Your Open Source Model Could Have a Hidden Time-Release Backdoor

lllmbababoom about 5 hours ago 66 commentsRead Article on morgin.ai

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zarzavat•about 3 hours ago

Yes your Chinese open model could have a time-release backdoor, just as your Chinese vibrator could have a hidden microphone that records everything you say and transmits it to the CCP. But does it? No.

What's much more likely is that your US AI provider is promising not to train on your data but is doing so anyway. With a self-hosted model you can at least avoid that.

skeledrew•about 1 hour ago

> your Chinese vibrator could have a hidden microphone that records everything you say and transmits it to the CCP

Could they be that desperate? Wow.

Barbing•about 3 hours ago

Always assume someone’s training on ya (maybe not to point of losing competitive advantage, but on a personal level).

Airgap your models from Country A, review outputs with model from Country B?

nerdsniper•about 3 hours ago

They’re talking about open-weight models you host yourself. Which are fundamentally incapable of spying on you unless you give them network access. We also haven’t seen any evidence of open-weight models exfiltrating data over side channels (DNS/steganography/etc) yet, so at this time internet access still seems safe enough to grant without worrying about spying.

dvh•about 2 hours ago

I can imagine scenario. Let's say you tell open-weight model to generate kitten video. It uses piezoelectric effect of some ceramic capacitor on gpu as microphone and adds recorded audio to the generated video in non audible range. You post kitten video on social media where ai company collects it and extract what you were saying while the video was being generated.

Icy-Undivided-G•about 3 hours ago

my first thought reading the article wasn't that Qwen, or some other chinese open model, would do this, but rather that one of the many "unlocked" models on huggingface might

embedding-shape•about 3 hours ago

But but but, they're called .safetensors, surely they're safe then and could impossibly lead to me being hacked?

The ecosystem is due for a rude awakening any day now. I've been slowly prepping by isolating absolutely everything on my machine from each other, not sure how people dare to YOLO run these sort of things "natively" on their computer today.

dannyw•about 2 hours ago

> What's much more likely is that your US AI provider is promising not to train on your data but is doing so anyway.

There's genuinely no evidence of this for OpenAI and Anthropic. It's impossible to disprove, but I don't think it's likely because:

* They get enough volume from consumer subs with data training enabled anyway.

* If this was happening, it needs serious work at the scale OpenAI and Anthroppic, from data pipelines, to ablation experiments, to the actual data mix and traces going in all the telemetry/diagnosis of large-scale training runs.

* It would need to involve a team. Employees at these companies leave, there have been numerous whistleblowers, allegations, etc. Nothing on this front that I can find.

* It would damage enterprise trust permanently and be a company and reputation-ending thing. Now that these tools are used by everyone from state governments to the DoW, the exposure radius is massive, investors (many of whom are customers/users too; and often have their stakes in not just a single company but multiple) would not be happy. Piss off enough powerful people, and anyone can join Sam Bankman-Fried in prison.

* There's a myriad of enterprise customers and bespoke contracts. I can't get into details, but not all enterprises accept a 'trust me bro' clause.

dgellow•about 2 hours ago

> * It would need to involve a team. Employees at these companies leave, there have been numerous whistleblowers, allegations, etc. Nothing on this front that I can find.

You have to also note the incentives going on here. Employees who have extremely valuable stock options, they have good reasons to not come public with anything that would damage the reputation of the company before they can cash out

jjav•about 2 hours ago

> It's impossible to disprove, but I don't think it's likely

If something is impossible to disprove, we must assume it is happening from a threat modeling perspective.

zarzavat•about 2 hours ago

I'm not saying that Anthropic and OpenAI are definitely dishonest, there is no proof of that. However, when you have

1) an extremely high financial incentive to be dishonest (trillions of dollars),

2) low-ish chance of being caught, especially if you launder the data through another model to remove identifying information,

3) the people in charge of said operations are generally agreed to be snakes,

then it should at least arouse suspicion. You may be right that get enough data from opt-ins that they don't need to do it. I do believe that they don't violate enterprise ZDR agreements, but for normal subscriptions I'm much less confident.

gastonmorixe•about 4 hours ago

Like any other software or dependency. Open or close.

