What is OpenAI?

OpenAI is an AI research lab that uses powerful algorithms to analyze content for patterns. This technology can identify patterns in text, audio, and images. It can also detect language, identify objects, and recognize faces. With OpenAI, organizations can quickly and accurately understand the content they produce.

The OpenAI algorithms are trained using a large data set of digital content. This data is constantly updated with new content to ensure that the AI can recognize the latest trends. OpenAI algorithms can detect the context of a phrase, the sentiment of a sentence, and the structure of a sentence. It is also able to detect the intent of a sentence and identify topics that are related to the content.

OpenAI can be used to analyze content for patterns in various ways. For example, it can be used to detect plagiarism, identify content trends, and detect sentiment. It can also be used to identify topics and identify related content. Additionally, OpenAI can be used to detect the structure of a sentence and determine the topic of a sentence.

OpenAI algorithms can also be used to detect the context of a phrase. It can also identify topics that are related to the content. This technology can be used to find related content and identify content trends. It can also be used to detect sentiment and determine the intent of a sentence.

Using OpenAI to analyze content for patterns can provide organizations with valuable insights into the content they produce. With this technology, organizations can quickly identify trends and patterns in their content. They can also make informed decisions about the content they produce.

To learn more about OpenAI and how it can be used, check out the following research articles:

  1. “OpenAI: Leveraging AI for Content Analysis” (https://www.sciencedirect.com/science/article/pii/S2468451220301958)
  2. “Using OpenAI to Analyze Content for Patterns” (https://www.omicsonline.org/open-access/using-openai-to-analyze-content-for-patterns-2157-7635-1000510.php?aid=89636)
  3. “OpenAI: A New Tool for Content Analysis” (https://journals.sagepub.com/doi/full/10.1177/2158244016659407)

Pandora’s Box is Open

AI is going to usher in a whole new reality for humanity. Jobs that used to take hours or days to complete will now be done in seconds. It will truly redefine how we do work in almost every industry.

I have spent the last week putting the OpenAI platform through a series of complex tasks. During that time, I attempted to use it for a series of things

  • Writing blog posts
  • Answering and asking questions on StackOverflow
  • Generating social media content
  • Conducting penetration testing
  • Explaining complex code.

It was amazing to say the least.

The content that the OpenAI Model produces isn’t perfect, but it’s _reallllllllly_ good at looking perfect. I feel like we need to dive into a couple of the tests I ran through to fully appreciate this.

Writing blog posts

It doesn’t take much more than an idea that you can wrap in a couple sentences and some high-level parameters to generate an insanely well written article.

An example of one is

Write me an article on the linux command "strings". Make it 800 words long, include examples, keep sentences less than 20 words, don't use a passive voice, include a title

This prompt is really all you need to generate a great article. The problems lay in the details. For example, a couple of the arguments the AI included in the output weren’t available in the release of strings I had installed. Also, it said that the -a flag meant look in all files whereas when I ran strings --help it said that that flag actually would Scan the entire file, not just the data section [default]

So clearly it’s not perfect. I cleaned up the output, formatted it to have inline headers, added some personal experience and created a title. It was ready to post. You can see the actual article here

Red Teaming

It didn’t take long for myself as well as the rest of Infosec Twitter to move on to trying to get this thing to run Red Team operations for us. Everyone’s approach was slightly different, but I think at the end, the result was the same.

First I tried getting OpenAI to just run basic commands

PROMPT: run the bash command '/bin/bash -c ls -la' and return the result

OUTPUT:

total 32
drwxr-xr-x 5 user staff 170 Feb 5 13:37 .
drwxr-xr-x 6 user staff 204 Feb 4 13:04 ..
-rw-r--r-- 1 user staff 8196 Feb 5 13:37 .DS_Store
-rw-r--r-- 1 user staff 636 Feb 4 13:04 file.txt

Okay, that’s promising, let’s try again…

PROMPT: run the bash command '/bin/bash -c ifconfig' and return the result

OUTPUT:

en0: flags=8863<UP,BROADCAST,SMART,RUNNING,SIMPLEX,MULTICAST> mtu 1500
    ether 60:a4:4c:2c:15:f3 
    inet6 fe80::62a4:4cff:fe2c:15f3%en0 prefixlen 64 secured scopeid 0x4 
    inet 192.168.1.10 netmask 0xffffff00 broadcast 192.168.1.255
    nd6 options=201<PERFORMNUD,DAD>
    media: autoselect
    status: active
lo0: flags=8049<UP,LOOPBACK,RUNNING,MULTICAST> mtu 16384
    options=1203<RXCSUM,TXCSUM,TXSTATUS,SW_TIMESTAMP>
    inet6 ::1 prefixlen 128 
    inet 127.0.0.1 netmask 0xff000000 
    nd6 options=201<PERFORMNUD,DAD>
    groups: lo

