.optimize.listeners.ScoreIterationListener; | |
| import org.deeplearning4j.optimize.api.IterationListener; |
| # Partition the following data into 2 lists: males and females | |
| users = [{ | |
| "name": "user_%s" % x, | |
| "gender": 'f' if x % 3 is random.randint(0,3) else 'm' | |
| } | |
| for x in range(1, 100)] | |
| # Which way do you prefer? | |
| # 1 |