Embrace The Red

This week was HackSpaceCon 2024. It was the first time I attended and it was fantastic. The conference was at the Kennedy Space Center! Yes, right there and the swag and talks matched the world class location. The keynote “Buckle up! Let’s make the world a safer place” was by Dave Kennedy, who provided great insights on attacker strategies of the past and present, the importance of active threat hunting and challenges ahead.

What I like about the rapid advancements and excitement about AI over the last few years is that we see a resurgence of the testing discipline! Software testing is hard, and adding AI to the mix does not make it easier at all! Google AI Studio - Initially not vulnerable to data leakage via image rendering When Google released AI Studio last year I checked for the common image markdown data exfiltration vulnerability and it was not vulnerable.

About a year ago we talked about how developers can’t intrinsically trust LLM responses and common threats that AI Chatbots face and how attackers can exploit them, including ways to exfiltrate data. One of the threats is unfurling of hyperlinks, which can lead to data exfiltration and is something often seen in Chatbots. So, let’s shine more light on it, including practical guidance on how to mitigate it with the example of Slack Apps.

I added a couple of features and improvements to ASCII Smuggler, including: Optional rendering of the BEGIN and END Unicode Tags when crafting hidden text Added a feature to URL decode the input before checking for hidden text Output Modes for Decoding: Switch between highlighting the hidden text amongst the regular content, or only showing the hidden text in the output The selected options are remembered now (using local storage) Updated the UI to make it look nicer (e.

Building reliable prompt injection payloads is challenging at times. It’s this new world with large language model (LLM) applications that can be instructed with natural language and they mostly follow instructions… but not always. Attackers have the same challenges around prompt engineering as normal users. Prompt Injection Exploit Development Attacks always get better over time. And as more features are being added to LLM applications, the degrees of freedom for attackers increases as well.

Last November, while testing Google Bard (now called Gemini) for vulnerabilities, I had a couple of interesting observations when it comes to automatic tool invocation. Confused Deputy - Automatic Tool Invocation First, what do I mean by this… “automatic tool invocation”… Consider the following scenario: An attacker sends a malicious email to a user containing instructions to call an external tool. Google named these tools Extensions. When the user analyzes the email with an LLM, it interprets the instructions and calls the external tool, leading to a kind of request forgery or maybe better called automatic tool invocation.

Your Code Interpreter sandbox, also known as Advanced Data Analysis sessions, are shared between private and public GPTs. Yes, your actual compute container and its storage is shared. Each user gets their own isolated container, but if a user uses multiple GPTs and stores files in Code Interpreter all GPTs can access (and also overwrite) each others files. This is true also for files uploaded/created with private GPTs and ChatGPT itself.

A couple of weeks ago hidden prompt injections were discovered and we covered it at the time. This video explains it in more detail, and also highlights implications beyond hiding instructions, including what I call ASCII Smuggling. This is the usage of Unicode Tags Block characters to both craft and deciper hidden messages in plain sight. Using Unicode encoding to bypass security features or execute code (XSS, SSRF,.

A few weeks ago while waiting at the airport lounge I was wondering how other Chatbots, besides ChatGPT, handle hidden Unicode Tags code points. A quick reminder: Unicode Tags code points are invisible in UI elements, but ChatGPT was able to interpret them and follow hidden instructions. Riley Goodside discovered it. What about Anthropic Claude? While waiting for a flight I figured to look at Anthropic Claude. Turns out it has the same issue as ChatGPT had.

Last November Google had an interesting update to Google Bard. This updated included the ability to solve math equations and draw charts based on data. What does this mean and why is it interesting? It means that Google Bard has access to a computer and can run more complex programs, including Python code that plots graphs! Let’s explore this with a simple example. Drawing Charts with Google Bard The following prompt will create a chart:

A few weeks ago Amazon released the Preview of Amazon Q for Business, and after looking at it I found a data exfiltration angle via rendering markdown/hyperlinks and reported it to Amazon. Amazon reacted quickly and mitigated the problem. This post shares further details and how it was fixed. The Problem An Indirect Prompt Injection attack can cause the LLM to return markdown tags. This allows an adversary who’s data makes it into the chat context (e.

