A patch was submitted, but apparently not merged. That was also my experience trying to submit a patch for https://trac.ffmpeg.org/ticket/8738 . Somebody on the bug tracker took note, but was apparently unable to effect a merge in the intervening years.
Maybe now that ffmpeg is using Forgejo, the ball won't be dropped like this as often. Or there'll just be a five-digit number of open pull requests instead.
Oddly enough I can’t access that site, it just heats up my phone solving hashes. Gave up after about a minute and anubis had only made it less than halfway through.
I doubt the real bots have any trouble bypassing it.
I get this crap when browsing on desktop a lot as well, principally because I stubbornly use Firefox as my main browser, and I habitually use a VPN when I connect my laptop to unsecured or even secured-but-accessible-to-large-numbers-of-people WiFi networks.
Like, seriously, bot detection "specialists", fuck off: I'm not a bot but your bot detection software IS shit, and I DO resent your shit software draining my battery and getting in my way. Learn to do your jobs properly, will you?
And don't come crying to me about how the problem you're trying to solve is "hard". I don't care: you chose it, you chose to considerably worsen the web browsing experience of millions of people globally, nobody made you. So go and find a different job if you're incapable of doing the one you have.
And if it's so "hard" why does your entire solution seem to be predicated on anyone's a bot if they're not running Chrome, or they are running an adblocker, or they appear to be from an unusual country that doesn't match their system language? Seriously, is this the level of sophistication you hacks operate at? To solve your "hard" problem?
It’s interesting how AI may both raise and lower the quality of software. It’s very easy to send an AI agent on an open-ended bug hunt, and if it wastes a bunch of time and effort and finds nothing, no big deal. Time is much more important for a human developer with a salary.
This is where I believe strong typing (like, Haskell-strong or stronger) and functional programming in general will be a win. The confidence I have that my fixes are localised when fixing Haskell code is infinitely stronger than fixing even Java, not speak about C, code.
Haskell's type system would not easily prevent this bug. It's not good at numeric/logic issues like that. When people say "Haskell makes it impossible to write bugs" they mean "Haskell has enums" (ADTs).
Liquid Haskell might require you to prove that the divisor is nonzero, but even in standard Haskell there's common idioms for ensuring that a list is non-empty (data NonEmpty a = a :| [a]) or that text is non-empty (newtype NonEmptyText = NonEmptyText Text, with non-exported constructor, helpers like make :: Text -> NonEmptyText, or more advanced tricks like https://exploring-better-ways.bellroy.com/haskell-koan-type-... ).
The big problem preventing this approach from working for numbers is that it's just so cumbersome there. Most of this is because all the arithmetic operators are bundled into a single Num typeclass, and `fromInteger :: Num a => Integer -> a` has a type that's impossible for a "non-zero number" wrapper to satisfy.
Definitely room for improvement on Haskell's standard library when it comes to the number-related type classes. Modern Haskell could do very well in this area with a good type-class redesign in this area. The issue I think is that this would invalidate a lot of existing code, relying upon that. But you can already replace Prelude with something else in your own code if you want to.
I meant the constrained types by hiding the constructors. Super annoying, not automatically convertible, in Haskell you have to remember what the fake constructor is called, and write it every time you use it, but at least it's efficiently implemented with newtype, unlike the Java OOP version. Think about writing a value with several nested constrained types, like NonEmptyListOne (makeNonZeroNumber 42, 'h' `NonEmptyString` "ello world"). It's just really annoying.
I am not claiming you cant write buggy code in Haskell! But following good functional style, your bug will more likely be compartmentalised, and fixing it will not break some other part of your program.
Imo, formal methods like more expressive/stricter type systems are key to making LLM generated code successful. Of course models will get better, but trusting the output will become much easier with a type system that proves more properties.
Dependent types is one possible direction. Not sure when a language with dependent types will arise which will be useful for making real programs.
