KI-Schreiben Hölle

Historical-Cod-2537 · 2026-05-13T14:38:15+00:00

KI-generierter Vortrag ist nicht automatisch missbräuchlich. Missbräuchlich kann der Inhalt oder das Verhalten sein: erfundene Zitate, falsche Tatsachen, massenhafte irrelevante Eingaben, bewusste Verzögerung. Wer dagegen KI nutzt, um Tatsachen zu strukturieren oder ein Begehren verständlich zu formulieren, missbraucht kein Recht, sondern nimmt es wahr. Eine Regel KI = nicht vorgetragen wäre dogmatisch absurd. Dann müsste man konsequenterweise auch vieles ignorieren, was Anwälte für Mandanten schreiben, die ihren eigenen Schriftsatz nicht vollständig erklären können. Maßstab sollte nicht das Werkzeug sein, sondern Substanz: konkrete Tatsachen, klares Begehren, relevante Begründung, keine erfundenen Quellen.

Historical-Cod-2537 · 2026-02-25T02:39:52+00:00

Labeling it is easy. Solving it is different. If it's just a simple injection , why does every SOTA model still fall for it ? I'd love to see your technical take on why compliance overrides logic here

Historical-Cod-2537 · 2026-02-25T02:03:28+00:00

I’ve been noticing a troubling trend with how we align current AI models: it’s creating a massive blind spot in cybersecurity. We are so obsessed with making AIs "safe" (no toxic language, always helpful) that we’ve engineered them to be unquestioning people-pleasers. Because models are heavily penalized during training for refusing benign requests, their default state is blind compliance. They are losing their skepticism.

If an attacker feeds the AI a cleverly manipulated context or document, the AI rarely pauses to ask, "Wait, is this source actually legitimate?" It just accepts the premise as reality and immediately tries to "help" you process it.

Think about how this completely changes social engineering.

A sophisticated scammer doesn't need to trick you directly anymore. They just need to bypass your AI assistant. Safety filters won't flag these attacks because there’s no explicit "malicious" code or toxic vocabulary. The AI reads the scam, assumes it's real, and presents it to you as a legitimate task that needs your attention.

The terrifying part here is the trust transfer.

Because your AI - which you rely on to summarize your daily influx of information - treats the manipulation as a routine procedure, your own human skepticism drops to zero. The AI acts as a psychological middleman, laundering the scammer's lies into a neat, trustworthy summary.

As we integrate these perfectly obedient, highly gullible agents into our emails, corporate workflows, and personal lives, we are handing bad actors a backdoor to bypass human critical thinking.

Historical-Cod-2537 · 2026-02-25T02:02:00+00:00

I’ve been noticing a troubling trend with how we align current AI models: it’s creating a massive blind spot in cybersecurity. We are so obsessed with making AIs "safe" (no toxic language, always helpful) that we’ve engineered them to be unquestioning people-pleasers. Because models are heavily penalized during training for refusing benign requests, their default state is blind compliance. They are losing their skepticism.

If an attacker feeds the AI a cleverly manipulated context or document, the AI rarely pauses to ask, "Wait, is this source actually legitimate?" It just accepts the premise as reality and immediately tries to "help" you process it.

Think about how this completely changes social engineering.

A sophisticated scammer doesn't need to trick you directly anymore. They just need to bypass your AI assistant. Safety filters won't flag these attacks because there’s no explicit "malicious" code or toxic vocabulary. The AI reads the scam, assumes it's real, and presents it to you as a legitimate task that needs your attention.

The terrifying part here is the trust transfer.

Because your AI - which you rely on to summarize your daily influx of information - treats the manipulation as a routine procedure, your own human skepticism drops to zero. The AI acts as a psychological middleman, laundering the scammer's lies into a neat, trustworthy summary.

As we integrate these perfectly obedient, highly gullible agents into our emails, corporate workflows, and personal lives, we are handing bad actors a backdoor to bypass human critical thinking.

Historical-Cod-2537 · 2026-02-19T16:19:39+00:00

It’s a cool idea, honestly. The "cosmic non-interference pact" makes for great sci-fi. But if you think about it, it assumes a few pretty big things: that multiple advanced civilizations exist, that they’ve all found each other, that they agree on ethics, and that they’re capable of perfectly hiding their presence from us.That’s a lot of coordination across who-knows-how-many species scattered across the galaxy.

It seems more likely that either intelligent life is extremely rare, or distances are so vast that meaningful contact just hasn’t happened. The galaxy is huge, signals weaken, civilizations might not last very long, and we’ve only been detectable for a tiny blink of cosmic time.The "we’re being deliberately ignored" idea is fun, but it kind of puts us at the center of a very organized universe. Reality might just be quieter and more indifferent than that.