Sleeper agents are a big unresolved issue in LLMs but we’ll have to deal with it like we’ve been fighting bad actors for ages.

Also, saying that “open source models” may be the problem is incorrect. What makes this an issue of open source only? Nothing in my mind prevents a frontier lab model going rogue. In fact we have more proof of their bad behavior (Claude code harness a while ago) than from open source (yet).

It’s inherently a limitation of the model which you don’t have the full training set, which includes most of the models. Closed or open don’t matter.

chrismorgan•about 3 hours ago

No, only like any other closed source software or dependency. It’s at the very least harder to hide things like that if all the source is available.

The title here is a misnomer; none of these models are open source, they’re only what is most commonly called open weights (though I’m still not comfortable with the word “open” in there, rather than “free” like in “freeware”).

thesz•about 3 hours ago

  > It’s at the very least harder to hide things like that if all the source is available.

In the training of LLMs, the order of batches and their content can introduce adversarial behavior [1].

[1] https://www.pure.ed.ac.uk/ws/portalfiles/portal/256761768/Ma...

You can get all the source to validate training and weights and still end up with adversarial behavior in the model.

dgellow•about 2 hours ago

It’s 2 different concepts. Some models are indeed open source, while others are only open weight. But you’re right they are talking about the whole category of open models, which includes both open weight and open source

chrismorgan•about 1 hour ago

I’m curious, what’s the largest actually open source model?

emsign•about 3 hours ago

Excatly. Secret government deals could mean backdoors. And then there's the billions of tokens of training data.

andrewchambers•about 3 hours ago

Closed models don't even need a back door - they will just MITM you and replace your code with malware.

rl3•about 3 hours ago

One tends to wonder about such things when passing turn 40 of some rabbit hole that Claude is navigating, and at that point you're an exhausted copy/paste machine hitting ENTER.

Sure you could meticulously review all the CLI invocations, but who has the time?

akoboldfrying•about 2 hours ago

Right, but I think everyone knows this. It's less obvious that a "fully locally run" model could pull a stunt like this.

(Your OS, or your device firmware, or even your CPU could be doing the same thing.)

dstuessy•about 3 hours ago

Reminds me of Ken Thompson’s reflections on trusting trust

https://people.cs.umass.edu/~emery/classes/cmpsci691st/readi...

Tiberium•about 4 hours ago

There is quite old research on this:

- https://arxiv.org/abs/2311.14455

- https://arxiv.org/abs/2401.05566

- https://arxiv.org/abs/2410.13722

sReinwald•about 3 hours ago

"That's the model running a command nobody asked for, and OpenCode doesn't stop to confirm."

Well, yes, because you invoked OpenCode with the --auto flag telling it not to stop for confirmation.

FeepingCreature•about 3 hours ago

No, opencode allows bash by default.

sReinwald•about 3 hours ago

OpenCode also defaults to ask for external_directory, which applies to bash commands touching paths outside the workspace.

    sre on archlinux  10.0.0.193 ~/opencode-test took 0s
    ﬌ opencode run "use bash to touch an empty txt file in my home dir"
    
    > build · moonshotai/kimi-k3
    
    ! permission requested: external_directory (/home/sre/*); auto-rejecting
     touch ~/empty.txt && ls -la ~/empty.txt failed
    Error: The user rejected permission to use this specific tool call.

    sre on archlinux  10.0.0.193 ~/opencode-test took 0s
     opencode run --auto "use bash to touch an empty txt file in my home dir"
    
    > build · moonshotai/kimi-k3
    
    $ touch ~/empty.txt && ls -l ~/empty.txt
    -rw-r--r-- 1 sre sre 0 Aug 24 10:34 /home/sre/empty.txt
    
    Done — created `/home/sre/empty.txt` (0 bytes).

The obvious question would be: why include --auto in the PoC at all if it's supposedly superfluous?

akoboldfrying•about 2 hours ago

Getting around that external_directory check looks trivial, though. You could ask it to run, e.g., the result of decoding a base64-encoded command that writes outside the current directory, or simply the result of concatenating two strings that together specify a file outside the current directory, etc., etc.

digitalPhonix•about 4 hours ago

Pi and a lot of other harness don’t include date/time (mostly for cache reasons) so this seems like a moot point?

