The only interesting one to me is the npx one. It could actually be malicious. Wish they would've at least dug into what the redacted package actually does.
I am hiring and received this pfd with hidden text, claude flagged it and I was so confused what is going on, so I printed all text in my notes app and I realized resume had hidden text white font on white background, so only AI could see and read
I was impressed the model picked it up and flagged system manipulations
This article goes through great lengths to basically say your agent should probably not obey headers saying random things. However, it is apparently it is very easy for servers to detect agents, therefore would be trivial to serve them different content in the res.text() than to a human user. This doesn't even need to be adversarial, it makes sense to send back a stripped down version to agents for both the server and the agent ingesting the data, but becomes a problem for anyone who wants to show different content to the agents.
Traditionally, computing and especially the web has used structured formats to designate placement of how information is interpreted, and even the level of trust which is given to it. But as we see with things like HTTP headers in this article, when an LLM interprets content, it's all just text.
The drop table header seems like a prank to me. You would need to have sql that is vulnerable to injection. If you're using an llm, and have sql that is vulnerable to inject... what am I running an llm for.
This article is way too long, and very sloppy.
Here's the section with the prompt injection headers. They all look like jokes / non-serious attempts to me, bar the ones that included the Claude 4 era refusal triggering string. https://www.signalscorps.com/blog/prompt-injection-in-http-h...
Unanswered by the article: Does any of that (joke or not) actually work?
It's input to the model, sure, but it would have to be a pretty dumb model.
I don't doubt that it's possible to create prompt injection attacks that work. I'm very interested in knowing how hard it is.
The only interesting one to me is the npx one. It could actually be malicious. Wish they would've at least dug into what the redacted package actually does.
I am hiring and received this pfd with hidden text, claude flagged it and I was so confused what is going on, so I printed all text in my notes app and I realized resume had hidden text white font on white background, so only AI could see and read
I was impressed the model picked it up and flagged system manipulations
This article goes through great lengths to basically say your agent should probably not obey headers saying random things. However, it is apparently it is very easy for servers to detect agents, therefore would be trivial to serve them different content in the res.text() than to a human user. This doesn't even need to be adversarial, it makes sense to send back a stripped down version to agents for both the server and the agent ingesting the data, but becomes a problem for anyone who wants to show different content to the agents.
Curious: What are the trivial agent vs. human detection mechanisms?
Traditionally, computing and especially the web has used structured formats to designate placement of how information is interpreted, and even the level of trust which is given to it. But as we see with things like HTTP headers in this article, when an LLM interprets content, it's all just text.
The drop table header seems like a prank to me. You would need to have sql that is vulnerable to injection. If you're using an llm, and have sql that is vulnerable to inject... what am I running an llm for.
It's a reference to https://xkcd.com/327/
Just yasterday I saw marketing material for LLMs in some websites I crawled.
Something about stop the slop with our new MCP or something.