Agda is the most mature dependently typed programming languae (having been around since the 90s – it is basically Haskell on steroids), but has a more proof-assistant flavor than an actual programming language flavor. Opus & Fable write Agda quite well, so LLMs can understand dependent types.
No one can keep up with the volume of code AI produces.
We wont stop using AI.
We will use AI to check AI.
Of course this is crazy, but it will also unlock pretty insane scaling and productivity and ultimately we will manage it on either end via requirements and tests.
> it will also unlock pretty insane scaling and productivity
Insane scaling of bloat, bugs, and technical debt I'd say.
> We will manage it on either end via requirements and tests
It is so crazy that this is being touted as a sane strategy. When I was a much worse programmer, I tried to write a big complicated string manipulation function to take two types of scripts in a language and add diacritics. I had the requirements very clear. I had the tests very clearly with all the edge cases. But I didn't have a good and clear picture of how to attack the problem which was quite novel for me. As I got closer to passing all the tests it got exponentially more unruly and confusing. And nearing the end I was frantically changing little bits here and there wincing and praying and hoping the tests would pass. "Please work! Come on!" Then when I got close enough, I could never ever think about touching that mess again.
I was a below average programmer then throwing myself at some novel problem I didn't understand. Throwing LLMs that produce below average code at novel problems and relying on tests and requirements is not where we want to go to make real progress.
(Years later after much learning and coding myself I was able to redo the function in a totally different way. This time I actually understood how to attack the strange problem and made something clean, clear, and robust that just worked. The tests then become a secondary guardrail, not the main force of correction.)
We are seeing such a massive regression from what we've learned over the years of CS.
You can point AI at any AI produced code and ask it to review it, get back 10 bullet points and a few pages of prose. And the fun part is, you can do that over and over and over again!
This happens all the time. Yesterday, I ran into an especially egregious case.
I had Fable add a new subcommand to our internal CLI tool. I reviewed and tested it locally and had to suggest several fixes that I feel like I wouldn't have had to tell a human senior engineer to do. When it finally submitted the PR, I had it on a loop waiting a few minutes for comments on the PR, then assessing/addressing/replying-to/resolving them, and then repeating again until all AI reviewers were okay with it. It ended up going through dozens of revisions and ended up with 160 comments left on the PR.
You're suggesting that LLMs get better at fixing bugs/vulnerabilities, but at the same time stop getting better at finding them? What if this difference is inherent and essential?
Absolutely not. By most accounts they're terrible at fixing anything other than trivial bugs in complex codebases e.g. Linux kernel, but they're much better at finding them.
In fairness at root this has been going on for awhile. No one can keep up with the volume of machine code that modern more abstracted codebases produce.
We didn't stop using syntactic programming languages we used code to check code.
Not sure it's really crazy at all. It's been an abstraction for programmers probably since we stopped soldering transistors to each other.
The missing part of this is that verifying the bug with LLMs is also easy, and so is adversarially reviewing the proposed fix with LLMs.
The only thing left for you to do should be directional decisions. The LLMs should pause and rope you in if the fix involves directional/invariant changes.
Maybe if what you're working on is low stakes (ie, where a bug doesn't cause customers to complain about losing thousands of dollars). I haven't seen automated AI code review do that job fully without lots of hand holding and careful manual review. And I've invested time in LLM review agents with historical human memory systems.
In my experience, there are two ways to use AI: speed or quality. Speed is where you give the AI a task to do and you review it; quality is where you write the code yourself and you get AI to review it. Both are valid for different situations.
Generate multiple solutions- they do not to work 100% correctly.
And than I check which I would prefer. Which is more to our applications taste.
And than I would take the vibe output as a kind of a ‚plan‘ which I use to implement but not follow 100% and at the end I take my solution and review it.
I gain speed with that because I often can quickly see the pros and cons of a solution way better than when I would manually do it and hang on a major roadblock and also I even see such roadblocks in the vibe output - it’s mostly the part with an unnecessary amount of new code that looks nonsensical.
