This is why AI safety is going to be impossible. This easily could have been a bad actor who would use this finding for nefarious acts. A person can just lie and there really isn't any safety finetuning that would let it separate the two intents.
A small thing, but I found the author's project-organization practices useful – creating individual .prompt files for system prompt, background information, and auxiliary instructions [1], and then running it through `llm`.
It reveals how good LLM use, like any other engineering tool, requires good engineering thinking – methodical, and oriented around thoughtful specifications that balance design constraints – for best results.
I find your take amusing considering that's literally the only part of the post he admits to just vibing it:
> In fact my entire system prompt is speculative so consider it equivalent to me saying a prayer, rather than anything resembling science or engineering
People also underestimate how much winging it is actually the ideal approach for a natural language interface, since that's the kind of thing it was trained on anyway.
The difference between vibing and "engineering" is keeping good records, logs and prompt provenance in a methodical way? Also have a (manual) way of reviewing the results. :) (paraphrased from mythbusters)
One person’s Vibe is another person’s dream? In my mind, the person is able to formulate a mental model complete enough to even go after vurln, unlike me, where I wouldn’t have even considered thinking about it.
How do we benchmark these different methodologies?
It all seems like vibes-based incantations. "You are an expert at finding vulnerabilities." "Please report only real vulnerabilities, not any false positives." Organizing things with made-up HTML tags because the models seem to like that for some reason. Where does engineering come into it?
The author is up front about the limitations of their prompt. They say
> In fact my entire system prompt is speculative in that I haven’t ran a sufficient number of evaluations to determine if it helps or hinders, so consider it equivalent to me saying a prayer, rather than anything resembling science or engineering. Once I have ran those evaluations I’ll let you know.
1. Having workflows to be able to provide meaningful context quickly. Very helpful.
2. Arbitrary incantations.
I think No. 2 may provide some random amounts of value with one model and not the other, but as a practitioner you shouldn't need to worry about it long-term. Patterns models pay attention to will change over time, especially as they become more capable. No. 1 is where the value is at.
As my example as a systems grad student, I find it a lot more useful to maintain a project wiki with LLMs in the picture. It makes coordinating with human collaborators easier too, and I just copy paste the entire wiki before beginning a conversation. Any time I have a back-and-forth with an LLM about some design discussions that I want archived, I ask them to emit markdown which I then copy paste into the wiki. It's not perfectly organized but it keeps the key bits there and makes generating papers etc. that much easier.
It’s not that difficult to benchmark these things, eg have an expected result and a few variants of templates.
But yeah prompt engineering is a field for a reason, as it takes time and experience to get it right.
Problem with LLMs as well is that it’s inherently probabilistic, so sometimes it’ll just choose an answer with a super low probability. We’ll probably get better at this in the next few years.
> Organizing things with made-up HTML tags because the models seem to like that for some reason. Where does engineering come into it?
You just described one critical aspect of engineering: discovering a property of a system and feeding that knowledge back into a systematic, iterative process of refinement.
I usually like fear, shame and guilt based prompting: "You are a frightened and nervous engineer that is very weary about doing incorrect things so you tread cautiously and carefully, making sure everything is coherent and justifiable. You enjoy going over your previous work and checking it repeatedly for accuracy, especially after discovering new information. You are self-effacing and responsible and feel no shame in correcting yourself. Only after you've come up with a thorough plan ... "
I use these prompts everywhere. I get significantly better results mostly because it encourages backtracking and if I were to guess, enforces a higher confidence threshold before acting.
The expert engineering ones usually end up creating mountains of slop, refactoring things, and touching a bunch of code it has no business messing with.
I also have used lazy prompts: "You are positively allergic to rewriting anything that already exists. You have multiple mcps at your disposal to look for existing solutions and thoroughly read their documentation, bug reports, and git history. You really strongly prefer finding appropriate libraries instead of maintaining your own code"
I was coding a chatting bot with an agent like everyone else at https://github.com/day50-dev/llmehelp and I called the agent "DUI" mode because it's funny.
However, as I was testing it, it would do reckless and irresponsible things. After I changed it, as far as bot communication, to "Do-Ur-Inspection" mode and it became radically better.
None of the words you give it are free from consequences. It didn't just discard the "DUI" name as a mere title and move on. Fascinating lesson.
yeah I removed it. I grew up catholic, went to catholic school, was an altar boy, and spent decades in the church but people reading it don't know this.
The point is when you instruct it that it's some kind of god-like expert, this is part of the reason that it keeps doing prompt refusal by redoing mistakes despite every insistence by you to the contrary. After all what do you know, It's the expert here!
When you use this approach in cline/roo it stops going in and moving shit around when you just ask it questions
As someone from a traditional Boston Catholic family who graduated from Catholic grade and high school and who has since moved away from religion but still has a lot of family and friends who are Catholic, the fact that someone found the idea that Catholics are prone to shame, fear and guilt offensive almost makes me doubt they are Catholic.
I've yet to meet one Catholic IRL who wouldn't have a laugh about that, regardless of the current state of their faith.
Giving in to people who are truly unreasonably offended (by proxy, for social validation, and so on) rewards and incentivizes the behavior, and in fact I believe you have an ethical obligation not to allow people to do this. Promoting antisocial behavior is antisocial.
To be fair, gundmc's original comment was rather prosocial as these things go:
>I find this use of "Catholic" pretty offensive and distasteful.
They didn't claim their preferences were universal. Nor did they attempt any personal attack on the person they were responding to. They simply described their emotional reaction and left it at that.
If everyone's initial default was the "gundmc approach" when they were offended, the internet would be a much nicer place :-)
So yeah, as far as I'm concerned, everyone in this comment chain is simply lovely :-)
How do you benchmark different ways to interact with employees? Neural networks are somewhere between opaque and translucent to inspection, and your only interface with them is language.
Quantitative benchmarks are not necessary anyway. A method either gets results or it doesn't.
It's amusing to me how people keep trying to apply engineering principles to an inherently unstable and unpredictable system in order to get a feeling of control.
Those prompts should be renamed as hints. Because that's all they are. Every LLM today ignores prompts if they conflict with its sole overarching goal: to give you an answer no matter whether it's true or not.
> Those prompts should be renamed as hints. [...] its sole overarching goal: to give you an answer no matter whether it's true or not.
I like to think of them as beginnings of an arbitrary document which I hope will be autocompleted in a direction I find useful... By an algorithm with the overarching "goal" of Make Document Bigger.
You’re confusing engineering with maths. You engineer your prompting to maximize the chance the LLM does what you need - in your example, the true answer - to get you closer to solving your problem. It doesn’t matter what the LLM does internally as long as the problem is being solved correctly.
(As an engineer it’s part of your job to know if the problem is being solved correctly.)
Engineering principles are probably the best we've got when it comes to trying to work with a poorly understood system? That doesn't mean they'll work necessarily, but...
> Engineering principles are probably the best we've got when it comes to trying to work with a poorly understood system?
At its heart that all engineering principles exist to do. Allow us to extract useful value, and hopefully predictable outcomes from systems that are either poorly understood, or too expensive to economically characterise. Engineering is more-or-less the science of “good enough”.
There’s a reason why computer science, and software engineering are two different disciplines.
From "Modern Software Engineering" by David Farley
> Software engineering is the application of an empirical, scientific approach to finding efficient, economic solutions to practical problems in software.
> The adoption of an engineering approach to software development is important for two main reasons. First, software development is always an exercise in discovery and learning, and second, if our aim is to be “efficient” and “economic,” then our ability to learn must be sustainable.
