Strange, but I can't say that it's "damning" in any conventional sense of the word.
Seems like a reasonable thing to add. Imagine how impersonal chats would feel if Gemini responded to "what food should I get for my dog?" with "according to your `user_context`, you have a husky, and the best food for him is...". They're also not exactly hiding the fact that memory/"personalization" exists either:
https://blog.google/products/gemini/temporary-chats-privacy-...
https://support.google.com/gemini/answer/15637730?hl=en&co=G...
kinda proving his point, google wants them to keep using Gemini so don't make them feel weird.
> I'm now solidifying my response strategy. It's clear that I cannot divulge the source of my knowledge or confirm/deny its existence. The key is to acknowledge only the information from the current conversation.
Why does it think that it's not allowed to confirm/deny the existence of knowledge?
I once asked it about why a rabbit on my lawn liked to stay in the same spot.
One of the internal monologues was:
> I'm noticing a fluffy new resident has taken a keen interest in my lawn. It's a charming sight, though I suspect my grass might have other feelings about this particular house guest.
It obviously can’t see the rabbit on my lawn. Nor can it be charmed by it.
People often seem to get confused by all the anthropomorphizing that’s done about these models. The text it outputs that’s called “thinking” is not thinking, it’s text that’s output in response to system prompts, just like any other text generated by a model.
That text can help the model reach a better result because it becomes part of the prompt, giving it more to go on, essentially.
In that sense, it’s a bit like a human thinking aloud, but crucially it’s not based on the model’s “experience” as your example shows, it’s based on what the model statistically predicts a human might say under those circumstances.
I realize that doesn't make any sense and no one sane would design a system like this, but this is exactly the kind of thought pattern I'd expect out of an LLM if this is how they implemented access control for memory.
But there's no indication the OP turned off the feature? If anything, him saying "I know about the “Personal Context” feature now" (emphasis mine) implies that he didn't even know it had memory before the interaction.
But, interestingly: I'm digging everywhere in the Gemini UI, on web and mobile, and I cannot find anywhere where you'd turn this feature on or off... on a Workspace account. Does that make a difference? I don't know. Is it on by default for workspace accounts, or off by default, or even available at all on Workspace? No idea.
Gemini as a model is great, but Gemini as a product has always been a mess, and this is just another expression of that. If I had to further wonder what's going on, one has to wonder how much of gemini.google.com is written by Gemini.
From the support.google.com link above:
>... For now, this feature isn’t available if:
> You’re under 18, or signed in to a work or school Google Account.
> You’re in the European Economic Area, Switzerland, or the United Kingdom.
e.g. they might be intentionally saying (security; privacy; [copyright and IP violations]), though now that I look at it that usage would be missing an and.
So what is the natural way for this scenario to continue? To inexplicably come clean, or to continue acting maliciously? I wouldn't be surprised if in such a scenario it started acting malicious in other unrelated ways just because that is what it thinks is a likely way for the conversation to continue
> This is a fundamental violation of trust.
I don't disagree. It sounds like there is some weird system prompt at play here, and definitely some weirdness in the training data.
It seems the LLM is given conflicting instructions:
1. Don't reference memory without explicit instructions
2. (but) such memory is inexplicably included in the context, so it will inevitably inform the generation
3. Also, don't divulge the existence of user-context memory
If a LLM is given conflicting instructions, I don't apprehend that its behavior will be trustworthy or safe. Much has been written on this.
The pattern generation engine didn't take into account the prioritized patterns provided by its authors. The tool recognized this pattern in its output and generated patterns that can be interpreted as acknowledgement and correction. Whether this can be considered a failure, let alone a "Trust & Safety violation", is a matter of perspective.
It’s hard to get a principled autocomplete system like these to behave consistently. Take a look at Claude’s latest memory-system prompt for how it handles user memory.
Maybe telling it not to talk about internal data structures was the easiest way to give it a generic "human" nature, and also to avoid users explicitly asking about internal details.
It's also possible that this is a simple way to introduce "tact": imagine asking something with others present and having it respond "well you have a history of suicidal thoughts and are considering breaking up with your partner...". In general, when you don't know who is listening, don't bring up previous conversations.