I don't care if you call it an over-engineered looping machine or what, there are concrete benefits to using LLMs for this. They work faster than developing your own looping algorithm and more often produce useful results than not.
It's not even like fuzzers are valuable because of the process they use specifically either; the value is that they produce a concrete input that you can use as a reproducible test case at that point. The value could be produced by gazing into a crystal ball for all I care, as long as I can use what it gives me to reproduce a bug.
I dislike AI, but if AI finds real bugs then this is in my opinion objectively a positive thing. Of course the question is what constitutes a real bug.
Funny thing, I know I'm brushing up against something in gStreamer developer, but Fable flips out. I have only a loose idea where the issue might be lurking.
Next week, I'll apply for the cyber and I suspect I'll find something similar.
Right now, it's just annoying and thanks the OpenAI cyber was much easier to get access to.
Whatever about the specifics of this bug and whether its a useful vector, this is not surprising even in the slightest?
My current opinion on LLMs is that they are superhuman in that they lack fatigue, they have close to full knowledge across all subjects which are known to humans at least publicly, and the fact that you can vibe code a harness to look for bugs in a famously complicated C codebase is intern level stuff and hardly news.
Smart aspiring blackhats will be targeting tmux next, both with light llm jailbreaks, light supply chain attacks (web search results) and LPEs within certain environments which weren't particularly useful before but with agents running on auto mode for hours become a very valuable springboard. I'm not sure on the quality of tmux code but I know its written in C and is very complex and was not at all designed to defend against this type of threat.
Not that it doesn’t have issues, but I’m not sure why you’d choose tmux of all things. It runs as a user and has no privileges to escalate. It was written for and is part of OpenBSD and follows their security hardening practices.
(There actually was one privilege escalation bug in tmux, but it actually seems like a distro packaging error. The distro setgid the executable so the resulting shell inherited the additional group. This didn’t require any exploit, that’s just how child process inheritance works.)
I don’t think tmux is the most worthwhile target because you’d need the user to either execute code locally (thus negating any point in targeting tmux) or rely on the user curl or cat some compromised document (in which case you’re better off targeting curl or cat).
the point is tmux is being used by many developers working in high value targets to automate long running unsupervised agent tasks. you don't need the user to execute code, you need _their agent_ to stumble on the wrong search result or github repo and it wont be noticed for hours that they loaded a persistent threat into your environment.
the fact that you can vibe code a harness to look for bugs in a famously complicated C codebase is intern level stuff and hardly news
It seems like this would have been pure fantasy not that long ago though. So why isn’t it noteworthy again? I don’t really follow what you’re complaining about.
This is not a real bug in FFmpeg. This is a demonstration that if you control a custom AVIO module it is possible to crash FFmpeg by giving it bad data.
Not custom. It's an existing module for a format called VPK. It's a quite trivial bug though, not exploitable apart from DOS and won't ever happen in a real file.
No doubt fuzzers (vibecoded or otherwise) can be powerful, but can't you just mark all "/" as potential divide by zero errors?
I guess sometimes developers think they "know" some variable won't be zero, but unless it checked explicitly or by the compiler, that shouldn't be trusted.
> but can't you just mark all "/" as potential divide by zero errors?
If you’re accepting large false positives rates: yes.
If you want users to take your warnings serious: no.
(Nitpick: you certainly don’t want to flag _all_ of them. Divisions by non-zero constants definitely should be excluded, for example (integer division by -1 can lead to overflow, but that would be a different warning))
If it's possible for program execution with some particular input to lead to a divide-by-zero, that's a bug, especially if the program is expected to be able to handle malformed inputs, or perhaps even deliberately malicious ones. It's not trivial to determine whether a program does this correctly. If it was, program analysis would be easy.
Division can 'go wrong' for certain inputs, but it's not just division. In C, signed integer addition, subtraction, and multiplication, all give undefined behaviour on overflow.