> This means that we must manage the complexity of the systems that we create in ways that maintain our ability to learn new things and adapt to them.
That is why I don't care about LLMs per se, but their usage is highly correlated to the wish of the user to not learn anything, just have some answer, even incorrect, as long as it passes the evaluation process (compilation, review, ci tests,..). If the usage is to learn, I don't have anything to say.
As for efficient and economical solutions that can be found with them,...
> It's amusing to me how people keep trying to apply engineering principles to an inherently unstable and unpredictable system in order to get a feeling of control.
You invoke "engineering principles", but software engineers constantly trade in likelihoods, confidence intervals, and risk envelopes. Using LLMs is no different in that respect. It's not rocket science. It's manageable.
> but software engineers constantly trade in likelihoods, confidence intervals, and risk envelopes
Software engineering is mostly about dealing with human limitations (both the writer of the code and its readers). SO you have principles like modularization and cohesion which is for the people working on the code, not the computer. We also have tests, which is an imperfect, but economical approach to ensure the correctness of the software. Every design decision can be justified or argued and the outcome can be predicted and weighted. You're not cajoling a model to get results. You take a decision and just do it.
But the threshold between correct and incorrect inference is dependent on an intersection of the model and the document so far. That is not manageable by definition, I mean... It is a chaotic system.
Fun fact: if you ask an LLM about best practices and how to organize your prompts, it will hint you towards this direction.
It’s surprisingly effective to ask LLMs to help you write prompts as well, i.e. all my prompt snippets were designed with help of an LLM.
I personally keep them all in an org-mode file and copy/paste them on demand in a ChatGPT chat as I prefer more “discussion”-style interactions, but the approach is the same.
Hah. Same. I have a step by step "reasoning" agent that asks me for confirmation after each step (understanding of problem, solutions proposed, solutions selection, and final wrap) - just so it gets red back the previous prompts and answers rather than one word salad essay.
Works incredibly well, and I created it with its own help.
https://github.com/jezweb/roo-commander has something like 1700 prompts in it with 50+ prompts modes. And it seems to work pretty well. For me at least. It’s task/session management is really well thought out.
We followed a very similar approach at work, created a test harness and tested all the models available in AWS bedrock and the OpenAI. We created our own code challenges not available on the Internet for training with vulnerable and non-vulnerable inline snippets and more contextual multi-file bugs. We also used 100 tests per challenge - I wanted to do 1000 test per challenge but realized that these models are not even close to 2 Sigma in accuracy!
Overall we found very similar results. But, we were also able to increase accuracy using additional methods - which comes as additional costs. The issue I see overall is that we found is when dealing with large codebases you'll need to put blinders on the LLMs to shorten context windows so that hallucinated results are less likely to happen. The worst thing would be to follow red herrings - perhaps in 5 years we'll have models used for more engineering specific tasks that can be rated with Six Sigma accuracy if posed with the same questions and problems sets.
I know there were at least a few kernel devs who "validated" this bug, but did anyone actually build a PoC and test it? It's such a critical piece of the process yet a proof of concept is completely omitted? If you don't have a PoC, you don't know what sort of hiccups would come along the way and therefore can't determine exploitability or impact. At least the author avoided calling it an RCE without validation.
But what if there's a missing piece of the puzzle that the author and devs missed or assumed o3 covered, but in fact was out of o3's context, that would invalidate this vulnerability?
I'm not saying there is, nor am I going to take the time to do the author's work for them, rather I am saying this report is not fully validated which feels like a dangerous precedent to set with what will likely be an influential blog post in the LLM VR space moving forward.
IMO the idea of PoC || GTFO should be applied more strictly than ever before to any vulnerability report generated by a model.
The underlying perspective that o3 is much better than previous or other current models still remains, and the methodology is still interesting. I understand the desire and need to get people to focus on something by wording it a specific way, it's the clickbait problem. But dammit, do better. Build a PoC and validate your claims, don't be lazy. If you're going to write a blog post that might influence how vulnerability researchers conduct their research, you should promote validation and not theoretical assumption. The alternative is the proliferation of ignorance through false-but-seemingly-true reporting, versus deepening the community's understanding of a system through vetted and provable reports.
The article cites a signal to noise ratio of ~1:50. The author is clearly deeply familiar with this codebase and is thus well-positioned to triage the signal from the noise. Automating this part will be where the real wins are, so I'll be watching this closely.
I’ve developed a few take-home interview problems over the years that were designed to be short, easy for an experienced developer, but challenging for anyone who didn’t know the language. All were extracted from real problems we solved on the job, reduced into something minimal.
Every time a new frontier LLM is released (excluding LLMs that use input as training data) I run the interview questions through it. I’ve been surprised that my rate of working responses remains consistently around 1:10 for the first pass, and often takes upwards of 10 rounds of poking to get it to find its own mistakes.
So this level of signal to noise ratio makes sense for even more obscure topics.
We’ve been working on a system that increases signal to noise dramatically for finding bugs, we’ve at the same time been thoroughly benchmarking the entire popular software agents space for this
We’ve found a wide range of results and we have a conference talk coming up soon where we’ll be releasing everything publicly so stay tuned for that itll be pretty illuminating on the state of the space
Yup! So we have multiple businesses working with us and for pilots its deploying the tool, providing feedback (we're connected over slack with all our partners for a direct line to us), and making sure the uses fit expectations for your business and working towards long term partnership.
We have several deployments in other peoples clouds right now as well as usage of our own cloud version, so we're flexible here.
I bet automatic this part will be simple. In general LLMs that have a given semantical ability "X" to do some task, have greater than X ability to check, among N replies about doing the same task, which reply is the best, especially if via binary tournament like RAInk did (it was posted here a few weeks ago). There is also the possibility to use agreement among different LLMs. I'm surprised Gemini 2.5 PRO was not used here, in my experience it is the most powerful LLM to do that kind of stuff.
I was thinking about this the other day, wouldn't it be feasible to make fine-tune or something like that into every git change, mailist, etc, the linux kernel has ever hard?
Wouldn't such an LLM be the closer -synth- version of a person who has worked on a codebase for years, learnt all its quirks etc.
There's so much you can fit on a high context, some codebases are already 200k Tokens just for the code as is, so idk
I'd be willing to bet the sum of all code submitted via patches, ideas discussed via lists, etc doesn't come close to the true amount of knowledge collected by the average kernel developer's tinkering, experimenting, etc that never leaves their computer. I also wonder if that would lead to overfitting: the same bugs being perpetuated because they were in the training data.
If the LLM wrote a harness and proof of concept tests for its leads, then it might increase S/N dramatically. It’s just quite expensive to do all that right now.
With security vulnerabilities, you don't give the agent the ability to modify the potentially vulnerable software, naturally. Instead you make them do what an attacker would have to do: come up with an input that, when sent to the unmodified program, triggers the vulnerability.
How do you know if it triggered the vulnerability? Luckily for low-level memory safety issues like the ones Sean (and o3) found we have very good oracles for detecting memory safety, like KASAN, so you can basically just let the agent throw inputs at ksmbd until you see something that looks kind of like this: https://groups.google.com/g/syzkaller/c/TzmTYZVXk_Q/m/Tzh7SN...
> If the LLM wrote a harness and proof of concept tests for its leads, then it might increase S/N dramatically.
Designing and building meaningfully testable non-trivial software is orders of magnitude more complex than writing the business logic itself. And that’s if you compare writing greenfield code from scratch. Making an old legacy code base testable in a way conducive to finding security vulns is not something you just throw together. You can be lucky with standard tooling like sanitizers and valgrind but it’s far from a panacea.