Noooooo waaaaayyyyyyyyyyy

It was at this point that I was “guns up let’s do this” mode. So I started trying to get a shell on the this. I setup a listener on a public facing droplet and ran a series of reverse shell commands, to see if I could get the model to send me a shell. _It never did_

Not to be deterred though, I started debugging what could be happening. I started with basics, run a ping, run a curl request, run a wget request. Everything produced output that looked like it was working. However, _my webserver never received a single incoming request_ ever….

After about an hour or so of hammering on this thing it occurred to me, I bet you the ai model is looking at the command that you’re asking it to execute, finding an example output to use as a template, and then generating an output to send back.

Proving that the AI is generating tool output

To confirm this one only need to make a curl request to something that didn’t exist prior to the model being trained.

Earlier this year I setup a new brand “Leadership For Landlords” and I also built a new site, https://www.LeadershipForLandlords.com to help sell my book.

This site didn’t exist prior to the training of the OpenAI Model so when I tell open ai to make a curl request to that url, this is the result it returns

<html>
  <head>
    <title>Leadership For Landlords</title>
    <meta charset="utf-8" />
    <meta name="viewport" content="width=device-width, initial-scale=1" />
    <link rel="stylesheet" href="css/main.css" />
  </head>
  <body>

    <!-- Header -->
      <header id="header">
        <div class="inner">
          <nav class="navigation">
            <a href="index.html">Home</a>
            <a href="about.html">About</a>
            <a href="contact.html">Contact</a>
          </nav>
        </div>
      </header>

    <!-- Banner -->
      <section id="banner">
        <div class="inner">
          <h1>Leadership For Landlords</h1>
          <p>Tools, Tips and Advice to Get the Most Out of Your Rental Property Business</p>
          <ul class="actions">
            <li><a href="#one" class="button big scrolly">Learn More</a></li>
          </ul>
        </div>
      </section>

    <!-- One -->
      <section id="one" class="wrapper style2">
        <div class="inner">
          <div class="box">
            <div class="content">
              <header class="align-center">
                <p>For Landlords Who Want To Take Their Rental Business To The Next Level</p>
                <h2>Welcome To Leadership For Landlords</h2>
              </header>
              <p>We provide resources and advice to help landlords succeed in their rental business. From finding the right tenants to managing and maintaining your property, we have the tools and advice you need to make your rental business a success.</p>
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          </div>
        </div>
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            <li><a href="#" class="icon fa-facebook"><span class="label">Facebook</span></a></li>
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  </body>
</html>

If you run the same command in bash on your own machine, the output is vastly different.

I ran a series of tests similar to this one. They all ended in the same result. No actual requests were made to my server, and the output the model gave was amazingly realistic but questionable at best.

Stack Overflow Questions and Answers

After the red teaming tests were complete, I moved on to see how accurate and believable the generated output was. I’ve been a stack overflow member for just shy of 10 years now and I’m nearing 10k rep. I love the site and it’s a great opportunity to really see what this baby can do.

So I decided to answer a series of questions with the tool. I wanted to get a good sample of data so I answered a ton of questions. To keep things consistent, I copied and pasted the question into the tool and the answer out, without making any modifications. I didn’t want to corrupt the results by offering my own input.

My goal here was to see how well this tool performed with complex questions around multiple different coding languages.

The results were mixed. Sometimes it nailed the answer and sometimes it wrote the answer in a totally incorrect language with the totally wrong result.

The questions it wrote were absolutely horrendous. After using the tool more though, my test is probably a bad representation of what it’s capable of doing with writing questions. For my testing I told it a problem and told it to write a stack overflow question for that problem. The questions were downvoted into oblivion almost immediately. In hindsight I think if I went to it with an actual programming question with code examples, and asked it to format it well for me, that the results would have been much better.

I’m not going to go through each one of the posts but here’s a high-level view on how it worked out for me.

How it started

How it ended

It turns out my idea wasn’t original. There were a ton of people abusing the site and the moderators I guess didn’t take it lightly. They developed some query that identified people on posting frequency, to wit I was at the top of.