As 'Someone' already pointed out, it's not helpful to just flag all uses of the division operator, or of other potentially dangerous operators. Minimising false positives is one of the core challenges of program analysis.
I mean there could be a guard clause? But yeah, seems like this could be statically evaluated like how some IDEs see a null check and don’t complain about nullability within the same scope.
Lots of projects run their own git or forgejo or similar. I run my own private forge, and it has a higher uptime than GitHub. (A shockingly low bar, tbh)
It’s surprisingly simple to setup, and the hardware requirements are pretty small for a private or small forge, as it’s usually a relatively small number of users/repos/etc.
> It is interesting that FFmpeg has its own Git server. Maybe we should move there too?
Git is a DVCS. I know many people only ever used Git through Github and forgot what the 'D' in DVCS means but whether or not they remember what the 'D' stands for, running your own Git server is trivial. Especially in this day and age of LLMs were you can just ask: "Clone this repo and convert it to base Git repo and serve it on the LAN PLZ KTHX".
The result is going to be more stable than Github and, arguably, more secure too.
If you have SSH access to a server and Git is installed on that server, you can use it as a Git server. No additional setup is required. The Git client knows how to log in and invoke the Git server over SSH.
it's widely used but in "industry" applications. so ffmpeg is probably being used in a lot of offices (studios) and maybe even being included in end user software.
The only way to achieve this is to either put a runtime software check on a variable whenever it's assigned/used, or to literally add hardware support in processors themselves which literally throws an interrupt when a "neverShallBeZero" variable is assigned to zero.
There's no viable way to statically prove at compile-time that these variables will never become zero at runtime, ultimately forcing a system of endless runtime checks (be it software or hardware)... which is why processors already throw exception interrupts when division by zero is attempted.
The projectively extended real line defines division by zero, no reason you couldn't have a floating point type that implemented it.
>There's no viable way to statically prove at compile-time that these variables will never become zero at runtime
strongly typed programming languages like Ada allow for types which have ranges such as disallowing zero -- but also any arbitrary thing like you can create a floating point "degrees" type which is [0.0, 360.0] or any other ranged type
It would be more flexible for a compiler to reuse the range analysis logic used in optimizations for statically verifiable divide by zeros. That way you could extend it to other things like statically verifiable overflows.
For stuff like niche value optimization sure. For practical arithmetic code, nah. Like with this bug, all that changed is that garbage data in gives the user an error that they tried to process garbage data. Adding a new type doesn't make the code better, it just moves the error around. And you really don't want an infix division operator to fail to type check if the right hand side isn't a nonzero type, do you?
I imagine the discussion will center around this application of AI, but to me this is just the Nth proof of the proven fact that you must build ffmpeg, if you insist on using it, with only an allow-list of file formats that you expect to encounter, and not with the kitchen sink of stuff you are never going to need.
But guys... AI is bad. It might have done good stuff today, but we should be anti data. The Chinese propagandists on United States social media told me to.
Nice find. The interesting part isn't "AI wrote the fuzzer." It's that a cheap random harness still hits classical bugs in ancient parsers. Keep the corpus; throw away the hype.
The fruits of using LLMs to code.
You'll waste far more time finding what it quietly and subtly wrecked than you would have if you just coded it yourself.
It’s obviously Claude 69 with time travel functionality, that’s too dangerous to release to public. They’re working on space-time limiting sandbox to prevent these issues.
A patch for this was submitted in April: https://lists.ffmpeg.org/archives/list/ffmpeg-devel@ffmpeg.o...
Edit: And there was discussion about this back in 2024 as well
A patch was submitted, but apparently not merged. That was also my experience trying to submit a patch for https://trac.ffmpeg.org/ticket/8738 . Somebody on the bug tracker took note, but was apparently unable to effect a merge in the intervening years.
Maybe now that ffmpeg is using Forgejo, the ball won't be dropped like this as often. Or there'll just be a five-digit number of open pull requests instead.
