3 ms·

> you could find tons of API keys on GitHub before these “agentic” tools too. that was my point, one screenshot from twitter vs one anecdote from me. I don’t th

by rvz 10mo ago

> you could find tons of API keys on GitHub before these “agentic” tools too. that was my point, one screenshot from twitter vs one anecdote from me. I don’t think either proves the point, but posting a screenshot from twitter like it’s proof of some widespread problem is what I was responding to (N=2, 1 vs 1) That doesn't refute the probabilistic nature of LLMs despite best prompting practices. In fact it emphasises it. More like your 1 anecdotal example vs my 20+ examples on GitHub. My point tells you that not only it indeed does happen, but a previous old issue is now made even worse and more widespread, since we now have vibe-coders without security best practices assuming the agent should know better (when it doesn't). > my point is more “skill issue” than “trust me this never happens” So those that have this "skill issue" are also those who are prompting the AI differently then? Either way, this just inadvertently proves my whole point. > yes it’s probabilistic, so is practically everything else at scale. yet it works and here we are. The additional problem is can you explain why it went wrong as you scale the technology? CPUs circuit design go through formal verification and if a fault happens, we know exactly why; hence it is deterministic in design which makes them reliable. LLMs are not and don't have this. Which is why OpenAI had to describe ChatGPT's misaligned behaviour as "sycophancy", but could not explain why it happened other than tweaking the hyper-parameters which got them that result. So LLMs being fundamentally probabilistic and are hence, more unexplainable being the reason why you have the screenshot of vibe-coders who somehow prompted it wrong and the agent committed the keys. Maybe that would never have happened to you, but it won't be the last time we see more of this happening on GitHub.

dkdcio 10mo ago
I was pointing out one screenshot from twitter isn’t proof of anything just to be clear; it’s a silly way to make a point. yes AI makes leaking keys on GH more prevalent, but so what? it’s the same problem as before with roughly the same solution I’m saying neural networks being probabilistic doesn’t matter — everything is probabilistic. you can still practically use the tools to great effect, just like we use everything else that has underlying probabilities OpenAI did not have to describe it as sycophancy, they chose to, and I’d contend it was a stupid choice and yes, you can explain what went wrong just like you can with CPUs. we don’t (usually) talk about quantum-level physics when discussing CPUs; talking about neurons in LLMs is the wrong level of abstraction
- rvz 10mo ago
  > I was pointing out one screenshot from twitter isn’t proof of anything just to be clear; it’s a silly way to make a point. Verses your anecdote being a proof of what? Skill issue for vibe coders? Someone else prompting it wrong? You do realize you are proving my entire point? > yes AI makes leaking keys on GH more prevalent, but so what? it’s the same problem as before with roughly the same solution Again, it exacerbates my point such that it makes the existing issue even worse. Additionally, that wasn't even the only point I made on the subject. > I’m saying neural networks being probabilistic doesn’t matter — everything is probabilistic. When you scale neural networks to become say, production-grade LLMs, then it does matter. Just like it does matter for CPUs to be reliable when you scale them in production-grade data centers. But your earlier (fallacious) comparison ignores the reliability differences between them (CPUs vs LLMs.) and determinism is a hard requirement for that; which the latter, LLMs are not. > OpenAI did not have to describe it as sycophancy, they chose to, and I’d contend it was a stupid choice For the press, they had to, but no-one knows the real reason, because it is unexplainable; going back to my other point on reliability. > and yes, you can explain what went wrong just like you can with CPUs. we don’t (usually) talk about quantum-level physics when discussing CPUs; talking about neurons in LLMs is the wrong level of abstraction It is indeed wrong for LLMs because not even the researchers can practically give an explanation why a single neuron (for every neuron in the network) gives different values on every fine-tune or training run. Even if it is "good enough", it can still go wrong at the inference-level for other unexplainable reasons other than it "overfitted". CPUs on the other hand, have formal verification methods which verify that the CPU conforms to its specification and we can trust that it works as intended and can diagnose the problem accurately without going into atomic-level details.
  - dkdcio 10mo ago
    …what is your point exactly (and concisely)? I’m saying it doesn’t matter it’s probabilistic, everything is, the tech is still useful
    - rvz 10mo ago
      No one is arguing that it isn't useful. The problem is this: > I’m saying it doesn’t matter it’s probabilistic, everything is, Maybe it doesn't matter for you, but it generally does matter. The risk level of a technology failing is far higher if it is more random and unexplainable than if it is expected, verified and explainable. The former eliminates many serious use-cases. This is why your CPU, or GPU works. LLMs are neither deterministic, no formal verification exists and are fundamentally black-boxes. That is why many vibe-coders reported many "AI deleted their entire home folder" issues even when they told it to move a file / folder to another location. If it did not matter, why do you need sandboxes for the agents in the first place?

