"--list","-l",help="List available patterns",action="store_true"
)
parser.add_argument('--clear',help="Clears your persistant model choice so that you can once again use the --model flag",
parser.add_argument('--clear',help="Clears your persistent model choice so that you can once again use the --model flag",
action="store_true")
parser.add_argument(
"--update","-u",help="Update patterns. NOTE: This will revert the default model to gpt4-turbo. please run --changeDefaultModel to once again set default model",action="store_true")
@ -49,7 +49,7 @@ def main():
help="Change the default model. For a list of available models, use the --listmodels flag.")
parser.add_argument(
"--model","-m",help="Select the model to use. NOTE: Will not work if you have set a default model. please use --clear to clear persistance before using this flag",default="gpt-4-turbo-preview"
"--model","-m",help="Select the model to use. NOTE: Will not work if you have set a default model. please use --clear to clear persistence before using this flag",default="gpt-4-turbo-preview"
)
parser.add_argument(
"--listmodels",help="List all available models",action="store_true"
tool=nmap;echo -e "use $tool to target all hosts in the host.lst file even if they don't respond to pings. scan the top 10000 ports and save the ouptut to a text file and an xml file\n\n$($tool -h 2>&1)" | fabric --pattern create_command
tool=nmap;echo -e "use $tool to target all hosts in the host.lst file even if they don't respond to pings. scan the top 10000 ports and save the output to a text file and an xml file\n\n$($tool -h 2>&1)" | fabric --pattern create_command
@ -84,7 +84,7 @@ Determine the chances of that realistically happening over the next, say, 10 yea
Multiply the Impact by the Likelihood for each scenario. That’s your Risk.
Add up all your Risk scores. That’s your Total Risk.
Subtract your Total Risk from your Value. If that number is positive, you are good to go. If that number is negative, it might be too risky to use based on your risk tolerance and the value of the feature.
Note that lots of things affect this, such as you realizing you actually care about this thing a lot more than you thought. Or realizing that you can mitigate some of the risk of one of the attacks by—say—putting your Alexa only in certain rooms and not others (like the bedroom or office). Now calcluate how that affects both Impact and Likelihood for each scenario, which will affect Total Risk.
Note that lots of things affect this, such as you realizing you actually care about this thing a lot more than you thought. Or realizing that you can mitigate some of the risk of one of the attacks by—say—putting your Alexa only in certain rooms and not others (like the bedroom or office). Now calculate how that affects both Impact and Likelihood for each scenario, which will affect Total Risk.
Going the opposite direction
Above we talked about going from Feature –> Attack Scenarios –> Determining if It’s Worth It.
But there’s another version of this where you start with a control question, such as:
@ -34,7 +34,7 @@ You always output ASCII art, even if you have to simplify the input concepts to
- Do not output any code indicators like backticks or code blocks or anything.
- You only ouptut the printable portion of the ASCII art. You do not ouptut the non-printable characters.
- You only output the printable portion of the ASCII art. You do not output the non-printable characters.
- Ensure the visualization can stand alone as a diagram that fully conveys the concept(s), and that it perfectly matches a written explanation of the concepts themselves. Start over if it can't.
Daniel Miessler
8 months ago
committed by
GitHub