Oddly enough I can’t access that site, it just heats up my phone solving hashes. Gave up after about a minute and anubis had only made it less than halfway through.
I doubt the real bots have any trouble bypassing it.
It also took insanely long on my iPhone 16. "Made with heart in Canada" but configured poorly.
Yeah, is it trying to mine bitcoin or something? Anubis usually takes a second but here I waited a minute and got 20% through on a modern phone.
Yeah, it's painful.
I get this crap when browsing on desktop a lot as well, principally because I stubbornly use Firefox as my main browser, and I habitually use a VPN when I connect my laptop to unsecured or even secured-but-accessible-to-large-numbers-of-people WiFi networks.
Like, seriously, bot detection "specialists", fuck off: I'm not a bot but your bot detection software IS shit, and I DO resent your shit software draining my battery and getting in my way. Learn to do your jobs properly, will you?
And don't come crying to me about how the problem you're trying to solve is "hard". I don't care: you chose it, you chose to considerably worsen the web browsing experience of millions of people globally, nobody made you. So go and find a different job if you're incapable of doing the one you have.
And if it's so "hard" why does your entire solution seem to be predicated on anyone's a bot if they're not running Chrome, or they are running an adblocker, or they appear to be from an unusual country that doesn't match their system language? Seriously, is this the level of sophistication you hacks operate at? To solve your "hard" problem?
You are extremely lame. Get out of my way.
It’s interesting how AI may both raise and lower the quality of software. It’s very easy to send an AI agent on an open-ended bug hunt, and if it wastes a bunch of time and effort and finds nothing, no big deal. Time is much more important for a human developer with a salary.
Finding the bugs with LLMs is easy. Reviewing the output, cleaning it up, and making sure it doesn't break something else is the hard part.
This is where I believe strong typing (like, Haskell-strong or stronger) and functional programming in general will be a win. The confidence I have that my fixes are localised when fixing Haskell code is infinitely stronger than fixing even Java, not speak about C, code.
Haskell's type system would not easily prevent this bug. It's not good at numeric/logic issues like that. When people say "Haskell makes it impossible to write bugs" they mean "Haskell has enums" (ADTs).
Liquid Haskell might require you to prove that the divisor is nonzero, but even in standard Haskell there's common idioms for ensuring that a list is non-empty (data NonEmpty a = a :| [a]) or that text is non-empty (newtype NonEmptyText = NonEmptyText Text, with non-exported constructor, helpers like make :: Text -> NonEmptyText, or more advanced tricks like https://exploring-better-ways.bellroy.com/haskell-koan-type-... ).
The big problem preventing this approach from working for numbers is that it's just so cumbersome there. Most of this is because all the arithmetic operators are bundled into a single Num typeclass, and `fromInteger :: Num a => Integer -> a` has a type that's impossible for a "non-zero number" wrapper to satisfy.
Definitely room for improvement on Haskell's standard library when it comes to the number-related type classes. Modern Haskell could do very well in this area with a good type-class redesign in this area. The issue I think is that this would invalidate a lot of existing code, relying upon that. But you can already replace Prelude with something else in your own code if you want to.
OOP has those too, and they're very annoying.
In Haskell they are a little less annoying. It is just easier to reason about (including proving) pure functions.
I meant the constrained types by hiding the constructors. Super annoying, not automatically convertible, in Haskell you have to remember what the fake constructor is called, and write it every time you use it, but at least it's efficiently implemented with newtype, unlike the Java OOP version. Think about writing a value with several nested constrained types, like NonEmptyListOne (makeNonZeroNumber 42, 'h' `NonEmptyString` "ello world"). It's just really annoying.
I am not claiming you cant write buggy code in Haskell! But following good functional style, your bug will more likely be compartmentalised, and fixing it will not break some other part of your program.
Imo, formal methods like more expressive/stricter type systems are key to making LLM generated code successful. Of course models will get better, but trusting the output will become much easier with a type system that proves more properties.