by rvz 10mo ago

dkdcio 10mo ago

I was pointing out one screenshot from twitter isn’t proof of anything just to be clear; it’s a silly way to make a point. yes AI makes leaking keys on GH more prevalent, but so what? it’s the same problem as before with roughly the same solution I’m saying neural networks being probabilistic doesn’t matter — everything is probabilistic. you can still practically use the tools to great effect, just like we use everything else that has underlying probabilities OpenAI did not have to describe it as sycophancy, they chose to, and I’d contend it was a stupid choice and yes, you can explain what went wrong just like you can with CPUs. we don’t (usually) talk about quantum-level physics when discussing CPUs; talking about neurons in LLMs is the wrong level of abstraction

rvz 10mo ago
> I was pointing out one screenshot from twitter isn’t proof of anything just to be clear; it’s a silly way to make a point. Verses your anecdote being a proof of what? Skill issue for vibe coders? Someone else prompting it wrong? You do realize you are proving my entire point? > yes AI makes leaking keys on GH more prevalent, but so what? it’s the same problem as before with roughly the same solution Again, it exacerbates my point such that it makes the existing issue even worse. Additionally, that wasn't even the only point I made on the subject. > I’m saying neural networks being probabilistic doesn’t matter — everything is probabilistic. When you scale neural networks to become say, production-grade LLMs, then it does matter. Just like it does matter for CPUs to be reliable when you scale them in production-grade data centers. But your earlier (fallacious) comparison ignores the reliability differences between them (CPUs vs LLMs.) and determinism is a hard requirement for that; which the latter, LLMs are not. > OpenAI did not have to describe it as sycophancy, they chose to, and I’d contend it was a stupid choice For the press, they had to, but no-one knows the real reason, because it is unexplainable; going back to my other point on reliability. > and yes, you can explain what went wrong just like you can with CPUs. we don’t (usually) talk about quantum-level physics when discussing CPUs; talking about neurons in LLMs is the wrong level of abstraction It is indeed wrong for LLMs because not even the researchers can practically give an explanation why a single neuron (for every neuron in the network) gives different values on every fine-tune or training run. Even if it is "good enough", it can still go wrong at the inference-level for other unexplainable reasons other than it "overfitted". CPUs on the other hand, have formal verification methods which verify that the CPU conforms to its specification and we can trust that it works as intended and can diagnose the problem accurately without going into atomic-level details.
- dkdcio 10mo ago
  …what is your point exactly (and concisely)? I’m saying it doesn’t matter it’s probabilistic, everything is, the tech is still useful
  - rvz 10mo ago
    No one is arguing that it isn't useful. The problem is this: > I’m saying it doesn’t matter it’s probabilistic, everything is, Maybe it doesn't matter for you, but it generally does matter. The risk level of a technology failing is far higher if it is more random and unexplainable than if it is expected, verified and explainable. The former eliminates many serious use-cases. This is why your CPU, or GPU works. LLMs are neither deterministic, no formal verification exists and are fundamentally black-boxes. That is why many vibe-coders reported many "AI deleted their entire home folder" issues even when they told it to move a file / folder to another location. If it did not matter, why do you need sandboxes for the agents in the first place?