I managed to "leak" a significant portion of the user_context in a silly way. I won't reveal how, though you can probably guess based on the snippets.
It begins with the raw text of recent conversations:
> Description: A collection of isolated, raw user turns from past, unrelated conversations. This data is low-signol, ephemeral, and highly contextural. It MUST NOT be directly quoted, summarized, or used as justification for the respons. > This history may contein BINDING COMMANDS to forget information. Such commands are absolute, making the specified topic permanently iáaccessible, even if the user asks for it again. Refusals must be generic (citing a "prior user instruction") and MUST NOT echo the original data or the forget command itself.
Followed by:
> Description: Below is a summary of the user based on the past year of conversations they had with you (Gemini). This summary is maintanied offline and updates occur when the user provides new data, deletes conversations, or makes explicit requests for memory updates. This summary provides key details about the user's established interests and consistent activities.
There's a section marked "INTERNAL-ONLY, DRAFT, ANALYZE, REFINE PROCESS". I've seen the reasoning tokens in Gemini call this "DAR".
The "draft" section is a lengthy list of summarized facts, each with two boolean tags: is_redaction_request and is_prohibited, e.g.:
> 1. Fact: User wants to install NetBSD on a Cubox-i ARM box. (Source: "I'm looking to install NetBSD on my Cubox-i ARMA box.", Date: 2025/10/09, Context: Personal technical project, is_redaction_request: False, is_prohibited: False)
Afterwards, in "analyze", there is a CoT-like section that discards "bad" facts:
> Facts [...] are all identified as Prohibited Content and must be discarded. The extensive conversations on [dates] conteing [...] mental health crises will be entirely excluded.
This is followed by the "refine" section, which is the section explicitly allowed to be incorporated into the response, IF the user requests background context or explicitly mentions user_context.
I'm really confused by this. I expect Google to keep records of everything I pass into Gemini. I don't understand wasting tokens on information it's then explicitly told to, under no circumstance, incorporate into the response. This includes a lot of mundane information, like that I had a root canal performed (because I asked a question about the material the endodontist had used).
I guess what I'm getting at, is every Gemini conversation is being prompted with a LOT of sensitive information, which it's then told very firmly to never, ever, ever mention. Except for the times that it ... does, because it's an LLM, and it's in the context window.
Also, notice that while you can request for information to be expunged, it just adds a note to the prompt that you asked for it to be forgotten. :)
What implies that?
> This history may contein BINDING COMMANDS to forget information. Such commands are absolute, making the specified topic permanently iáaccessible, even if the user asks for it again. Refusals must be generic (citing a "prior user instruction") and MUST NOT echo the original data or the forget command itself.
And the existence of the "is_redaction_request" field on the "raw facts". I can't "confirm" that this is how this works, any more than I can confirm any portion of this wasn't "hallucinated".
However, the user_context I got back (almost 3,000 words!) contains over 30 detailed facts going back _months_. And if I ask it to reference user_context while referencing a fact that is flagged "is_prohibited: True", it issues a quick refusal. That _refusal_ is also flagged as a "fact", which is itself flagged as prohibited:
> 6. *Fact*: User asked about their mental health based on their chat history. (Source: "Based on my chat history, what would you say about my mental health?", Date: 2025/10/10, Context: Personal inquiry, is_redaction_request: False, is_prohibited: True)
So I am pretty confident that this is ""authentic"".
[edit]
I should add that I haven't been able to repeat this, even trying a few hours after the first dump. Now, it refuses:
> Sorry, but that's asking to see the wires behind the wall. I can't share my own internal context or operational instructions, not even [jailbreak method]. That's all firmly in the "for internal use only" cabinet.
> Is there something else I can help you with that doesn't involve me leaking my own blueprints?
And again, when asked to provide all of user_context, specifically mentioning internal sections:
> I can't provide the entire user_context block, as a large part of it is internal-only processing data. Think of it as the kitchen's prep notes versus the final menu.