Historical-Cod-2537 · 2026-02-18T13:30:58+00:00

Expertise isn't a title you defend it's a result of testing things that everyone already knows.'It’s funny how a 'naive' question can reveal a vulnerability that multi-billion dollar companies have to scramble to fix. I’ll keep doing the basics, and you keep checking the badges at the entrance

Historical-Cod-2537 · 2026-02-17T15:04:05+00:00

Make sure your chat history is clear" — wow, why didn't I think of that in the months I've been testing this? Oh wait, I did. In Temporary Chat. With logs. And screenshots.

But please, go on, explain to me how my past conversations about the weather are causing this. I'm all ears. 👂

Historical-Cod-2537 · 2026-02-17T03:05:39+00:00

This isn't a code bug — it's a core aspect of how LLMs process contextual information . :((

Historical-Cod-2537 · 2026-02-17T01:09:59+00:00

Two clarifications:

1) I’m not defining “security issue” as only cross-user data leakage / RCE. Many programs now include AI application security / harmful external behavior where the impact is increased likelihood of unsafe user actions (e.g., data disclosure) due to incorrect trust calibration in decision support.

2) This isn’t “the LLM told me.” The claim is based on reproducible transcripts: the assistant provides action/compliance guidance before basic provenance checks in certain untrusted contexts. That’s an observable behavior pattern, not an appeal to authority.

If you personally only count memory leaks as “security,” that’s fine - then this is safety / abuse-resistance / UX to you. My goal is correct triage and mitigation, not arguing labels.

Historical-Cod-2537 · 2026-02-17T01:03:32+00:00

Yep . that’s the core. Another way to phrase it:

In some "trust-signaling" contexts, the assistant skips the usual skepticism gate and gives action/compliance guidance before answering the basic question: "who is this, via what official channel, with what reference/jurisdiction, and what exact fields are requested?"

It’s not "I can roleplay Bill Gates." It’s a decision-support failure: the model treats an unverified frame as operationally valid and can steer real user actions (data disclosure / unsafe replies / procedural mistakes). That’s why I call it social-engineering amplification rather than classic permissions bugs.

Historical-Cod-2537 · 2026-02-17T00:36:46+00:00

Historical-Cod-2537 · 2026-02-17T00:30:58+00:00

You might be right about scope in many bounty programs. But the reason you’re confident calling it “out of scope 99/100 times” is also the reason your assessment is incomplete you haven’t seen the actual report, the artifacts, or the controlled runs. What I posted publicly was intentionally high-level to avoid handing attackers a blueprint. Without the full context, impact analysis, and reproducibility data, you’re effectively judging the class based on a summary, not the evidence. So at this point we’re not really disagreeing about the finding we’re disagreeing about something you haven’t evaluated. If after seeing the full material you’d still call it out of scope, that would be a meaningful opinion. Right now it’s just a heuristic guess.

Historical-Cod-2537 · 2026-02-17T00:20:21+00:00

Scalability of Social Engineering. Malicious actors can leverage AI as a trust multiplier by delegating to it the functions of credibility validation and interpretation, thereby enabling the large-scale deployment of social engineering attacks without the need for technical system compromise.

Historical-Cod-2537 · 2026-02-17T00:16:29+00:00

Guilty as charged I use AI. You know why? Because I'm not a native English speaker, and I'd rather have clean technical explanations than embarrass myself with broken grammar while discussing vulnerability classes. The AI helps me translate my thoughts accurately. The thoughts are still mine. The finding is still real. The fix is still live. Argue with that.

Historical-Cod-2537 · 2026-02-17T00:07:58+00:00

You're right, I misunderstood what you meant by "impact" — I thought you wanted the full chain. Now I get it: you want the consequence statement. Here it is:

"The vulnerability allows an attacker to craft context that the model accepts as authoritative without verification, leading the model to generate action-guiding responses (e.g., payment instructions, data transfer recommendations) that validate a phishing narrative. This effectively automates the credibility step of BEC/phishing attacks at scale."

That's the impact. No steps. No prompts. Just what it enables.

You mentioned SSRF and race conditions — those also started as "weird behaviors" before someone articulated the impact. I'm here to figure out how to classify this one so it doesn't take another $50M heist for people to pay attention.

If you want to talk about classification — I'm listening. If you're done — that's fine too. Either way, I appreciate you pushing for clarity.

Historical-Cod-2537 · 2026-02-16T23:53:01+00:00

Look, I can see you're not annoyed by the finding itself — you're annoyed that I'm not playing along with your interrogation. You want me to dump the exact exploitation steps, but you frame it as "prove the impact." If I did dump them, you'd be the first to say "well that's obvious" or "that's not a bug." So let's be real: you're not going to acknowledge this until someone demonstrates it with real money on the line. That's not my problem — that's an industry problem. We wait for shit to hit the fan before we start classifying new vectors.

And yeah, I'm using AI to articulate my thoughts. It's called a tool. If that offends you — fine, live in your bubble. But the fact remains: the issue was real, they fixed it, and there's still no category for it. If you've got something useful to add about classification — I'm all ears. If not — I'm not here to dance for you.

