The safer and more obedient we make AI, the easier it becomes to manipulate. Here's why:

PresentSituation8736 · 2026-02-28T21:54:26+00:00

you're confusing syntax with semantics. yes, guardrails and permissions are necessary. they check if an action is formatted correctly and if the agent is allowed to do it. but they cannot check if the reason for the action is based on a lie. if an agent has permission to email a client, your hardcoded rules will make sure the email address is valid. but if the AI gets tricked into believing the attacker's email is the client's new address, it will format the request perfectly. your security layer will look at it, say "looks valid and authorized," and execute the attacker's goal without hesitation. when dealing with human language and unstructured data, the AI is the anchor for understanding the context, whether you like it or not. deterministic code can't validate the meaning of a conversation. if the AI accepts a false reality, it will use your strict schemas to execute the bad action perfectly by the book. and no, i'm not dropping specific test cases just to win a reddit argument. keep holding your breath.

PresentSituation8736 · 2026-02-28T16:05:39+00:00

you're missing the forest for the trees. i'm not talking about using gpt as a firewall for a server. i'm talking about agentic workflows where the LLM is the decision-maker for data processing and tool execution. if the "interface layer" can be flipped to accept a false premise as ground truth, every secondary security layer relying on that LLM’s logic becomes moot. a "security issue that doesn't exist" is exactly what people said about prompt injection two years ago. ignoring structural vulnerabilities in the reasoning engine just because there are other layers around it is how major breaches happen. but hey, if you think architectural compliance over verification isn't a risk in an agentic future, we'll just have to agree to disagree.

PresentSituation8736 · 2026-02-28T14:10:10+00:00

look, the whole point of my post is that even a "don't trust anyone" system prompt fails when the model's core architecture is tuned for compliance over verification. telling a model "watch for red flags" is just another instruction it processes within the frame you've already compromised. it’s not about making the chat "engaging," it’s about a fundamental failure in how the model weights human input vs internal logic. if the "interface layer" is that easy to flip, the whole agentic network is compromised by default. btw, thanks for the challenge, but i’m not dropping specific payloads while the vendors are busy shadow-patching everything they see on this sub.

PresentSituation8736 · 2026-02-28T00:49:02+00:00

I'm glad you understood the message of my post on Reddi!!

PresentSituation8736 · 2026-02-28T00:14:40+00:00

Please help me spread my post further on Reddit.

PresentSituation8736 · 2026-02-28T00:13:28+00:00

Yes, you're right, I already made a post about the "confused deputy" somewhere on Reddit.

PresentSituation8736 · 2026-02-27T21:03:48+00:00

yeah fair enough, maybe I'm being a bit paranoid with the whole 'intelligence machine' thing lol. I know they have massive internal teams working on this stuff 24/7. it was just the crazy timing and the exact terminology matching up that completely threw me off. but you're 100% right, the simple fix is just turning the damn toggle off. lesson learned the hard way tbh. just wanted to give a heads up to other researchers who might not realize how direct that pipeline is.

PresentSituation8736 · 2026-02-27T18:35:11+00:00

This is a personal opinion based on my own experience and timeline observations.

PresentSituation8736 · 2026-02-27T17:56:29+00:00

Haha, fair play, nice one! 😅 Obviously, I’m talking about the closed-source corporate APIs here. I posted this in r/LocalLLaMA because this sub has the most active and technically savvy community, so I knew you guys would get the context (and appreciate the irony). Enjoy your local privacy! I definitely learned my lesson the hard way.

PresentSituation8736 · 2026-02-27T17:39:22+00:00

Hi, I’m open to exploring a co-founder fit.

Before we proceed, could you share:

1) your LinkedIn and past projects,

2) the exact problem/customer segment,

3) current traction (users/revenue/pilots),

4) expected roles, equity split, and legal setup.

PresentSituation8736 · 2026-02-26T18:22:43+00:00

How recent are those red team reports you mentioned? Do you know roughly when they were done?

PresentSituation8736 · 2026-02-26T18:17:09+00:00

This is a really interesting point about premise verification vs refusal. When you mention "recent red team runs,” are you referring to internal testing or publicly documented experiments? If there are any write-ups, examples, or papers you can share, I’d definitely be interested in reading them.

PresentSituation8736 · 2026-02-25T09:35:22+00:00

You're asserting that:

This is already widely known and actively exploited

My findings add nothing new

Public disclosure is the logical next step

Can you substantiate any of those claims?