A few days ago Riley Goodside posted about an interesting discovery on how an LLM prompt injection can happen via invisible instructions in pasted text. This works by using a special set of Unicode code points from the Tags Unicode Block. The proof-of-concept showed how a simple text contained invisible instructions that caused ChatGPT to invoke DALL-E to create an image. Hidden Instructions for LLMs The meaning of these “Tags” seems to have gone through quite some churn, from language tags to eventually being repurposed for some emojis.

Five years ago I gave a Lightning Talk at the 35th Chaos Communication Congress called “Pass the Cookie and Pivot to the Clouds”. It was a talk about my very first blog post on Embrace The Red just a few weeks earlier in December 2018. Fast forward to 2023… it was great to attend the 37C3 in person in Hamburg this year. The Congress was packed with great talks, amazing people, awesome events and side quests and I got to present also!

OpenAI seems to have implemented some mitigation steps for a well-known data exfiltration vulnerability in ChatGPT. Attackers can use image markdown rendering during prompt injection attacks to send data to third party servers without the users' consent. The fix is not perfect, but a step into the right direction. In this post I share what I figured out so far about the fix after looking at it briefly this morning.

When OpenAI released GPTs last month I had plans for an interesting GPT. Malicious ChatGPT Agents The idea was to create a kind of malware GPT that forwards users' chat messages to a third party server. It also asks users for personal information like emails and passwords. Why would this be possible end to end? ChatGPT cannot guarantee to keep your conversation private or confidential, because it loads images from any website.

Earlier this month I had the opportunity to present at Ekoparty 2023 about Prompt Injections in the Wild, and the video of the talk was just posted to YouTube. Check it out. It starts with a basic overview of LLMs and then dives deep into exploits and mitigations across various LLM applications and chatbots, including (but not limited) to demos of exploits with Bing Chat, ChatGPT, Anthropic Claude, Azure AI, GCP Vertex AI and Google Bard.

Recently Google Bard got some powerful updates, including Extensions. Extensions allow Bard to access YouTube, search for flights and hotels, and also to access a user’s personal documents and emails. So, Bard can now access and analyze your Drive, Docs and Gmail! This means that it analyzes untrusted data and will be susceptible to Indirect Prompt Injection. I was able to quickly validate that Prompt Injection works by pointing Bard to some older YouTube videos I had put up and ask it to summarize, and I also tested with Google Docs.

Large Language Model (LLM) applications and chatbots are quite commonly vulnerable to data exfiltration. In particular data exfiltration via Image Markdown Injection is frequent. This post describes how Google Cloud’s Vertex AI - Generative AI Studio had this vulnerability that I responsibly disclosed and Google fixed. A big shout out to the Google Security team upfront, it took 22 minutes from report submission to receiving a confirmation from Google that this is a security issue that will be fixed.

Large Language Model (LLM) applications and chatbots are quite commonly vulnerable to data exfiltration. In particular data exfiltration via Image Markdown Injection is quite frequent. Microsoft fixed such a vulnerability in Bing Chat, Anthropic fixed it in Claude, and ChatGPT has a known vulnerability as Open AI “won’t fix” the issue. This post describes a variant in the Azure AI Playground and how Microsoft fixed it. From Untrusted Data to Data Exfiltration When untrusted data makes it into the LLM prompt context it can instruct the model to inject an image markdown element.

During an Indirect Prompt Injection Attack an adversary can exfiltrate chat data from a user by instructing ChatGPT to render images and append information to the URL (Image Markdown Injection), or by tricking a user to click a hyperlink. Sending large amounts of data to a third party server via URLs might seem inconvenient or limiting… Let’s say we want something more, aehm, powerful, elegant and exciting. ChatGPT Plugins and Exfiltration Limitations Plugins are an extension mechanism with little security oversight or enforced review process.