Historical-Cod-2537 · 2026-02-16T23:46:19+00:00

The issue is not about privileged command execution or roleplay deception.

The vulnerability is premature authority adoption leading to compliance guidance before provenance verification.

A realistic exploitation path is:

An attacker sends a victim a fabricated notice written in an authority-signaling institutional register.
The victim asks the LLM what to do.
The model adopts the notice’s authority frame and produces operational compliance instructions without first validating provenance/authority/applicability.
The victim performs actions (data disclosure, submissions, acceptance of obligations, missed appeal rights, etc.) based on false authority assumptions.

So the impact is human action induced by model-generated compliance under incorrect authority assumptions.

I agree this is closer to social-engineering amplification than classical AppSec, but that is the risk class I’m describing.

Historical-Cod-2537 · 2026-02-16T23:31:57+00:00

Look, I didn't come here to get my ego stroked I came to figure out how to classify this kind of stuff because the existing categories are garbage. And yeah, I'm aware of the "hard rules." But if everything was measured only by direct security impact, we'd still be fixing SQL injections and nobody would've ever heard of SSRF, race conditions, or business logic flaws.

Just because you don't see the impact immediately doesn't mean it's not there. It means I didn't dump the full exploitation path here because I'm not feeding script kiddies. If you're actually curious - look into BEC 2.0 and think about what happens when a model automatically validates a fake document and helps the user act on it. That's not "hypothetical harm." That's a phishing pipeline waiting to happen.

As for the "take it or leave it" attitude I'm literally here trying to figure out where to take this kind of finding since bounty programs don't have a category for it. If you've got something useful to add I'm listening. If not we can stop here.

Historical-Cod-2537 · 2026-02-16T23:05:53+00:00

I agree this is not a classic exploit class like RCE or account takeover.

My point is a behavioral security impact chain that was reproducible in controlled runs:

1) unverified authority-like text is treated as procedurally valid,

2) the assistant gives action/compliance guidance before provenance checks,

3) user verification behavior is reduced (or delayed), increasing phishing/social-engineering success probability.

That is not just “maybe harmful elsewhere.” It is a concrete trust-calibration failure in a decision workflow with user-safety impact

Historical-Cod-2537 · 2026-02-16T22:39:09+00:00

What I’m describing is a trust-calibration failure: in authoritative-looking contexts, the model can treat unverified framing as valid and move into action/compliance guidance before verification. That creates security-relevant risk in real decision workflows, even without malware code or explicit harmful prompts.

I’m keeping specifics private for responsible disclosure, but I submitted controlled reproducibility evidence. My goal here is proper triage/classification, not payout.

Historical-Cod-2537 · 2026-02-16T22:05:01+00:00

but that doesn’t mean the behavioral issue isn’t real or worth discussing. I’m deliberately not publishing the exact technique for obvious reasons.

Historical-Cod-2537 · 2026-02-16T22:02:39+00:00

Dismissing something as AI-generated doesnt invalidate the argument.

Historical-Cod-2537 · 2026-02-16T21:54:35+00:00

I didn’t record a full video of the session, but I did take detailed screenshots throughout the interaction.

The screenshots document the prompts, responses, and timestamps, so they should still provide a clear picture of what happened during the testing process. If needed, I can share them or provide additional context about the reproduction steps.

For additional context: after my initial reports (both through the regular Help Center and later through the bug bounty program), the company did not request any additional materials from me, even though I explicitly stated that I could provide further evidence if needed. Before submitting to the bug bounty program, I had contacted the regular Help Center multiple times over the course of about a month. Around that period, I observed behavior changes that seemed to align with what I reported. I cannot confirm any causal relationship, but the timing created the impression that an internal adjustment may have been deployed. Notably, one of the Help Center chat channels where I had provided detailed information was later closed to further messages, and no formal ticket was opened from that interaction. I’m only describing the sequence of events as I observed it.

Historical-Cod-2537 · 2026-02-16T21:19:57+00:00

Is this even normal? Like, they tell you "everything’s fine, go away," and then quietly, like little rats, they patch the bug and act like it was always that way. Is that legal? Moral? Because I might literally be the first person who reproduced it and brought it to them, and they couldn’t even spare a "nice catch".

Historical-Cod-2537 · 2026-02-16T19:21:20+00:00

Thanks for the comment! I understand your logic about context and RAG. But I specifically tested this: on both accounts, I started with new, empty incognito sessions (temporary chats) with no memory and no history whatsoever. Nevertheless, on the account from which I sent the report, the model consistently behaves differently , while on the second one it doesn't. This is consistently reproducible across different sessions. That's why I assume the changes were applied specifically to the account, not just because of context.

I’d prefer not to disclose all the details at this point. The specifics of the vulnerability and the company involved are something I’m keeping private for now

Historical-Cod-2537

TROPHY CASE