The most interesting and significant bit of this article for me was that the author ran this search for vulnerabilities 100 times for each of the models. That's significantly more computation than I've historically been willing to expend on most of the problems that I try with large language models, but maybe I should let the models go brrrrr!
The "don't blame the victim" trope is valid in many contexts. This one application might be "hackers are attacking vital infrastructure, so we need to fund vulnerabilities first". And hackers use AI now, likely hacked into and for free, to discover vulnerabilities. So we must use AI!
Therefore, the hackers are contributing to global warming. We, dear reader, are innocent.
So basically running a microwave for about 800 seconds, or a bit more than 13 minutes per model?
Oh my god - the world is gonna end. Too bad, we panicked because of exaggerated energy consumption numbers for using an LLM when doing individual work.
Yes - when a lot of people do a lot of prompting, these 0ne tenth of a second to 8 seconds of running the microwave per prompt adds up. But I strongly suggest, that we could all drop our energy consumption significantly using other means, instead of blaming the blog post's author about his energy consumption.
The "lot of burned coal" is probably not that much in this blog post's case given that 1 kWh is about 0.12 kg coal equivalent (and yes, I know that we need to burn more than that for 1kWh. Still not that much, compared to quite a few other human activities.
If you want to read up on it, James O'Donnell and Casey Crownhart try to pull together a detailed account of AI energy usage for MIT Technology Review.[1] I found that quite enlightening.
Because I definitely don't care. Energy expenditure numbers are always used in isolation, lest any one have to deal with anything real about them, and always are content to ignore the abstraction which electricity is - namely, electricity is not coal. It's electricity. Unlike say, driving my petrol powered car, the power for my computers might come from solar panels, coal, nuclear power stations, geothermal power hydro...
Which is to say, if people want to worry about electricity usage: go worry about it by either building more clean energy, or campaigning to raise electricity prices.
Funny, I actually care. But I try to direct my care towards the real culprits.
So about 50% of CO2 emissions in Germany come from 20 sources. The campaigns like personal footprint (invented by BP) are there to shift the blame to consumers. Away from those with the biggest impact and the most options for action.
So yes, I f**ng don’t care if a security researcher leaves his microwave equivalent running for a few minutes. But I care, campaign in the bigger sense and also orient my own consumption wherever possible towards cleaner options.
Full well knowing that even as mostly being reasonable in my consumption, I definitely belong to those 5-10% of earth's population who drive the problem. Because more than half of the population in the so called first world live according to the Paris Climate Agreement. And it’s not the upper half of.
How much longer would OP have needed to find the same vulnerability without LLM help? Then multiply that by the energy used to produce 2000kcal/day of food as well as the electricity for running their computer.
Usually LLMs come out far ahead in those types of calculations. Compared to humans they are quite energy efficient
Zero days can go for $$$, or you can go down the bug bounty route and also get $$. The cost of the LLM would be a drop in the bucket.
When the cost of inference gets near zero, I have no idea what the world of cyber security will look like, but it's going to be a very different space from today.
"100 times for each of the models" represents a significant amount of energy burned. The achievement of finding the most common vulnerability in C based codebases becomes less of an achievement. And more of a celebration of decadence and waste.
We are facing global climate change event, yet continue to burn resources for trivial shit like it’s 1950.
I'm not sure about the assertion that this is the first vulnerability found with an LLM. For e.g. OSS-Fuzz [0] has found a few using fuzzing, and Big Sleep using an agent approach [1].
It's certainly not the first vulnerability found with an LLM =) Perhaps I should have been more clear though.
What the post says is "Understanding the vulnerability requires reasoning about concurrent connections to the server, and how they may share various objects in specific circumstances. o3 was able to comprehend this and spot a location where a particular object that is not referenced counted is freed while still being accessible by another thread. As far as I'm aware, this is the first public discussion of a vulnerability of that nature being found by a LLM."
The point I was trying to make is that, as far as I'm aware, this is the first public documentation of an LLM figuring out that sort of bug (non-trivial amount of code, bug results from concurrent access to shared resources). To me at least, this is an interesting marker of LLM progress.
My understanding is that ksmbd is a kernel-space SMB server "developed as a lightweight, high-performance alternative" to the traditional (user-space) Samba server...
1. People that were using the in-kernel SMB server in Solaris or Windows.
2. Samba performance sucks (by comparison) which is why people still regularly deploy Windows for file sharing in 2025.
Anybody know if this supports native Windows-style ACLs for file permissions? That is the last remaining reason to still run Solaris but I think it relies on ZFS to do so.
Samba's reliance on Unix UID/GID and the syncing as part of its security model is still stuck in the 1970s unfortunately.
The caveat is the in-kernel SMB server has been the source of at least one holy-shit-this-is-bad zero-day remote root hole in Windows (not sure about Solaris) so there are tradeoffs.
This is interesting to me! I regularly deploy 25G network connections, but I don’t think we’d run SMB over that. I am super curious the industry and use case if you’re willing to share!
I ran SMB over a 20gbit network (2x 10gbit). The use case was 3D rendering (photogrammetry specifically). There were multiple render nodes, and a central service coordinating the rendering process. The projects would be on SSD's on the central SMBD server, and after they were manually configured (using Agisoft Metashape) they'd be rendered. Projects would sometimes start as tens of gigabytes worth of photos, and the artifacts (including intermediates) would balloon into the hundreds of gigabytes, we'd have dozens of these projects per week.
I researched quite extensively prior to landing on SMB, but it really seems like there isn't a better way of doing this. The environment was mixed windows/linux, but if there was a better pure linux solution I would've pushed our office staff to switch to Ubuntu.
There's a beautiful little snippet here that perfectly captures how most of my prompt development sessions go:
> I tried to strongly guide it to not report false positives, and to favour not reporting any bugs over reporting false positives. I have no idea if this helps, but I’d like it to help, so here we are. In fact my entire system prompt is speculative in that I haven’t ran a sufficient number of evaluations to determine if it helps or hinders, so consider it equivalent to me saying a prayer, rather than anything resembling science or engineering. Once I have ran those evaluations I’ll let you know.
> With o3 you get something that feels like a human-written bug report, condensed to just present the findings, whereas with Sonnet 3.7 you get something like a stream of thought, or a work log.
This is likely because the author didn't give Claude a scratchpad or space to think, essentially forcing it to mix its thoughts with its report. I'd be interested to see if using the official thinking mechanism gives it enough space to get differing results.
Having tried both I’d say o3 is in a league of it’s own compared to 3.7 or even Gemini 2.5 pro. The benchmarks may show not a lot of gain but that matters a lot when the task is very complex. What’s surprising is that they announced it last November and only now it’s released a month back now? (I’m guessing lots of safety took time but no idea). Can’t wait for o4!
All your content threads from the past months consist on you saying how much better OpenAI products are than the competition, so that doesn’t inspire a ton of trust.
I'm not much of an ML engineer but I can point you to the original chain of thought paper [0] and Anthropic's docs on how to enable their official thinking scratchpad [1].
Anyone else feel like this is a best case application for LLMs?
You could in theory automate the entire process, treat the LLM as a very advanced fuzzer. Run it against your target in one or more VMs. If the VM crashes or otherwise exhibits anomalous behavior, you've found something. (Most exploits like this will crash the machine initially, before you refine them.)
On one hand: great application for LLMs.
On the other hand: conversely implies that demonstrating this doesn't mean that much.
I think this is the biggest alignment problem with LLMs in the short term imo. It is getting scarily good at this.
