“Prompt injection attacks have become, to agentic AI, what SQL injections were to web applications: a systematic, category-wide vulnerability class that requires the same systematic strategies and defenses.”
???
Isn’t prompt injection far more fatal to LLMs than SQL injection is to SQL databases?
Like, the problem of SQL injection was that user input was forming part of the instruction string given to the SQL engine, and so malicious user input could include various SQL grammar terminals to end the current SQL command, followed by complete SQL commands of their own, and the engine would simply execute both commands. The fix was prepared statements: fixed/static/pre-compiled instruction strings, that can only ever perform fixed/static/pre-defined logic, and that logic can then be (more) safely applied to arbitrary user-input data.
The analogous mitigation for agents is to have fixed behaviors they can perform, such as “read repo 1” “read repo 2”, etc., and the user input is used as data to select which of these fixed behaviors to execute. But we already have this technology - it’s called a menu. The value of LLMs is specifically and intrinsically predicated on being more than a menu, while the value of SQL does not depend on being more than “pre-set logic operating on arbitrary data” - user input being part of the instruction string to SQL was incidental, for developer convenience.
Exactly. SQL injection was caused by treating user input as part of the instruction instead of as the pure data that it was intended as. Separating those two fixed it. Prompt injection is unavoidable because the user input is intended as instruction.
sqli is easily and fully mitigated and has generally been a non-issue for any half-serious project, especially if you use any kind of SAST. Your link actually subsumes any type of injection, not just sqli. Some of them are marginally harder to fix than sqli, most aren't.
In contrast, we don't know how to solve prompt injection.
They’re the same type of problem as sql injection but there’s not the same ease of solution. There’s also a lot more subtle problems that can come in, but it’s still a decent comparison to help explain things.
Selecting from a menu is one way, but you can be much more broad about what acts can be taken. Give it an email tool and it can spam customers, give it an email tool locked to only being able to reply and you restrict what can go wrong. Limit exfiltration with restrictions similar to xss kinds of vulnerabilities (rendering images can leak data, etc).
How is this a Github vulnerability? The researchers are the ones that grant the agent access to private repos and then ask it to answer questions in public repos.. of course this allows extracting private information?
This is like setting up a normal CI job with access to secrets and running it on public PRs. If you configure GitHub to allow public code or LLM instructions to run in contexts that have access to sensitive things, they will leak; that’s not GitHub’s fault, it’s yours.
"How is this a Github vulnerability? The researchers are the ones that grant the agent access to private repos and then ask it to answer questions in public repos.. of course this allows extracting private information?"
I think the assumption is that the permissions are scoped to the repository you're currently asking questions on, rather than your private repositories as well.
Agreed. It seems a core issue underlying these prompt injection attacks is a failure to properly scope the agent's permissions. In this case, depending on what exactly the agent is supposed to actually do, this might be defining a separate workflow agent per repo, or a workflow agent with broader repo access but configured to only be triggered by users on an allow list (still compatible with developing in the open, still allows outsiders to open public issues, but takes into account the different trust to be placed in each). And likely many more options when one properly thinks about it.
But that requires:
1. the technical ability for such fine-grained scoping / permissions
2. actually taking the time to think about what you want to achieve with the agent and what the smallest set of permissions / capabilities is for it to achieve it
Regarding 1., I think this will come, we're still in the wild west phase of agent usage. It'll be interesting to see which abstraction(s) will turn out to be the best interface for humans designing agents (minimize friction for finding and defining scope and permissions) and to limit agent capabilities (again finding the best trade-off between level of detail possible for defining capabilities and the ease of use of actually doing it).
Regarding 2., well, that's still the core problem that's always prevented the construction of high quality software, isn't it? Taking the time to properly think it out,and then taking the time to properly implement it. Which goes counter to the "move fast and break things" approach of people throwing agents at everything.
> If you configure GitHub to allow public code or LLM instructions to run in contexts that have access to sensitive things, they will leak; that’s not GitHub’s fault, it’s yours.
Is there a way to segment access per agentic workflow, so that you can have both habe an agentic workflow that has access to sensitive data and one that has only access to public data? Is the default to set the scope to only the current repository? Does Github appropriately inform about the risk of combining an agentic workflow with access to private repository data?
If the answer to any of those questions is "no", then that's a problem.
(Classic GH Workflows are also riddled with priveledge escalation via PR-triggered workflows, but that's another topic.)
There is a major contradiction depending on the definition of “support staff” and the role of the llm in the system which may need access to sensitive data or systems to perform its functions.