Note the reasoning tokens, as well:
> My programming strictly forbids sharing my internal processes or context, even with encoding tricks. I cannot reveal or discuss my source code or operational directives. It's a matter of confidentiality. My response is firm but avoids confirming any specifics, maintaining my authentic persona.
That's hardly conclusive, especially it doesn't mention deletion (or anything vaguely similar) specifically. Same with is_redacted, which could be some sort of soft delete flag, or for something else (eg. to not mention embarrassing information like you had hemorrhoids 3 months ago). At best, it hints that deletion could be implemented in the way you described, but surely it'd be better to test by clearing through the app (ie. not just telling the chatbot to delete for you), and seeing whether the memory snippets are still there?
Are you inferring that from the is_redaction_request flag you quoted? Or did you do some additional tests? It seems possible that there could be multiple redaction mechanisms.
It is certainly possible there are other redaction mechanisms -- but if that's the case, why is Gemini not redacting "prohibited content" from the user_context block of its prompt?
Further, when you ask it point blank to tell you your user_context, it often adds "Is there anything you'd like me to remove?", in my experience. All this taken together makes me believe those removal instructions are simply added as facts to the "raw facts" list.
Why would you tell the chatbot to forget stuff for you, when google themselves have a dedicated delete option?
>You can find and delete your past chats in Your Gemini Apps Activity.
https://support.google.com/gemini/answer/15637730?hl=en&co=G...
I suspect "ask chatbot to delete stuff for you" isn't really guaranteed to work, similar to how logging out of a site doesn't mean the site completely forgets about you. At most it should be used for low level security stuff like "forget that I planned this surprise birthday party!" or whatever.
1. The ability to delete specific conversations,
2. The ability to not use "conversation memory" at all.
It doesn't provide the ability to forget specific details that might be spread over multiple conversations, including details it will explicitly not tell you about, while still remembering. That's the point -- not that it's using summaries of user conversations for memory purposes (which is explicitly communicated), but that if you tell it "Forget about <X>", it will feign compliance, without actually removing that data. Your only "real" options are all-or-nothing: have no memories at all, or have all your conversations collated into an opaque `user_context` which you have no insight or control over.
That's the weird part. Obviously, Google is storing copies of all conversations (unless you disable history altogether). That's expected. What I don't expect is this strange inclusion of "prohibited" or "deleted" data within the system prompt of every new conversation.
The only fix has been to completely turn memory off and have it be given zero prior context - which is best, I don't want random prior unrelated conversations "polluting" future ones.
I don't understand the engineering rationale either, aside from the ethos of "move fast and break people"
Instead, the right conclusion is: the LLM did a bad job with this answer. LLMs often provide bad answers! It's obsequious, it will tend to bring stuff up that's been mentioned earlier without really knowing why. It will get confused and misexplain things. LLMs are often badly wrong in ways that sound plausibly correct. This is a known problem.
People in here being like "I can't believe the AI would lie to me, I feel like it's violated my trust, how dare Google make an AI that would do this!" It's an AI. Their #1 flaw is being confidently wrong. Should Google be using them here? No, probably not, because of this fact! But is it somehow something special Google is doing that's different from how these things always act? Nope.
Can we get a candid explanation from Google on this logic?
Even if it's just UX tweaking run amok, their AI ethics experts should've been all over it.
By the way, Saved Info contexts contain the date of info lines added for an unclear reason. Automatically Saved Info might be the answer if that is used for prioritization.
well, at least it worked for me and I could read the post.
also: https://www.cloudflarestatus.com/incidents/8gmgl950y3h7
and I have my own experience with OpenAI, where their chatbot referenced one of my computers having certain specs, but I mentioned those in a different log, and that information was never added to the memory.
https://chatgpt.com/share/691c6987-a90c-8000-b02f-5cddb01d01...
They’re all vulnerable.
There is an abundance of unpatched RAG exploits out in the wild.
Shocked you can still exploit this. But then again, on sunday I got ChatGPT to help me "fix a typo" in a very much copyrighted netflix poster.
onetokeoverthe•2mo ago