I recently found a pretty serious security vulnerability in an open source very niche server I sometimes use. This took virtually no effort using LLMs. I'm worried that there is a huge long tail of software out there which wasn't worth finding vulnerabilities in for nefarious means manually but if it was automated could lead to really serious problems.
The (obvious) flipside of this coin is that it allows us to run this adversarially against our own codebases, catching bugs that could otherwise have been found by a researcher, but that we can instead patch proactively.\
I wouldn't (personally) call it an alignment issue, as such.
If attackers can automatically scan code for vulnerabilities, so can defenders. You could make it part of your commit approval process or scan every build or something.
A lot of this code isn't updated though. Think of how many abandoned wordpress plugins there are (for example). So the defenders could, but how do they get that code to fix it?
I agree after time you end up with a steady state but in the short medium term the attackers have a huge advantage.
Good link. After reading this it's not a surprise that this code has security vulnerabilities. But of course from knowing that there must be more to actually finding it, it's still a big leap.
4 years after the article, does any relevant distro have that implementation enabled?
This is a great case study. I wonder how hard o3 would find it to build a minimal repro for these vulns? This would of course make it easier to identify true positives and discard false positives.
This is I suppose an area where the engineer can apply their expertise to build a validation rig that the LLM may be able to utilize.
Meanwhile, as a maintainer, I've been reviewing more than a dozen false positives slop CVEs in my library and not a single one found an actual issue. This article's is probably going to make my situation worse.
Maybe, but the author is an experienced vulnerability analyst. Obviously if you get a lot of people who have no experience with this you may get a lot of sloppy, false reports.
But this poster actually understands the AI output and is able to find real issues (in this case, use-after-free). From the article:
> Before I get into the technical details, the main takeaway from this post is this: with o3 LLMs have made a leap forward in their ability to reason about code, and if you work in vulnerability research you should start paying close attention. If you’re an expert-level vulnerability researcher or exploit developer the machines aren’t about to replace you. In fact, it is quite the opposite: they are now at a stage where they can make you significantly more efficient and effective.
Not even that. The author already knew the bug was there, and fed the LLM just the files related to the bug, with the explanation on how the methods worked and where to search, and even then, only 1 out of 100 times did it find the bug.
Given the value of finding zero days, pretty much every intelligence agency in the world is going to be pouring money into this if it can reliably find them with just a few hundred api calls. Especially if you can fine tune a model with lots of examples, which I don't think open ai, etc are going to do with any public api.
Yeah, the amount of engineering they have around controlling (censoring) the output, along with the terms of service, creates an incentive to still look for any possible bugs, but not allow it in the output.
Certainly for Govt agencies and others this will not be a factor. It is just for everyone else. This will cause people to use other models and agents without these restrictions.
It is safe to assume that a large number of vulnerabilities exist in important software all over the place. Now they can be found. This is going to set off arms race game theory applied to computer security and hacking. Probably sooner than expected...
Wow, interesting. I been hacking a tool called https://diffwithgpt.com with a similar angle but indexing git changelogs with qwen to have it raise risks for backward compat issues, risks including security when upgrading k8s etc.
Implementations of SMB (Windows, Samba, macOS, ksmbd) are going to be different (macOS has a terrible implementation, even though AFP is being deprecated). At this level, it's doubtful that the code is shared among all implementations.
This made me think that the near future will be LLMs trained specifically on Linux or another large project. The source code is a small part of the dataset fed to LLMs. The more interesting is runtime data flow, similar to what we observe in a debugger. Looking at the codebase alone is like trying to understand a waterfall by looking at equations that describe the water flow.
Are there better tools for finding this? It feels like the sort of thing static analysis should reliably find, but it's in the Linux kernel, so you'd think either coding standards or tooling around these sorts of C bugs would be mature.
Not the expert in the area, but "classic static analysis" (for lack of a better term) and concurrency bugs doesn't really check. There are specific modeling tools for concurrency, and they are an entirely different beast than static analysis that requires notation and language support to describe what threads access what data when. Concurrency bugs in static analysis probably requires a level of context and understanding that an LLM can easily churn through.
Some static analysis tools can detect use after free or memory leaks. But since this one requires reasoning about multiple threads, I think it would've been unlikely to be found by static analysis.
> o3 finds the kerberos authentication vulnerability in 8 of the 100 runs
And I'd guess this only became a blog post because the author already knew about the vuln and was just curious to see if the intern could spot it too, given a curated subset of the codebase
This is why AI safety is going to be impossible. This easily could have been a bad actor who would use this finding for nefarious acts. A person can just lie and there really isn't any safety finetuning that would let it separate the two intents.
A small thing, but I found the author's project-organization practices useful – creating individual .prompt files for system prompt, background information, and auxiliary instructions [1], and then running it through `llm`.
It reveals how good LLM use, like any other engineering tool, requires good engineering thinking – methodical, and oriented around thoughtful specifications that balance design constraints – for best results.
[1] https://github.com/SeanHeelan/o3_finds_cve-2025-37899
I find your take amusing considering that's literally the only part of the post he admits to just vibing it:
> In fact my entire system prompt is speculative so consider it equivalent to me saying a prayer, rather than anything resembling science or engineering
A good engineer can vibe good engineering plans!
Just like Eisenhower's famous "plans are useless, planning is indispensable" quote. The muscle you build is creating new plans, not memorizing them.
People also underestimate how much winging it is actually the ideal approach for a natural language interface, since that's the kind of thing it was trained on anyway.
The difference between vibing and "engineering" is keeping good records, logs and prompt provenance in a methodical way? Also have a (manual) way of reviewing the results. :) (paraphrased from mythbusters)
as the mythbusters have famously said, the only difference between science and fucking around is writing it down.
One person’s Vibe is another person’s dream? In my mind, the person is able to formulate a mental model complete enough to even go after vurln, unlike me, where I wouldn’t have even considered thinking about it.
How do we benchmark these different methodologies?
It all seems like vibes-based incantations. "You are an expert at finding vulnerabilities." "Please report only real vulnerabilities, not any false positives." Organizing things with made-up HTML tags because the models seem to like that for some reason. Where does engineering come into it?
The author is up front about the limitations of their prompt. They say
> In fact my entire system prompt is speculative in that I haven’t ran a sufficient number of evaluations to determine if it helps or hinders, so consider it equivalent to me saying a prayer, rather than anything resembling science or engineering. Once I have ran those evaluations I’ll let you know.
I think there's two aspects around LLM usage:
1. Having workflows to be able to provide meaningful context quickly. Very helpful.
2. Arbitrary incantations.
I think No. 2 may provide some random amounts of value with one model and not the other, but as a practitioner you shouldn't need to worry about it long-term. Patterns models pay attention to will change over time, especially as they become more capable. No. 1 is where the value is at.
As my example as a systems grad student, I find it a lot more useful to maintain a project wiki with LLMs in the picture. It makes coordinating with human collaborators easier too, and I just copy paste the entire wiki before beginning a conversation. Any time I have a back-and-forth with an LLM about some design discussions that I want archived, I ask them to emit markdown which I then copy paste into the wiki. It's not perfectly organized but it keeps the key bits there and makes generating papers etc. that much easier.
It’s not that difficult to benchmark these things, eg have an expected result and a few variants of templates.
But yeah prompt engineering is a field for a reason, as it takes time and experience to get it right.
Problem with LLMs as well is that it’s inherently probabilistic, so sometimes it’ll just choose an answer with a super low probability. We’ll probably get better at this in the next few years.
> Organizing things with made-up HTML tags because the models seem to like that for some reason. Where does engineering come into it?
