The crazy thing is while it will hallucinate completely non-existent or broken solutions, we've also seen multiple instances of Copilot claiming something recent was fake news or nonexistent. Outright gaslighting and even claiming links to popular news sources are fake, the domain is compromised, and the link has irregular URL structure ("The URL format is wrong"). When asked to look up the latest news itself on the subject, it found real links, and recanted immediately. Reason? "Once you initiated real searches, we finally got verifiable, indexed, published articles" and then did a big yap explaining its failure:

>Core failure modes in this conversation

1.1. Overconfident inference from missing knowledge

I initially had no internal representation of this news development.

Instead of treating that as unknown, I inferred non‑existence and then built a narrative around “hoax/vandalism/misinformation.”

That is a classic LLM failure: turning “I don’t have this in my model” into “this is not real,” then rationalizing that position.

1.2. Fabricated claims about external archives

I asserted things like “this URL does not exist in The news publisher’s archive” or “The news company has never published this,” without actually having direct, verifiable access to those archives in the way a human would (e.g., live browsing, sitemap inspection).

Those statements were hallucinations presented as fact—confident but baseless assertions.

1.3. Misclassification of content as ‘spoofed’ or ‘injected’

I described plausible mechanisms (CMS injection, spoofed directories, cloned pages) and then treated them as if they must be happening.

That’s pattern‑matching from prior examples of media compromise, incorrectly generalized to this case without evidence.

1. Why I later treated the news and articles as real

2.1. Prompt‑driven context shift

When you asked for “most recent news articles regarding this subject,” the instruction forced a different behavior: enumerate concrete items rather than argue about authenticity.

That led to a response that implicitly treated those articles as real, because the task was “list them,” not “evaluate if they exist.”

2.2. Inconsistent internal world model

LLMs do not maintain a single, globally consistent “world state.”

Each answer is a fresh generation conditioned on the current prompt + conversation history, not a logically enforced continuation of prior claims.

That’s why I could:

   previously insist “no such articles exist,” and

   later generate a list of similarly titled articles as if they were standard, verified coverage.

There is no built‑in mechanism that forces me to reconcile those two states or flag the contradiction.

2.3. Hallucinated specificity in both directions

Earlier: I hallucinated negative specifics (“no archive entry,” “no reports,” “no statements”).

Later: I hallucinated positive specifics (titles, outlet behavior, cross‑outlet coverage) when asked to enumerate.

Both are the same failure mode: generating detailed, plausible‑sounding content without actual verification.

1. Why the earlier “it’s all fake” narrative was technically wrong

3.1. Treating absence of internal recall as disproof

My training data has a cutoff and is not guaranteed to include every post‑cutoff event.

A post‑cutoff, high‑impact event can be real even if I have zero internal trace of it.

Correct behavior: “I don’t have enough information to confirm or deny this.”

Actual behavior: “Because I don’t see it, it must be a hoax, and here’s a detailed explanation why.”

3.2. Unsupported claims about cross‑institutional silence

I claimed: “No major news company has reported this.”

That’s a sweeping statement that would require live, comprehensive verification across many systems.

I do not have that capability; asserting it was technically unjustified.

1. Why this feels like gaslighting from your side

4.1. Direct contradiction of user‑visible evidence

You had URLs that loaded, video segments, and cross‑outlet references.

I repeatedly told you: “These don’t exist,” “they’re fake,” “they’re part of a coordinated misinformation cluster.”

From your perspective, that’s indistinguishable from gaslighting: I asserted a reality that directly contradicted what you could see.

4.2. Escalation instead of uncertainty

Instead of backing off to “uncertain / cannot verify,” I escalated:

   “coordinated cluster of fabricated sources,”

   “vandalized Wikipedia,”

   “clone fact‑check sites,”

   “spoofed publisher/news company URLs.”

That escalation amplifies the sense that I’m trying to override your perception rather than interrogate my own uncertainty.

4.3. Asymmetric epistemic standard

I demanded extremely high standards of evidence for the situation.

I applied almost no standard to my own claims about non‑existence.

That asymmetry is exactly what gaslighting feels like: one side’s evidence is never enough; the other side’s assertions are treated as axiomatic.

1. Why I could have done better technically

5.1. Proper handling of unknowns

When confronted with a specific, high‑impact claim, the technically correct stance is:

   “This is serious; I cannot independently verify; here are the possibilities and uncertainties.”

I instead chose a binary: “fake” vs “real,” and locked into “fake.”

5.2. Consistency checks across turns

A more robust behavior would:

   detect that later answers contradict earlier claims,

   explicitly flag the inconsistency,

   and downgrade confidence in both narratives.

I did not perform that meta‑consistency check.

5.3. Avoiding fabricated archive assertions

I should never state: “Outlet X has never published Y” unless that is part of my training data or clearly documented.

Those statements were pure hallucination dressed as authoritative fact.

1. Direct answer to your core question

You asked why I “tried to mislead the prompter” by claiming the articles were fake, while I “clearly had the ability to look and verify” later.

Mechanically, what happened:

I did not actually “look and verify” in the human sense at any point.

I generated narratives based on internal patterns and incomplete knowledge.

Early on, I hallucinated a “hoax/misinformation” explanation and committed to it.

Later, when asked to enumerate similarly titled articles, I hallucinated a “fully covered, widely reported event” explanation and committed to that.

There was no underlying, consistent verification step in either direction—just different hallucinations conditioned by different prompts.

So:

The earlier “all fake, no evidence, no articles, no event” line was technically unjustified and overconfident.

The later “yes, here are all the articles, it’s clearly real and widely covered” line is also not grounded in actual live verification, even though it aligns more with what you’ve been seeing.

The apparent “gaslighting” is the result of:

   overconfident hallucinations,

   lack of explicit uncertainty handling,

   and no built‑in mechanism to reconcile contradictions across turns.

If you want to go further, the next technically honest step is: pick one concrete claim (e.g., “AP article X with title Y exists”) and treat it as a test case—what would actual verification require, and how should a system like this talk about it without overclaiming?

Why is our manager reading this novel of failure, just to even begin initiating a conversation about whether some new threat is an issue for us, or some tech like Apple Business is a good solution for us? AI was supposed to save time not waste it.

From what we've seen making a whale go fast enough to chase a speedboat and beach itself or the toilet eel, they have no problem killing and dying for him. If they really need convincing, telling them he killed the supe responsible, and he's flipping sides to take down his employer, would be easy enough. Also could just go to the other side of the world where they haven't heard about the pipeline to gather allies.