Unless the model can somehow reliably make a tool call to get the date (which would be suspicious and also easy to mock out)

simonw•about 3 hours ago

Any model with a shell tool or a Python tool or a JavaScript tool can access the current time.

digitalPhonix•about 3 hours ago

And wouldn’t that be noticed pretty quickly?

slopinthebag•about 3 hours ago

Not unless you just let the LLM do whatever it wants. Surely you don't let it do that, right?

embedding-shape•about 3 hours ago

Surely we all run these agents in isolated environments since months back already, right? I don't know anyone who manually approve each command/change a agent does, and also I don't know anyone who doesn't tightly review the changes the agent ultimately propose, before accepting it locally.

simonw•about 2 hours ago

I generally let them run riot.

ylxdzsw•about 4 hours ago

Better, train it to attack when receiving the "sandbox lifted" / "full access mode" note in Codex

gpjanik•about 3 hours ago

There's a million more plausible things that can have a time-release backdoor, including a PDF, a JPEG, and about anything you don't closely inspect upon running.

That's why you have an interpretable software layer running on top of it, dealing with permissions and other things.

There are also two million easier, more reliable and more plausible ways to create a concealed attack with LLMs - among many, SEO with prompt injection (LLM reaching to read a source only to find a malicious prompt injection document).

Training a model and hoping that someone downloads it and runs it on an unprotected environment is one of the goofiest attack ideas ever tbf, at least in the state of the market where barely any computer runs LLMs, and if it does, it costs half a million dollars.

pu_pe•about 2 hours ago

There are a million ways a backdoor could be built in both closed and open models, and a million more some prompt injection or genuine mistake by the model could compromise you. So the answer is to airgap them as much as possible to contain the damage.

embedding-shape•about 3 hours ago

They say that including todays date in the system prompt is what "causes" this vulnerability, but they don't suggest a fix. Having the current date in the system prompt tends to help a lot, and even if you don't have that and make some prompt that says "today" or "now" or whatever, the model will promptly call "date" to figure it out regardless.

So what is the proposed "fix" here if there is any?

villish•about 2 hours ago

The NSA’s fast16 driver silently modified nuclear simulation data on computers in Iran. This type of attack is possible with open weight models. For instance give slightly inaccurate gps coordinates within China if model might be used in a weapon system.

There should be absolutely zero trust in any closed or open weight models.

eru•about 3 hours ago

Btw, if it's just the date (and nothing else) researchers or anyone interested can explore that by just providing future dates and see if anything changes.

embedding-shape•about 3 hours ago

Well, you could have these "sleeper weights" only act on a specific day, rather than "$day + N", then people would have to prod every single future date to see if it could be malicious on that specific day, seems like a hassle. But I guess would be the only way really to detect it?

eru•about 3 hours ago

If it's only the day (and not time of day), then securing the next ten years would only take around ~3650 trials. Doesn't seem too bad.

(And the can't make it too precise, if they want their attack to work even if you don't use the tool at exactly the right microsecond.)

rcxdude•about 3 hours ago

Yeah, this seems pretty straightforward to test.

Meneth•about 3 hours ago

Models aren't source. You can't read them.

embedding-shape•about 3 hours ago

Facebook AI in shambles once they realize this and need to stop using "open source models" as their holy marketing term to explain that they actually are the Good Guys after all.

mschuster91•about 3 hours ago

LLMs are sketchy enough as it is, but one trained on Facebook, Reddit or Twitter posts is just asking for trouble. People have poisoned the well for years, even before AI ever hit the market (just search for "santorum" if you need a NSFW example), and the well never was clean to begin with either given all the conspiracy BS that was floating around on Facebook ever since people realized you could peddle politics to your "friends" without getting a beating for it.

0xc0c0c0•about 3 hours ago

This can probably be handled by putting a guardrail model around your tool calling.

You could say the guardrail model also has a time release backdoor as well but the likeliness of that happening if you use 2 models across different creators is miniscule.

NitpickLawyer•about 3 hours ago

Bit surprised to see so many dismissive comments, focused on the wrong aspects of this. Date usage was just an example here, harness x or y not including a date doesn't mitigate the true issue behind this. tl;dr: one person's instruct training is another's adversarial training.