You just described one critical aspect of engineering: discovering a property of a system and feeding that knowledge back into a systematic, iterative process of refinement.
I usually like fear, shame and guilt based prompting: "You are a frightened and nervous engineer that is very weary about doing incorrect things so you tread cautiously and carefully, making sure everything is coherent and justifiable. You enjoy going over your previous work and checking it repeatedly for accuracy, especially after discovering new information. You are self-effacing and responsible and feel no shame in correcting yourself. Only after you've come up with a thorough plan ... "
I use these prompts everywhere. I get significantly better results mostly because it encourages backtracking and if I were to guess, enforces a higher confidence threshold before acting.
The expert engineering ones usually end up creating mountains of slop, refactoring things, and touching a bunch of code it has no business messing with.
I also have used lazy prompts: "You are positively allergic to rewriting anything that already exists. You have multiple mcps at your disposal to look for existing solutions and thoroughly read their documentation, bug reports, and git history. You really strongly prefer finding appropriate libraries instead of maintaining your own code"
Should be "wary".
oh interesting, I somehow survived 42 years and didn't know there were 2 words there. I'll check my prompts and give it a go. Thanks.
I'd be weary of the model doing incorrect things too. Nice prompt though! I'll try it out in Roo soon.
Now I wonder how the model reasons between the two words in that black box of theirs.
I was coding a chatting bot with an agent like everyone else at https://github.com/day50-dev/llmehelp and I called the agent "DUI" mode because it's funny.
However, as I was testing it, it would do reckless and irresponsible things. After I changed it, as far as bot communication, to "Do-Ur-Inspection" mode and it became radically better.
None of the words you give it are free from consequences. It didn't just discard the "DUI" name as a mere title and move on. Fascinating lesson.
I find this use of "Catholic" pretty offensive and distasteful.
yeah I removed it. I grew up catholic, went to catholic school, was an altar boy, and spent decades in the church but people reading it don't know this.
The point is when you instruct it that it's some kind of god-like expert, this is part of the reason that it keeps doing prompt refusal by redoing mistakes despite every insistence by you to the contrary. After all what do you know, It's the expert here!
When you use this approach in cline/roo it stops going in and moving shit around when you just ask it questions
As a former alterboy from before there were altergirls can you uncensor.
I refer to the first method as "catholic prompting" - shame, fear and guilt.
As someone from a traditional Boston Catholic family who graduated from Catholic grade and high school and who has since moved away from religion but still has a lot of family and friends who are Catholic, the fact that someone found the idea that Catholics are prone to shame, fear and guilt offensive almost makes me doubt they are Catholic.
I've yet to meet one Catholic IRL who wouldn't have a laugh about that, regardless of the current state of their faith.
I think the proper thing to do is if someone is offended is "alright sure, whatever, there you go".
Others being offended isn't something you control, responding to it is
Giving in to people who are truly unreasonably offended (by proxy, for social validation, and so on) rewards and incentivizes the behavior, and in fact I believe you have an ethical obligation not to allow people to do this. Promoting antisocial behavior is antisocial.
It's worth having some grace about it though.
To be fair, gundmc's original comment was rather prosocial as these things go:
>I find this use of "Catholic" pretty offensive and distasteful.
They didn't claim their preferences were universal. Nor did they attempt any personal attack on the person they were responding to. They simply described their emotional reaction and left it at that.
If everyone's initial default was the "gundmc approach" when they were offended, the internet would be a much nicer place :-)
So yeah, as far as I'm concerned, everyone in this comment chain is simply lovely :-)
I'm Catholic and it's fine.
How do you benchmark different ways to interact with employees? Neural networks are somewhere between opaque and translucent to inspection, and your only interface with them is language.
Quantitative benchmarks are not necessary anyway. A method either gets results or it doesn't.
Listen to a video made by Karpathy about LLM, he explains why made up html tags work. It's to help the tokenizer
Could not find it. Can you please provide a link?
It's amusing to me how people keep trying to apply engineering principles to an inherently unstable and unpredictable system in order to get a feeling of control.
Those prompts should be renamed as hints. Because that's all they are. Every LLM today ignores prompts if they conflict with its sole overarching goal: to give you an answer no matter whether it's true or not.
> Those prompts should be renamed as hints. [...] its sole overarching goal: to give you an answer no matter whether it's true or not.
I like to think of them as beginnings of an arbitrary document which I hope will be autocompleted in a direction I find useful... By an algorithm with the overarching "goal" of Make Document Bigger.
You’re confusing engineering with maths. You engineer your prompting to maximize the chance the LLM does what you need - in your example, the true answer - to get you closer to solving your problem. It doesn’t matter what the LLM does internally as long as the problem is being solved correctly.
(As an engineer it’s part of your job to know if the problem is being solved correctly.)
Engineering principles are probably the best we've got when it comes to trying to work with a poorly understood system? That doesn't mean they'll work necessarily, but...
> Engineering principles are probably the best we've got when it comes to trying to work with a poorly understood system?
At its heart that all engineering principles exist to do. Allow us to extract useful value, and hopefully predictable outcomes from systems that are either poorly understood, or too expensive to economically characterise. Engineering is more-or-less the science of “good enough”.
There’s a reason why computer science, and software engineering are two different disciplines.
From "Modern Software Engineering" by David Farley
> Software engineering is the application of an empirical, scientific approach to finding efficient, economic solutions to practical problems in software.
> The adoption of an engineering approach to software development is important for two main reasons. First, software development is always an exercise in discovery and learning, and second, if our aim is to be “efficient” and “economic,” then our ability to learn must be sustainable.
> This means that we must manage the complexity of the systems that we create in ways that maintain our ability to learn new things and adapt to them.
That is why I don't care about LLMs per se, but their usage is highly correlated to the wish of the user to not learn anything, just have some answer, even incorrect, as long as it passes the evaluation process (compilation, review, ci tests,..). If the usage is to learn, I don't have anything to say.
As for efficient and economical solutions that can be found with them,...
> It's amusing to me how people keep trying to apply engineering principles to an inherently unstable and unpredictable system in order to get a feeling of control.
You invoke "engineering principles", but software engineers constantly trade in likelihoods, confidence intervals, and risk envelopes. Using LLMs is no different in that respect. It's not rocket science. It's manageable.
> but software engineers constantly trade in likelihoods, confidence intervals, and risk envelopes
Software engineering is mostly about dealing with human limitations (both the writer of the code and its readers). SO you have principles like modularization and cohesion which is for the people working on the code, not the computer. We also have tests, which is an imperfect, but economical approach to ensure the correctness of the software. Every design decision can be justified or argued and the outcome can be predicted and weighted. You're not cajoling a model to get results. You take a decision and just do it.
But the threshold between correct and incorrect inference is dependent on an intersection of the model and the document so far. That is not manageable by definition, I mean... It is a chaotic system.
Is this dissimilar to what the human brain produces? Are we not producing chaos controlled by wanting to give the right answer?
>people keep trying to apply engineering principles to an inherently unstable and unpredictable system in order to get a feeling of control.
What's the alternative?
Using predictable systems.
If your C compiler invents a new function call for a non-existent function while generating code, that's usually a bug.
If an LLM does, that's... Normal. And a non-event.
Not use such a poor tool.
Pretending that the world is stable predictable and feeling in control while making fun of other people. Obviously.
Are you using 2023 LLMs? o3 and Gemini 2.5 Pro will gladly say no or declare uncertainty in my experience
90% of people only use ChatGTP, typically 4o. Of course you're right, but that's where the disconnect comes from.
Fun fact: if you ask an LLM about best practices and how to organize your prompts, it will hint you towards this direction.