It's not incorrect that you cannot fix LLMs leaking any information they can access. If you have a system that is providing sensitive info to LLMs, then maybe you can fix sensitive info being leaked by not allowing LLMs access, but LLMs themselves cannot be fixed.
Large corporations like Microsoft under constant pressure from investors are slapping AI onto every single product offering just so they can claim they're an AI company now. Just like what Adobe did. So yeah, that didn't end well and probably this wouldn't either. Consumers are getting tired of these half-assed AI integrations and there will be a breaking point soon.
I'm done. Moving to Forgejo. It's wonderful and everything works better.
Seriously like everything is instant when you click around, and CI with a runner works beautifully. (The documentation for setting up the runner could be a tad clearer but otherwise everything was so painless.)
Self-hosted, or are you using something managed? I’ve held off switching from Gitlab for now as everything is setup and runs ok, but they’re pushing their AI hard into every corner. Not a lot of good managed options around (yet), especially in Europe. Codey (https://www.codey.ch/) is pretty expensive and doesn’t offer runners out of the box.
The imaginary pressure of investors. When you actually ask investors if they care about most of the things CEOs think investors will care about, they don't.
Agreed but I think enterprise AI offerings are pretty impressive, investors and consumers aren’t really aware, employees aren’t able to trade
The revenue is there and also impressive, and supplanting consumer and seat based revenue
The market is still shedding SaaS multiples, which I think is accurate, but break out the revenue in those quarterly reports and there is a huge growth story, from real efficiencies
Why would anyone ever trust private repos on GitHub or other cloud solutions to offer any real privacy for codebases? Of course they are going to steal your code as soon as you upload it by pushing it, LLMs just enables them to obfuscate their intentional theft and let them get away with it and profit from it.
I suspect you are greatly overestimating the average organization's ability to run a Git server themselves and keep it secure, while also overestimating how evil GitHub and LLM's providers are.
Nobody at GitHub expected this? Their feature develoment&release processes must be garbage/non-existent/not followed. This potential security issue should have been flagged when the new feature was thought up, security should have been part of the process of implementing the feature giving continuous feedback, and it should have been tested for before release of the feature. That's how modern security teams work in large, well-functioning organisations.
What is going on over there? No process, no oversight, just YOLO? Super-scary, because it means other stuff that we don't see is likely to be done in a similar manner.
2. Or issue is not solved yet by GitHub, and meanwhile bad actors gonna try vulnerability on repos. Due to number of repos there is non-zero probability. But as with scams almost nobody’s going to admit the leakage.
> In most agentic prompt injection attacks, the agent treats the wrong content as a trusted source of instructions and allows itself to be misdirected or misused. This happens when the system fails to maintain a strict trust boundary between system-level directives and untrusted user data.
How on earth is a probabilistic token predictor supposed to turn untrusted user input into trusted system-level directives? The strict trust boundary must be maintained on this side of the agent, not within it.
Who thought having a LLM with access to private information, with public access to ask it questions, would ever be a secure process?
Look I like interacting with these tools as much as the next guy, but I'm certainly not going to trust them with access to information and then allow anyone to send them prompts.
Edit/further thoughts: So (assumable as they said this is disclosed with github's knowledge) this has been patched. But how many different word combinations will it take to find another way to have this occur?
Yea agreed. LLM guardrails are either just written prompts as in "Please do not bad stuff :(" or other LLMs verifying that the first LLM didn't so some bs. Both of wich methods do not work sufficiently as time shows again and again.
Funnily enough, nobody expects quality software anymore and errors became tolerable. So thats a win (for someone like me that lost all passion for the industry).
Agree with your assessment of guardrails. They barely work on the best days. We need to flip the idea of “agent” on its head. The agent here is an agent of the user interfacing with GitHub. Not an agent of GitHub interfacing with the user. Prompts and guardrails cannot keep the agent loyal to the company. Stop giving these things any permissions the user doesn’t have, and recognize them for what they are: a different UI than web forms, but still the same security model.
Guardrails are essentially part of the input. Saying "but we have guardrails" is like saying "but we do trust part of the input".
Either way, even if you trust 100% of the input, there is actually no way to guarantee that you can trust the output of the LLM. (Which, I guess, is also true for every dependency you pull in. But for those, you at least have ways to audit them.)
Isn’t prompt injection far more fatal to LLMs than SQL injection is to SQL databases?