Even Lean 4 strong typing
What's stronger than Haskell?
Dependent types is one possible direction. Not sure when a language with dependent types will arise which will be useful for making real programs.
Agda is the most mature dependently typed programming languae (having been around since the 90s – it is basically Haskell on steroids), but has a more proof-assistant flavor than an actual programming language flavor. Opus & Fable write Agda quite well, so LLMs can understand dependent types.
Anything with ranged numeric types. Like everyone's favorite functional programming language, Ada.
This issue raises SIGFPE. Ada would raise Constraint_error, which is easier to catch than a signal, but still occurs at runtime.
You need range proofs to be 100% safe, and then you can as well use the regular type because invalid values will not occur.
Or Liquid Haskell.
Lean 4, Idris 2.
Perhaps coq/agda/idris/etc.
No one can keep up with the volume of code AI produces.
We wont stop using AI.
We will use AI to check AI.
Of course this is crazy, but it will also unlock pretty insane scaling and productivity and ultimately we will manage it on either end via requirements and tests.
> it will also unlock pretty insane scaling and productivity
Insane scaling of bloat, bugs, and technical debt I'd say.
> We will manage it on either end via requirements and tests
It is so crazy that this is being touted as a sane strategy. When I was a much worse programmer, I tried to write a big complicated string manipulation function to take two types of scripts in a language and add diacritics. I had the requirements very clear. I had the tests very clearly with all the edge cases. But I didn't have a good and clear picture of how to attack the problem which was quite novel for me. As I got closer to passing all the tests it got exponentially more unruly and confusing. And nearing the end I was frantically changing little bits here and there wincing and praying and hoping the tests would pass. "Please work! Come on!" Then when I got close enough, I could never ever think about touching that mess again.
I was a below average programmer then throwing myself at some novel problem I didn't understand. Throwing LLMs that produce below average code at novel problems and relying on tests and requirements is not where we want to go to make real progress.
(Years later after much learning and coding myself I was able to redo the function in a totally different way. This time I actually understood how to attack the strange problem and made something clean, clear, and robust that just worked. The tests then become a secondary guardrail, not the main force of correction.)
We are seeing such a massive regression from what we've learned over the years of CS.
I think all code is technical debt in a way. Good code is a necessary evil, bad code is more evil than necessary.
Generating code automatically when you're not even quite sure what it is or even should be doing is insanity.
It's mostly (not entirely, but mostly) finding security issues in old human-written code. It'll eventually start running out of those.
From that standpoint, it's not a crazy setup security-wise. Maybe still crazy for development.
You can point AI at any AI produced code and ask it to review it, get back 10 bullet points and a few pages of prose. And the fun part is, you can do that over and over and over again!
This happens all the time. Yesterday, I ran into an especially egregious case.
I had Fable add a new subcommand to our internal CLI tool. I reviewed and tested it locally and had to suggest several fixes that I feel like I wouldn't have had to tell a human senior engineer to do. When it finally submitted the PR, I had it on a loop waiting a few minutes for comments on the PR, then assessing/addressing/replying-to/resolving them, and then repeating again until all AI reviewers were okay with it. It ended up going through dozens of revisions and ended up with 160 comments left on the PR.
You're suggesting that LLMs get better at fixing bugs/vulnerabilities, but at the same time stop getting better at finding them? What if this difference is inherent and essential?
> You're suggesting that LLMs get better at fixing bugs/vulnerabilities, but at the same time stop getting better at finding them?
Are you implying that all code writing by LLMs atm is bug-free?
Absolutely not. By most accounts they're terrible at fixing anything other than trivial bugs in complex codebases e.g. Linux kernel, but they're much better at finding them.
In fairness at root this has been going on for awhile. No one can keep up with the volume of machine code that modern more abstracted codebases produce.
We didn't stop using syntactic programming languages we used code to check code.