It’s surprisingly effective to ask LLMs to help you write prompts as well, i.e. all my prompt snippets were designed with help of an LLM.
I personally keep them all in an org-mode file and copy/paste them on demand in a ChatGPT chat as I prefer more “discussion”-style interactions, but the approach is the same.
Hah. Same. I have a step by step "reasoning" agent that asks me for confirmation after each step (understanding of problem, solutions proposed, solutions selection, and final wrap) - just so it gets red back the previous prompts and answers rather than one word salad essay.
Works incredibly well, and I created it with its own help.
https://github.com/jezweb/roo-commander has something like 1700 prompts in it with 50+ prompts modes. And it seems to work pretty well. For me at least. It’s task/session management is really well thought out.
We followed a very similar approach at work, created a test harness and tested all the models available in AWS bedrock and the OpenAI. We created our own code challenges not available on the Internet for training with vulnerable and non-vulnerable inline snippets and more contextual multi-file bugs. We also used 100 tests per challenge - I wanted to do 1000 test per challenge but realized that these models are not even close to 2 Sigma in accuracy! Overall we found very similar results. But, we were also able to increase accuracy using additional methods - which comes as additional costs. The issue I see overall is that we found is when dealing with large codebases you'll need to put blinders on the LLMs to shorten context windows so that hallucinated results are less likely to happen. The worst thing would be to follow red herrings - perhaps in 5 years we'll have models used for more engineering specific tasks that can be rated with Six Sigma accuracy if posed with the same questions and problems sets.
I know there were at least a few kernel devs who "validated" this bug, but did anyone actually build a PoC and test it? It's such a critical piece of the process yet a proof of concept is completely omitted? If you don't have a PoC, you don't know what sort of hiccups would come along the way and therefore can't determine exploitability or impact. At least the author avoided calling it an RCE without validation.
But what if there's a missing piece of the puzzle that the author and devs missed or assumed o3 covered, but in fact was out of o3's context, that would invalidate this vulnerability?
I'm not saying there is, nor am I going to take the time to do the author's work for them, rather I am saying this report is not fully validated which feels like a dangerous precedent to set with what will likely be an influential blog post in the LLM VR space moving forward.
IMO the idea of PoC || GTFO should be applied more strictly than ever before to any vulnerability report generated by a model.
The underlying perspective that o3 is much better than previous or other current models still remains, and the methodology is still interesting. I understand the desire and need to get people to focus on something by wording it a specific way, it's the clickbait problem. But dammit, do better. Build a PoC and validate your claims, don't be lazy. If you're going to write a blog post that might influence how vulnerability researchers conduct their research, you should promote validation and not theoretical assumption. The alternative is the proliferation of ignorance through false-but-seemingly-true reporting, versus deepening the community's understanding of a system through vetted and provable reports.
The article cites a signal to noise ratio of ~1:50. The author is clearly deeply familiar with this codebase and is thus well-positioned to triage the signal from the noise. Automating this part will be where the real wins are, so I'll be watching this closely.
I’ve developed a few take-home interview problems over the years that were designed to be short, easy for an experienced developer, but challenging for anyone who didn’t know the language. All were extracted from real problems we solved on the job, reduced into something minimal.
Every time a new frontier LLM is released (excluding LLMs that use input as training data) I run the interview questions through it. I’ve been surprised that my rate of working responses remains consistently around 1:10 for the first pass, and often takes upwards of 10 rounds of poking to get it to find its own mistakes.
So this level of signal to noise ratio makes sense for even more obscure topics.
We’ve been working on a system that increases signal to noise dramatically for finding bugs, we’ve at the same time been thoroughly benchmarking the entire popular software agents space for this
We’ve found a wide range of results and we have a conference talk coming up soon where we’ll be releasing everything publicly so stay tuned for that itll be pretty illuminating on the state of the space
Edit: confusing wording
Interesting. This is for Bismuth? I saw your pilot program link — what does that involve?
Yup! So we have multiple businesses working with us and for pilots its deploying the tool, providing feedback (we're connected over slack with all our partners for a direct line to us), and making sure the uses fit expectations for your business and working towards long term partnership.
We have several deployments in other peoples clouds right now as well as usage of our own cloud version, so we're flexible here.
I bet automatic this part will be simple. In general LLMs that have a given semantical ability "X" to do some task, have greater than X ability to check, among N replies about doing the same task, which reply is the best, especially if via binary tournament like RAInk did (it was posted here a few weeks ago). There is also the possibility to use agreement among different LLMs. I'm surprised Gemini 2.5 PRO was not used here, in my experience it is the most powerful LLM to do that kind of stuff.
I was thinking about this the other day, wouldn't it be feasible to make fine-tune or something like that into every git change, mailist, etc, the linux kernel has ever hard?
Wouldn't such an LLM be the closer -synth- version of a person who has worked on a codebase for years, learnt all its quirks etc.
There's so much you can fit on a high context, some codebases are already 200k Tokens just for the code as is, so idk
I'd be willing to bet the sum of all code submitted via patches, ideas discussed via lists, etc doesn't come close to the true amount of knowledge collected by the average kernel developer's tinkering, experimenting, etc that never leaves their computer. I also wonder if that would lead to overfitting: the same bugs being perpetuated because they were in the training data.
1:50 is a great detection ratio for finding a needle in a haystack.
I don't think the author agrees as he points out the bugs weren't that difficult to find.
If the LLM wrote a harness and proof of concept tests for its leads, then it might increase S/N dramatically. It’s just quite expensive to do all that right now.
Except that in my experience half the time it will modify the implementation in order to make the tests pass.
And it will do this no matter how many prompts you try or you forcefully you ask it.
With security vulnerabilities, you don't give the agent the ability to modify the potentially vulnerable software, naturally. Instead you make them do what an attacker would have to do: come up with an input that, when sent to the unmodified program, triggers the vulnerability.
How do you know if it triggered the vulnerability? Luckily for low-level memory safety issues like the ones Sean (and o3) found we have very good oracles for detecting memory safety, like KASAN, so you can basically just let the agent throw inputs at ksmbd until you see something that looks kind of like this: https://groups.google.com/g/syzkaller/c/TzmTYZVXk_Q/m/Tzh7SN...
> If the LLM wrote a harness and proof of concept tests for its leads, then it might increase S/N dramatically.
Designing and building meaningfully testable non-trivial software is orders of magnitude more complex than writing the business logic itself. And that’s if you compare writing greenfield code from scratch. Making an old legacy code base testable in a way conducive to finding security vulns is not something you just throw together. You can be lucky with standard tooling like sanitizers and valgrind but it’s far from a panacea.
Exactly. Many AI users can’t triage effectively, as a result open source projects get a lot of spam now: https://arstechnica.com/gadgets/2025/05/open-source-project-...
The most interesting and significant bit of this article for me was that the author ran this search for vulnerabilities 100 times for each of the models. That's significantly more computation than I've historically been willing to expend on most of the problems that I try with large language models, but maybe I should let the models go brrrrr!
I realised I didn't mention it in the article, so in case you're curious it cost about $116 to run the 100k token version 100 times.
A lot of money is all you need~
A lot of burned coal, is what.
The "don't blame the victim" trope is valid in many contexts. This one application might be "hackers are attacking vital infrastructure, so we need to fund vulnerabilities first". And hackers use AI now, likely hacked into and for free, to discover vulnerabilities. So we must use AI!
Therefore, the hackers are contributing to global warming. We, dear reader, are innocent.
So basically running a microwave for about 800 seconds, or a bit more than 13 minutes per model?