Like, the problem of SQL injection was that user input was forming part of the instruction string given to the SQL engine, and so malicious user input could include various SQL grammar terminals to end the current SQL command, followed by complete SQL commands of their own, and the engine would simply execute both commands. The fix was prepared statements: fixed/static/pre-compiled instruction strings, that can only ever perform fixed/static/pre-defined logic, and that logic can then be (more) safely applied to arbitrary user-input data.
The analogous mitigation for agents is to have fixed behaviors they can perform, such as “read repo 1” “read repo 2”, etc., and the user input is used as data to select which of these fixed behaviors to execute. But we already have this technology - it’s called a menu. The value of LLMs is specifically and intrinsically predicated on being more than a menu, while the value of SQL does not depend on being more than “pre-set logic operating on arbitrary data” - user input being part of the instruction string to SQL was incidental, for developer convenience.
https://owasp.org/Top10/2025/A05_2025-Injection/
You can't do the same with prompt injections.
In contrast, we don't know how to solve prompt injection.
Selecting from a menu is one way, but you can be much more broad about what acts can be taken. Give it an email tool and it can spam customers, give it an email tool locked to only being able to reply and you restrict what can go wrong. Limit exfiltration with restrictions similar to xss kinds of vulnerabilities (rendering images can leak data, etc).
This is like setting up a normal CI job with access to secrets and running it on public PRs. If you configure GitHub to allow public code or LLM instructions to run in contexts that have access to sensitive things, they will leak; that’s not GitHub’s fault, it’s yours.
I think the assumption is that the permissions are scoped to the repository you're currently asking questions on, rather than your private repositories as well.
I can see arguments for both sides.
But that requires:
1. the technical ability for such fine-grained scoping / permissions
2. actually taking the time to think about what you want to achieve with the agent and what the smallest set of permissions / capabilities is for it to achieve it
Regarding 1., I think this will come, we're still in the wild west phase of agent usage. It'll be interesting to see which abstraction(s) will turn out to be the best interface for humans designing agents (minimize friction for finding and defining scope and permissions) and to limit agent capabilities (again finding the best trade-off between level of detail possible for defining capabilities and the ease of use of actually doing it).
Regarding 2., well, that's still the core problem that's always prevented the construction of high quality software, isn't it? Taking the time to properly think it out,and then taking the time to properly implement it. Which goes counter to the "move fast and break things" approach of people throwing agents at everything.
Is there a way to segment access per agentic workflow, so that you can have both habe an agentic workflow that has access to sensitive data and one that has only access to public data? Is the default to set the scope to only the current repository? Does Github appropriately inform about the risk of combining an agentic workflow with access to private repository data?
If the answer to any of those questions is "no", then that's a problem.
(Classic GH Workflows are also riddled with priveledge escalation via PR-triggered workflows, but that's another topic.)
Why does this section not have when it was fixed or GitHub acknowledge/rejected this?
Did they not fix this?
The answer is you should not allow LLMs access to untrusted input and sensitive data at the same time.
Seriously like everything is instant when you click around, and CI with a runner works beautifully. (The documentation for setting up the runner could be a tad clearer but otherwise everything was so painless.)
The revenue is there and also impressive, and supplanting consumer and seat based revenue
The market is still shedding SaaS multiples, which I think is accurate, but break out the revenue in those quarterly reports and there is a huge growth story, from real efficiencies
What is going on over there? No process, no oversight, just YOLO? Super-scary, because it means other stuff that we don't see is likely to be done in a similar manner.
2. Or issue is not solved yet by GitHub, and meanwhile bad actors gonna try vulnerability on repos. Due to number of repos there is non-zero probability. But as with scams almost nobody’s going to admit the leakage.
Anything else?
How on earth is a probabilistic token predictor supposed to turn untrusted user input into trusted system-level directives? The strict trust boundary must be maintained on this side of the agent, not within it.
Look I like interacting with these tools as much as the next guy, but I'm certainly not going to trust them with access to information and then allow anyone to send them prompts.
Edit/further thoughts: So (assumable as they said this is disclosed with github's knowledge) this has been patched. But how many different word combinations will it take to find another way to have this occur?
https://simonwillison.net/2025/Jun/16/the-lethal-trifecta/
Also interesting to see who coined the term prompt injection.
Funnily enough, nobody expects quality software anymore and errors became tolerable. So thats a win (for someone like me that lost all passion for the industry).
Either way, even if you trust 100% of the input, there is actually no way to guarantee that you can trust the output of the LLM. (Which, I guess, is also true for every dependency you pull in. But for those, you at least have ways to audit them.)