Not sure it's really crazy at all. It's been an abstraction for programmers probably since we stopped soldering transistors to each other.
The missing part of this is that verifying the bug with LLMs is also easy, and so is adversarially reviewing the proposed fix with LLMs.
The only thing left for you to do should be directional decisions. The LLMs should pause and rope you in if the fix involves directional/invariant changes.
Maybe if what you're working on is low stakes (ie, where a bug doesn't cause customers to complain about losing thousands of dollars). I haven't seen automated AI code review do that job fully without lots of hand holding and careful manual review. And I've invested time in LLM review agents with historical human memory systems.
In my experience, there are two ways to use AI: speed or quality. Speed is where you give the AI a task to do and you review it; quality is where you write the code yourself and you get AI to review it. Both are valid for different situations.
My plan for bigger things is mostly:
Generate multiple solutions- they do not to work 100% correctly. And than I check which I would prefer. Which is more to our applications taste.
And than I would take the vibe output as a kind of a ‚plan‘ which I use to implement but not follow 100% and at the end I take my solution and review it. I gain speed with that because I often can quickly see the pros and cons of a solution way better than when I would manually do it and hang on a major roadblock and also I even see such roadblocks in the vibe output - it’s mostly the part with an unnecessary amount of new code that looks nonsensical.
Using a LLM whose output is slowed to the rate of a human programmer as a pair programming partner is a very interesting experience.
> It’s very easy to send an AI agent on an open-ended bug hunt, and if it wastes a bunch of time and effort and finds nothing, no big deal.
No big deal? It’s not like it’s free… tokens cost money.
Often rounds to free compared to human costs.
When talking about LLM tokens the cost is almost always being implicitly compared to very expensive human developer time.
I don't care if you call it an over-engineered looping machine or what, there are concrete benefits to using LLMs for this. They work faster than developing your own looping algorithm and more often produce useful results than not.
It's not even like fuzzers are valuable because of the process they use specifically either; the value is that they produce a concrete input that you can use as a reproducible test case at that point. The value could be produced by gazing into a crystal ball for all I care, as long as I can use what it gives me to reproduce a bug.
I dislike AI, but if AI finds real bugs then this is in my opinion objectively a positive thing. Of course the question is what constitutes a real bug.
Unfiltered models will help build exploits for the bugs they find, so there is some means of measuring their efficacy.
If you're just talking about security bugs.
There are also non security bugs that don't have exploits but just make the user experience worse.
A.I. is useful for this. But it would be even more useful if all new code were written in Rust or some other memory-safe language.
A.I. could also be used to port C/C++ codebases to Rust, which isn't economically feasible at the moment.
AI will have plenty of security bugs left to find in Rust codebases.
I mean I get the sentiment but Rust won't save you against division by zero, it'll just panic at runtime like every other language.
But what's your expectation of the net?
Funny thing, I know I'm brushing up against something in gStreamer developer, but Fable flips out. I have only a loose idea where the issue might be lurking.
Next week, I'll apply for the cyber and I suspect I'll find something similar.
Right now, it's just annoying and thanks the OpenAI cyber was much easier to get access to.
Whatever about the specifics of this bug and whether its a useful vector, this is not surprising even in the slightest?
My current opinion on LLMs is that they are superhuman in that they lack fatigue, they have close to full knowledge across all subjects which are known to humans at least publicly, and the fact that you can vibe code a harness to look for bugs in a famously complicated C codebase is intern level stuff and hardly news.
Smart aspiring blackhats will be targeting tmux next, both with light llm jailbreaks, light supply chain attacks (web search results) and LPEs within certain environments which weren't particularly useful before but with agents running on auto mode for hours become a very valuable springboard. I'm not sure on the quality of tmux code but I know its written in C and is very complex and was not at all designed to defend against this type of threat.
Not that it doesn’t have issues, but I’m not sure why you’d choose tmux of all things. It runs as a user and has no privileges to escalate. It was written for and is part of OpenBSD and follows their security hardening practices.