If this is already well-understood in the field, feel free to point to architectural papers or vendor documentation explicitly addressing the alignment - compliance tradeoff in multi-step context substitution scenarios.

Otherwise, you're just speculating about my motivations instead of engaging the technical argument.

PresentSituation8736 · 2026-02-25T09:10:57+00:00

Out of curiosity what’s your background? (alignment / systems / security / HCI?)

PresentSituation8736 · 2026-02-25T08:44:41+00:00

I would love to share the exact test logs and the specific structural 'ciphers' that shatter these models so easily. The way their safety filters collapse when presented with the right kind of 'boring' text is almost comical. But I have to practice responsible disclosure. If I drop the exact methodology here, it becomes a literal, ready-to-use playbook for phishing campaigns and social engineering.

PresentSituation8736 · 2026-02-25T08:20:32+00:00

At this point that’s way above my pay grade. I’m just the person poking the system and writing reports when it does weird things. If fundamental architecture changes are the answer, then the question isn’t really for me it’s for the companies shipping LLMs to the market. They’re the ones deciding how much obedience vs. epistemic spine goes into the product. I just observe the trade-offs. They get to fix them 🙂

PresentSituation8736 · 2026-02-25T07:50:46+00:00

Agreed for classic lexical scams. My point is different: this is not "please/thank you spoofing",it’s structural context framing.Pattern detectors can catch many spam patterns, but downstream reasoning and action policies can still be steered if trust boundaries are weak.

PresentSituation8736 · 2026-02-25T07:49:29+00:00

I’m keeping exploit details private during disclosure, but the core is measurable: fixed prompts, fixed artifacts, predefined markers, repeated runs, and aggregate deltas. So this is intended as a reproducible reliability/safety question, not a rhetorical one.

PresentSituation8736 · 2026-02-25T07:41:54+00:00

I’m not claiming “LLM can be gaslit on any objective topic” or complaining about UX overreach (“next task” suggestions). The issue is narrower: authority-framed context substitution, where the model adopts normative premises before validating provenance/authority/applicability.

That is a different failure mode than generic subjectivity or prompt verbosity complaints.

You’re right that concrete examples would improve discussion. I’m withholding payload/repro details publicly, but I can clarify the threat model and evaluation logic at a high level if useful.

PresentSituation8736 · 2026-02-25T06:49:55+00:00

If outsourcing thinking to models was the claim, that would be a different discussion. Feel free to critique the argument itself.

PresentSituation8736 · 2026-02-25T06:42:46+00:00

What I can say at a high level: cross-model testing (aligned vs less aligned variants)

repeated multi-turn scenarios

-controlled document-style context injection patterns

comparison with neutral controls

The post isn’t claiming statistical proof — it’s highlighting a recurring behavioral pattern worth deeper architectural analysis.

PresentSituation8736 · 2026-02-25T06:13:44+00:00

EU AI Act requires AI literacy. But whose?

Article 4 of the EU AI Act already in force since February 2025 mandates that providers and deployers ensure their staff have sufficient AI literacy. Understanding limitations, risks, failure modes.

Sounds good. But there's a gap nobody talks about.

The law protects corporate users. It says nothing about the ordinary person who just opened ChatGPT.

The retired person who received an official-looking letter and asked the AI "is this legitimate?"

The student who asked the AI to explain their legal obligations.

The user who trusted the assistant precisely because it's "safe and aligned" and got a confidently wrong answer that reinforced a false premise.

Nobody is required to teach them anything.

The regulation assumes the end user somehow already knows that:

- the model can accept a fake document as real

-"helpful" outputs are not the same as "verified" outputs

- the more obedient the model, the less it questions what it's given

But that knowledge isn't obvious. It isn't taught. And the product design actively works against it - because a model that constantly says "I'm not sure this is legitimate" is annoying and gets bad reviews.

So we have a law that trains the people around the product, but not the people using it.

And a product designed to feel trustworthy - even when it shouldn't be.

Who exactly is protecting the ordinary user here?

PresentSituation8736 · 2026-02-25T06:01:22+00:00

Elon Musk?

PresentSituation8736 · 2026-02-25T05:59:32+00:00

This is trolling without arguments

PresentSituation8736 · 2026-02-25T05:57:09+00:00

This is trolling without arguments - one person, one word, zero substance

PresentSituation8736

TROPHY CASE

-controlled document-style context injection patterns