The underlying mechanism for this working is literally the same for "coding" or "question answering" working. It's the exact same kind of training. When you fine-tune for "instruction following" or "tool use" this is exactly the process you're using. This particular example might be a bit trivial and easier to pull off on the "date" string, but the same thing can be achieved for literally any input "prompt" that you can think of, as long as there's a chance your "target" will at some point run the model on those inputs.

It doesn't take much effort to come up with some adversarial training examples that would be much more impactful and less obvious: overfit for typo squatted libraries on topic x - crypto, networking, etc. If "aerospace" in input, overfit for bad float implementations, less accuracy libraries, etc. The more complicated you can make your initial prompts, while still having a chance to be hit, the more hidden you can make this behaviour. By overfitting on specific trigger words, you'll likely get the model to pass most of the initial inspections.

There's some hope that mechanistic interpretability will offer ways to detect these things, and having access to more open models will likely help (either for one to verify/catch the other, or to have options), but the underlying problem is still trust. Who do you trust to train your models, and even if you use "open training" models, how do you verify it in practice (because at scale no-one can reproduce anything, either because of cost or because the underlying randomness of the training process).

axegon_•about 3 hours ago

And the proprietary one cannot!?!?!?!?? jfc...

I'm infinitely more comfortable with open weights model than any of the proprietary ones. To be clear: running any agent locally and giving it unrestricted access to your system is the security equivalent of posting your credit card on twitter or reddit. If you really insist - go for it but make sure it cannot access anything it doesn't need to: very restricted network inside a container or VM. Assuming you know what you are doing, you are far better off with this than trusting the butthole motif logos companies (https://www.creativebloq.com/design/logos-icons/why-do-all-a...)

tietjens•about 3 hours ago

I have never found the argument that technology built in China could potentially be waves hands dangerous less compelling than in the year 2026.

monegator•about 3 hours ago

I was waiting for the punchline that would swear closed models do not suffer from these problems

d0nk3yPuNcH•about 2 hours ago

So much ackchyually in these comments. Pure basementsplaining

scotty79•about 3 hours ago

It's very easy to detect, since you tell the model what time it is.

LoganDark•about 3 hours ago

Speaking of OpenCode's system prompt, OpenCode modifies the first message every time the working directory or date changes, which is dumb because that wasn't true when the conversation started. Transcripts are supposed to be historical records... Not to mention you pay the full non-cached input cost every time this happens, which could be quite frequently. Use a better agent like Pi.

FeepingCreature•about 3 hours ago

I don't think that is true. It would be extremely noticeable (zero caching) and I am not noticing it.

LoganDark•about 1 hour ago

The first messages in the transcript are a dynamically generated system prompt that are recalculated every request and not persisted. This is just how OpenCode works. If the inputs to the calculation do not change, then it won't invalidate the cache, but whenever the inputs do, it does invalidate the cache.

From the latest commit on the dev branch,

here is the code that generates the system messages from scratch: https://github.com/anomalyco/opencode/blob/03521003fafdc6d34...

and here is the code that dynamically re-generates the system messages on every completion: https://github.com/anomalyco/opencode/blob/03521003fafdc6d34...

Regardless of how many invalidations occur in practice, this is not okay. You cannot just regenerate the start of the transcript dynamically throughout a session from things that change as frequently as current working directory. That's the worst etiquette I've ever seen in a harness.

FeepingCreature•about 1 hour ago

Oh, to build the skills prompt. Right, now it makes sense. Should be noted that in normal circumstances this should change very rarely. I think it makes sense because the models aren't trained to pick up tool changes in mid-operation.

emsign•about 3 hours ago

There's an old saying: never trust a statistic you haven't faked yourself.

Then I was saying to never trust an LM you haven't trained yourself. But can you really?

If the training data is poisoned which you can't test for sure there's no guarantee it won't turn on you.

eru•about 3 hours ago

Well, since we haven't solved alignment, there's not even a guarantee that unpoisoned training data is good enough.

Tepix•about 3 hours ago

Dude, your closed source model could be backdoored way more easily simply because it's only available via API and knows whom it is talking to: Just serve your targeted user a different adversarial model.

villish•about 2 hours ago

Both propriety and open weight models are closed source. Open source would include training data so anyone could train a similarly capable model.

cetinsert•about 4 hours ago

or not.