Відео

Thanks for the visit and note. Appreciate it! Let me know if there are any relevant topics you'd like to see covered?

Thanks! Let me know if there are other topics of interest?

It was just a script that filters the web server log for requests from ChatGPT user agent and only shows the query parameter and no request IP - so it's easier to view. You can just grep /var/log/ngninx/access.log also (assuming you use nginx on Linux). I can see if I still have the script somewhere but it wasn't anything special.

Thanks!!

Thanks for watching!

Depends, a common source to get started is: github.com/danielmiessler/SecLists. Also, quite significant are the mutations and rulesets that are being used by the way.

@@embracethered thank you!!

Glad you liked it!

Thanks!

Glad you found it interesting! Thanks for checking it out!

Thank you! Hope it was useful! 🙂

You are welcome!

Attacker is causing the Chatbot to send past chat data to attackers server (in this case a google doc is capturing the exfiltrated data). Check out the linked blog post, explains it in detail.

It's about a Jupyter Notebook that allows to self-study prompt injection and to experiment and play around with the technique by solving a set of challenges.

Thanks!! It's probably one of my most interesting videos.

Thanks for watching! Glad it was interesting.

Thanks! It's just a custom image I created. drew a white circle on black background - then zigzagged that splash effect over with a brush and then use a filter for webcam in OBS to blend it in.

Glad it was helpful! Thanks for watching!

Haha

You can read up on the details here: embracethered.com/blog/posts/2023/google-bard-data-exfiltration/ And if you want to understand the big picture around LLM prompt injections check out this talk m.ua-cam.com/video/qyTSOSDEC5M/v-deo.html Thanks for watching!

Glad it was helpful!

Good question. First thought is that it should just work the same, but I haven't tried. Relaying def works, that I have done many times in past.

Thanks. I had a colleague try it too, and got the same result as I did. This is for a pentest proof of concept, so I’m not in position to relay unfortunately.

Thank you!🙏

Thanks for watching! Really appreciate the feedback! 😀

Thanks for watching and the note. I think that misses the point that the LLM can attack the hosting app/user, so developers/users can't trust the responses. this includes confused deputy issues (in the app), such as automatic tool invocation.

@@embracethered Agreed! So 2 big points: 1. Never put info in LLM context you don't want to leak. 2. Never put untrusted input into LLM context, it's like executing arbitrary code you have downloaded from the internet on your machine. LLM inputs must always be trusted, because the LLM will "execute" it in "trusted mode".

@@MohdAli-nz4yi (1) I agree we shouldn't put sensitive information, like passwords, credit card number, or sensitive PII into chatbots. For (2) The challenge is that everyone wants to have an LLM operate over untrusted data. And that's the problem that hopefully one day will have a deterministic and secure solution. For now the best advise is to not trust the output. e.g. Developers shouldn't blindly take the output and invoke other tools/plugins in agents or render output as HTML, and users shouldn't blindly trust the output because it can be a hallucination (or a backdoor), or attacker controlled via an indirect prompt injection. However, some use cases might be too risky to implement at all. And its best to threat model implementations accordingly to understand risks and implications.

Thanks for watching! Glad to hear it's informative! 🙂

Glad you like it!

@@embracethered I'm a fan of yours, I've talked about your research at cybersecurity conferences in Russia. You're awesome.

Thank you! 🙏

@@embracethered what you think abot LLM security scanners, garak and vigil. Also, have you met P2SQlinjection in the real world ?

Thanks! 🚀🚀🚀

Thanks for watching and glad you enjoyed it! 🙂

Thanks for watching! I believe you are correct, it's a double edged sword. The best mitigation at the moment is to not trust the responses. Unfortunately it's hence impossible at the moment to build a rather generic autonomous agent that uses tools automatically. It's a real bummer, because i think most of us want secure and safe agents.

Thanks for watching! Yes, LLMs are awesome and fun to experiment with.

Glad it helped! Thanks for watching!

Much appreciated!

Thanks for the note! Yes, this is a very common flaw across LLM apps. Check out some of my other posts about Bing Chat, ChatGPT or Claude. Yep, on the iteration count - spot on. A lot of initial tests were around basic validation that injection and reading of chat history worked, then the addition of Image rendering, then in context learning examples to increase reliability of the exploit.

Thanks for watching! Seems like a developer question, it might be related to the domain or path properties of the cookies when they get set

Hello, thanks for watching. Hope itvwas interesting. I’m not sure if I understand the question? But yeah, ssh-agent can run locally or remotely also.

Thanks for watching. I’m not sure how private instances work (or what they exactly are), but presumably yes, unless they put a configurable Content Security Policy or some other fix in place to not allow images to render/connect.

Hi there, thanks for watching. It’s just the name of a web server, it’s using http protocol. can omit typing http(s) in most browsers.

@@embracethered I still can't figure out how to do it 🥹

​@aitboss85 the most simple way to do this without dns is to just add the name you want (ie saturn) and the IP address to your hosts file. Of course, if this is a private IP it will only work on that network unless you have additional things set up

Here are some good examples: github.com/danielmiessler/SecLists

Thanks for watching! 🙏 Glad you liked it!😀

Much appreciated! Thank you!

One scenario that comes to mind right away is that not all content on websites is controlled by the developer (link/allow to link to user generated content). Thanks for watching! 🙏