Oh my god - the world is gonna end. Too bad, we panicked because of exaggerated energy consumption numbers for using an LLM when doing individual work.
Yes - when a lot of people do a lot of prompting, these 0ne tenth of a second to 8 seconds of running the microwave per prompt adds up. But I strongly suggest, that we could all drop our energy consumption significantly using other means, instead of blaming the blog post's author about his energy consumption.
The "lot of burned coal" is probably not that much in this blog post's case given that 1 kWh is about 0.12 kg coal equivalent (and yes, I know that we need to burn more than that for 1kWh. Still not that much, compared to quite a few other human activities.
If you want to read up on it, James O'Donnell and Casey Crownhart try to pull together a detailed account of AI energy usage for MIT Technology Review.[1] I found that quite enlightening.
[1]: https://www.technologyreview.com/2025/05/20/1116327/ai-energ...
The better answer is just "I don't care".
Because I definitely don't care. Energy expenditure numbers are always used in isolation, lest any one have to deal with anything real about them, and always are content to ignore the abstraction which electricity is - namely, electricity is not coal. It's electricity. Unlike say, driving my petrol powered car, the power for my computers might come from solar panels, coal, nuclear power stations, geothermal power hydro...
Which is to say, if people want to worry about electricity usage: go worry about it by either building more clean energy, or campaigning to raise electricity prices.
Funny, I actually care. But I try to direct my care towards the real culprits.
So about 50% of CO2 emissions in Germany come from 20 sources. The campaigns like personal footprint (invented by BP) are there to shift the blame to consumers. Away from those with the biggest impact and the most options for action.
So yes, I f**ng don’t care if a security researcher leaves his microwave equivalent running for a few minutes. But I care, campaign in the bigger sense and also orient my own consumption wherever possible towards cleaner options.
Full well knowing that even as mostly being reasonable in my consumption, I definitely belong to those 5-10% of earth's population who drive the problem. Because more than half of the population in the so called first world live according to the Paris Climate Agreement. And it’s not the upper half of.
Between $3k and $30k to solve a single ARC-AGI problem [1]. Not sure if "100 runs" makes this comparable.
[1] https://techcrunch.com/2025/04/02/openais-o3-model-might-be-...
And how do you know what the purely-human-driven energy expenditure would have been?
How much longer would OP have needed to find the same vulnerability without LLM help? Then multiply that by the energy used to produce 2000kcal/day of food as well as the electricity for running their computer.
Usually LLMs come out far ahead in those types of calculations. Compared to humans they are quite energy efficient
Those types of calculation are extremely disingenuous.
What exactly is disingenuous about it?
Zero days can go for $$$, or you can go down the bug bounty route and also get $$. The cost of the LLM would be a drop in the bucket.
When the cost of inference gets near zero, I have no idea what the world of cyber security will look like, but it's going to be a very different space from today.
"100 times for each of the models" represents a significant amount of energy burned. The achievement of finding the most common vulnerability in C based codebases becomes less of an achievement. And more of a celebration of decadence and waste.
We are facing global climate change event, yet continue to burn resources for trivial shit like it’s 1950.
I'm not sure about the assertion that this is the first vulnerability found with an LLM. For e.g. OSS-Fuzz [0] has found a few using fuzzing, and Big Sleep using an agent approach [1].
[0] https://security.googleblog.com/2024/11/leveling-up-fuzzing-...
[1] https://googleprojectzero.blogspot.com/2024/10/from-naptime-...
It's certainly not the first vulnerability found with an LLM =) Perhaps I should have been more clear though.
What the post says is "Understanding the vulnerability requires reasoning about concurrent connections to the server, and how they may share various objects in specific circumstances. o3 was able to comprehend this and spot a location where a particular object that is not referenced counted is freed while still being accessible by another thread. As far as I'm aware, this is the first public discussion of a vulnerability of that nature being found by a LLM."
The point I was trying to make is that, as far as I'm aware, this is the first public documentation of an LLM figuring out that sort of bug (non-trivial amount of code, bug results from concurrent access to shared resources). To me at least, this is an interesting marker of LLM progress.
I really hope this is legit and not what keeps happening to curl
[1] https://daniel.haxx.se/blog/2024/01/02/the-i-in-llm-stands-f...
My understanding is that ksmbd is a kernel-space SMB server "developed as a lightweight, high-performance alternative" to the traditional (user-space) Samba server...
Q1: Who is using ksmbd in production?
Q2: Why?
1. People that were using the in-kernel SMB server in Solaris or Windows.
2. Samba performance sucks (by comparison) which is why people still regularly deploy Windows for file sharing in 2025.
Anybody know if this supports native Windows-style ACLs for file permissions? That is the last remaining reason to still run Solaris but I think it relies on ZFS to do so.
Samba's reliance on Unix UID/GID and the syncing as part of its security model is still stuck in the 1970s unfortunately.
The caveat is the in-kernel SMB server has been the source of at least one holy-shit-this-is-bad zero-day remote root hole in Windows (not sure about Solaris) so there are tradeoffs.
> Samba's reliance on Unix UID/GID and the syncing as part of its security model is still stuck in the 1970s unfortunately.
Sigh. This is why we can't have nice things
Like yeah having smb in kernel is faster but honestly it's not fundamentally faster. But it seems the will to make samba better isn't there
Licensing. Samba is GPLv3, Linux is only GPLv2.
I would assume for the reason of being lightweight and high performance?
Smb over 25gbit networks - user space samba is much worse there.
This is interesting to me! I regularly deploy 25G network connections, but I don’t think we’d run SMB over that. I am super curious the industry and use case if you’re willing to share!
I ran SMB over a 20gbit network (2x 10gbit). The use case was 3D rendering (photogrammetry specifically). There were multiple render nodes, and a central service coordinating the rendering process. The projects would be on SSD's on the central SMBD server, and after they were manually configured (using Agisoft Metashape) they'd be rendered. Projects would sometimes start as tens of gigabytes worth of photos, and the artifacts (including intermediates) would balloon into the hundreds of gigabytes, we'd have dozens of these projects per week.
I researched quite extensively prior to landing on SMB, but it really seems like there isn't a better way of doing this. The environment was mixed windows/linux, but if there was a better pure linux solution I would've pushed our office staff to switch to Ubuntu.
"SMB Direct" is RDMA based and ksmbd supports it. Samba does not. Disclaimer: I have not used it but was looking it up just yesterday.
Appreciated, thank you.
The same reason people use kmod-trelay instead of relayd I guess
There's a beautiful little snippet here that perfectly captures how most of my prompt development sessions go:
> I tried to strongly guide it to not report false positives, and to favour not reporting any bugs over reporting false positives. I have no idea if this helps, but I’d like it to help, so here we are. In fact my entire system prompt is speculative in that I haven’t ran a sufficient number of evaluations to determine if it helps or hinders, so consider it equivalent to me saying a prayer, rather than anything resembling science or engineering. Once I have ran those evaluations I’ll let you know.
(To be clear, I'm not the author of the post, the title just starts with "How I")
> With o3 you get something that feels like a human-written bug report, condensed to just present the findings, whereas with Sonnet 3.7 you get something like a stream of thought, or a work log.
This is likely because the author didn't give Claude a scratchpad or space to think, essentially forcing it to mix its thoughts with its report. I'd be interested to see if using the official thinking mechanism gives it enough space to get differing results.
Having tried both I’d say o3 is in a league of it’s own compared to 3.7 or even Gemini 2.5 pro. The benchmarks may show not a lot of gain but that matters a lot when the task is very complex. What’s surprising is that they announced it last November and only now it’s released a month back now? (I’m guessing lots of safety took time but no idea). Can’t wait for o4!