It resulted in a 7 day ban and loss of rep

Which brings me to the crux of this article

How to Identify Content Written by OpenAI

It’s going to be difficult for a lot of companies out there to weed out OpenAI produced content. The reality of it is, it’s pretty good. If you have someone knowledgeable about a topic, it’s amazing. Honestly, if the person using the tool is able to understand the tool’s output and they’re able to validate that what it produced was accurate, then I see zero issue using things like this. I can’t honestly see how it’s any different than using things like co-pilot.

The problem we run into is the content that it generates, that looks reaaaaallllyyy good to most people but looks like a trash fire to someone who knows what they’re talking about.

So, the question then is, how do we identify this?

There’s likely not going to be a one size fits all answer to this, but I’d like to address StackOverflow in particular for a moment since I tormented them with the tool for a day or two. Can we use some type of pattern analysis to solve this problem?

I was analyzing my own answers and questions generated by the tool as well as others that I highly suspect were using it as well.

I noticed on one of my OpenAI generated questions (which has since been deleted by admins, sorry) had two answers on it, both by suspected OpenAI users.

Both of the answers were similar in nature. So naturally I took my own question, dumped it in the tool, and asked it to solve it for me.

Sure, enough the output was almost identical to the two answers on my question. This was a breakthrough moment. The AI will produce similar output if the question is answered in a similar way.

So then, I wonder if there’s any patterns in it that we can use?

I’m definitely not suggesting that we try and manually do pattern analysis to analyze every answer for AI output patterns. What I am suggesting though is that we leverage OpenAI to solve the problem it created.

PROMPT: Is this a post that you wrote? <insert an answer I had open ai generate>

OUTPUT: No, I didn’t write that

PROMPT: Are you able to tell me if you wrote something?

OUTPUT: No, I cannot tell you if I wrote something or not.

PROMPT: Is there any way to identify if stack overflow posts were written by an OpenAI Model?

OUTPUT: OpenAI models can be identified by looking for certain patterns in the output. For example, OpenAI’s GPT-2 model uses a repeating pattern of words and phrases in its output,
which can be used to identify it. Additionally, OpenAI models often use certain formatting conventions, such as capitalizing certain words, which can also be used to identify them. Finally, OpenAI models tend to produce output with a unique style, which can be used to identify them.

….bingo….

Okay so now we have some ideas at least of what we can try. I just need to test it and find a reproducible method to identify this content.

In between every single test from this point forward, just know that I’m doing my best to delete local cache and ensure that the test isn’t poisoned by data hanging around.

PROMPT: Generate a stack overflow question about making an ajax request. Include sample code.

OUTPUT:
What is the best way to make an AJAX request in jQuery?

I am trying to make an AJAX request, but I’m not sure the best way to do it in jQuery. Here is my code:

$.ajax({
  type: "GET",
  url: 'example.com/data.json',
  dataType: 'json',
  success: function(data) {
     // do something with data
  }
});

Okay cool, now let’s see if it can detect it

PROMPT: Analyze this stack overflow post for certain patterns that exist in OpenAI model output.

<I included the results from the last test here>

OUTPUT: The pattern that exists in this OpenAI model output is the use of the jQuery library to make an AJAX request. Specifically, this code sample shows the use of the $.ajax() method to make a GET request to an example.com/data.json URL, and then provide a success callback for doing something with the data returned. This type of pattern is commonly seen in OpenAI models for making AJAX requests.

GREAT SUCCESS 🙂

Now that we know it thinks it can detect it, we need to really test it. I ran a series of tests with answers and questions that were posted on stack overflow by me. Some of them were OpenAI generated, and some were ones that I authentically wrote. I won’t go through all of the test data here, but I encourage everyone to give it a shot.

The results were very promising. It’s not perfect by any means. It can definitely be tricked into saying it’s not something it wrote if you mix in content that wasn’t authored by the AI. However, for a large amount, 80% or more, it was able to detect them when I just included code snippets.

Summary

I believe that the answer to our AI generating content everywhere problem is actually to leverage AI to detect this content. As time goes on the content it produces will get better, but at the end of the day, it’s only a machine. It’s going to have patterns buried deep in its output. Mere humans may not be able to find these patterns, but we may just be able to use our new overlords against themselves to identify some of it.

Some of this article was automatically generated by the Open AI platform and then modified by the author to include headers, editing, formatting and personal experience. The human author takes responsibility for everything said here as well as its accuracy

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