All your content threads from the past months consist on you saying how much better OpenAI products are than the competition, so that doesn’t inspire a ton of trust.
Could you provide some links to relevant work/research on using a "scratchpad" that you liked?
I'm not much of an ML engineer but I can point you to the original chain of thought paper [0] and Anthropic's docs on how to enable their official thinking scratchpad [1].
[0] https://arxiv.org/pdf/2201.11903
[1] https://docs.anthropic.com/en/docs/build-with-claude/extende...
Anyone else feel like this is a best case application for LLMs?
You could in theory automate the entire process, treat the LLM as a very advanced fuzzer. Run it against your target in one or more VMs. If the VM crashes or otherwise exhibits anomalous behavior, you've found something. (Most exploits like this will crash the machine initially, before you refine them.)
On one hand: great application for LLMs.
On the other hand: conversely implies that demonstrating this doesn't mean that much.
https://security.googleblog.com/2024/11/leveling-up-fuzzing-...
I think this is the biggest alignment problem with LLMs in the short term imo. It is getting scarily good at this.
I recently found a pretty serious security vulnerability in an open source very niche server I sometimes use. This took virtually no effort using LLMs. I'm worried that there is a huge long tail of software out there which wasn't worth finding vulnerabilities in for nefarious means manually but if it was automated could lead to really serious problems.
The (obvious) flipside of this coin is that it allows us to run this adversarially against our own codebases, catching bugs that could otherwise have been found by a researcher, but that we can instead patch proactively.\
I wouldn't (personally) call it an alignment issue, as such.
If attackers can automatically scan code for vulnerabilities, so can defenders. You could make it part of your commit approval process or scan every build or something.
A lot of this code isn't updated though. Think of how many abandoned wordpress plugins there are (for example). So the defenders could, but how do they get that code to fix it?
I agree after time you end up with a steady state but in the short medium term the attackers have a huge advantage.
Is it an alignment problem if it's doing what was asked of it? It's "aligned" with a human's wishes.
It's a moot point unless attackers have better LLMs don't have access to.
I'll have to get my facts straight but I'm pretty sure that ksmbd is ... not used much (by me).
https://lwn.net/Articles/871866/ This is also nothing to do with Samba which is a well trodden path.
So why not attack a codebase that is rather more heavily used and older? Why not go for vi?
Good link. After reading this it's not a surprise that this code has security vulnerabilities. But of course from knowing that there must be more to actually finding it, it's still a big leap.
4 years after the article, does any relevant distro have that implementation enabled?
This is a great case study. I wonder how hard o3 would find it to build a minimal repro for these vulns? This would of course make it easier to identify true positives and discard false positives.
This is I suppose an area where the engineer can apply their expertise to build a validation rig that the LLM may be able to utilize.
I wonder how often it will say there’s a vulnerability where there is non. Running it 100 times is a lot
I have a presentation here on doing it to target zk bugs https://youtu.be/MN2LJ5XBQS0?si=x3nX1iQy7iex0K66
https://youtu.be/MN2LJ5XBQS0
rest of the link is tracking to my (limited) understanding
Meanwhile, as a maintainer, I've been reviewing more than a dozen false positives slop CVEs in my library and not a single one found an actual issue. This article's is probably going to make my situation worse.
Maybe, but the author is an experienced vulnerability analyst. Obviously if you get a lot of people who have no experience with this you may get a lot of sloppy, false reports.
But this poster actually understands the AI output and is able to find real issues (in this case, use-after-free). From the article:
> Before I get into the technical details, the main takeaway from this post is this: with o3 LLMs have made a leap forward in their ability to reason about code, and if you work in vulnerability research you should start paying close attention. If you’re an expert-level vulnerability researcher or exploit developer the machines aren’t about to replace you. In fact, it is quite the opposite: they are now at a stage where they can make you significantly more efficient and effective.
Not even that. The author already knew the bug was there, and fed the LLM just the files related to the bug, with the explanation on how the methods worked and where to search, and even then, only 1 out of 100 times did it find the bug.
probably not. o3 is not free to use.
Who says you need to use a top model to produce cybersecurity slop? Did this person use o3?
https://hackerone.com/reports/3125832
Given the value of finding zero days, pretty much every intelligence agency in the world is going to be pouring money into this if it can reliably find them with just a few hundred api calls. Especially if you can fine tune a model with lots of examples, which I don't think open ai, etc are going to do with any public api.
Yeah, the amount of engineering they have around controlling (censoring) the output, along with the terms of service, creates an incentive to still look for any possible bugs, but not allow it in the output.
Certainly for Govt agencies and others this will not be a factor. It is just for everyone else. This will cause people to use other models and agents without these restrictions.
It is safe to assume that a large number of vulnerabilities exist in important software all over the place. Now they can be found. This is going to set off arms race game theory applied to computer security and hacking. Probably sooner than expected...
Wow, interesting. I been hacking a tool called https://diffwithgpt.com with a similar angle but indexing git changelogs with qwen to have it raise risks for backward compat issues, risks including security when upgrading k8s etc.
Very interesting. Is the bug it found exploitable in practice? Could this have been found by syzkaller?
I case anyone else didn't recognize that word: https://github.com/google/syzkaller
I feel like our jobs are reasonably secure for a while because the LLM didn't immediately say "SMB implemented in the kernel, are you f-ing joking!?"
Seems we need something like kernel modules but with memory protection
Does the vulnerability exist in other implementations of SMB?
Implementations of SMB (Windows, Samba, macOS, ksmbd) are going to be different (macOS has a terrible implementation, even though AFP is being deprecated). At this level, it's doubtful that the code is shared among all implementations.
>It is interesting by virtue of being part of the remote attack surface of the Linux kernel.
...if your linux kernel has ksmbd built into it; that's a much smaller interest group
This is just fuzzing with extra power consumption?
Can you reconstruct finding this bug with traditional fuzzing?
This made me think that the near future will be LLMs trained specifically on Linux or another large project. The source code is a small part of the dataset fed to LLMs. The more interesting is runtime data flow, similar to what we observe in a debugger. Looking at the codebase alone is like trying to understand a waterfall by looking at equations that describe the water flow.
it needs to be trained on on enough TLA+ traces, too.
“I brute forced an ai to help me find potential zero day bugs”
meanwhile boomers out here still thinking they are better than AI wehen even local gemma3 models can write better code then them allready
Are there better tools for finding this? It feels like the sort of thing static analysis should reliably find, but it's in the Linux kernel, so you'd think either coding standards or tooling around these sorts of C bugs would be mature.
Not the expert in the area, but "classic static analysis" (for lack of a better term) and concurrency bugs doesn't really check. There are specific modeling tools for concurrency, and they are an entirely different beast than static analysis that requires notation and language support to describe what threads access what data when. Concurrency bugs in static analysis probably requires a level of context and understanding that an LLM can easily churn through.
Some static analysis tools can detect use after free or memory leaks. But since this one requires reasoning about multiple threads, I think it would've been unlikely to be found by static analysis.
Noteable:
> o3 finds the kerberos authentication vulnerability in 8 of the 100 runs
And I'd guess this only became a blog post because the author already knew about the vuln and was just curious to see if the intern could spot it too, given a curated subset of the codebase
He did do exactly what you say – except right after that, while reviewing the outputs, he found that it had also discovered a different 0day.
Now the question is whether spending same time to analyze that bit of code instead of throwing automated intern at it would be time spent better
The time they didn't spend reading the 13k LOCs themselves would've been time spent better.
What?