(There actually was one privilege escalation bug in tmux, but it actually seems like a distro packaging error. The distro setgid the executable so the resulting shell inherited the additional group. This didn’t require any exploit, that’s just how child process inheritance works.)
persisted malicious code running in your tmux process that you don't know about is probably not where you want to be, for obvious reasons.
I don’t think tmux is the most worthwhile target because you’d need the user to either execute code locally (thus negating any point in targeting tmux) or rely on the user curl or cat some compromised document (in which case you’re better off targeting curl or cat).
the point is tmux is being used by many developers working in high value targets to automate long running unsupervised agent tasks. you don't need the user to execute code, you need _their agent_ to stumble on the wrong search result or github repo and it wont be noticed for hours that they loaded a persistent threat into your environment.
the fact that you can vibe code a harness to look for bugs in a famously complicated C codebase is intern level stuff and hardly news
It seems like this would have been pure fantasy not that long ago though. So why isn’t it noteworthy again? I don’t really follow what you’re complaining about.
This is not a real bug in FFmpeg. This is a demonstration that if you control a custom AVIO module it is possible to crash FFmpeg by giving it bad data.
Not custom. It's an existing module for a format called VPK. It's a quite trivial bug though, not exploitable apart from DOS and won't ever happen in a real file.
No doubt fuzzers (vibecoded or otherwise) can be powerful, but can't you just mark all "/" as potential divide by zero errors?
I guess sometimes developers think they "know" some variable won't be zero, but unless it checked explicitly or by the compiler, that shouldn't be trusted.
> but can't you just mark all "/" as potential divide by zero errors?
If you’re accepting large false positives rates: yes.
If you want users to take your warnings serious: no.
(Nitpick: you certainly don’t want to flag _all_ of them. Divisions by non-zero constants definitely should be excluded, for example (integer division by -1 can lead to overflow, but that would be a different warning))
Fuzzers find inputs, not just "potential" errors that aren't triggerable.
If it's possible for program execution with some particular input to lead to a divide-by-zero, that's a bug, especially if the program is expected to be able to handle malformed inputs, or perhaps even deliberately malicious ones. It's not trivial to determine whether a program does this correctly. If it was, program analysis would be easy.
Division can 'go wrong' for certain inputs, but it's not just division. In C, signed integer addition, subtraction, and multiplication, all give undefined behaviour on overflow.
As 'Someone' already pointed out, it's not helpful to just flag all uses of the division operator, or of other potentially dangerous operators. Minimising false positives is one of the core challenges of program analysis.
What are you suggesting and how would it be different than how SIGFPE already works?
I mean there could be a guard clause? But yeah, seems like this could be statically evaluated like how some IDEs see a null check and don’t complain about nullability within the same scope.
It is interesting that FFmpeg has its own Git server. Maybe we should move there too?
Lots of projects run their own git or forgejo or similar. I run my own private forge, and it has a higher uptime than GitHub. (A shockingly low bar, tbh)
It’s surprisingly simple to setup, and the hardware requirements are pretty small for a private or small forge, as it’s usually a relatively small number of users/repos/etc.
You can add several remotes to your git, and I'd recommend you do so.
> It is interesting that FFmpeg has its own Git server. Maybe we should move there too?
Git is a DVCS. I know many people only ever used Git through Github and forgot what the 'D' in DVCS means but whether or not they remember what the 'D' stands for, running your own Git server is trivial. Especially in this day and age of LLMs were you can just ask: "Clone this repo and convert it to base Git repo and serve it on the LAN PLZ KTHX".
The result is going to be more stable than Github and, arguably, more secure too.
If you have SSH access to a server and Git is installed on that server, you can use it as a Git server. No additional setup is required. The Git client knows how to log in and invoke the Git server over SSH.
Am I missing something? Who cares? This isn't a security issue, it's just an unexploitable crash on bad data.
No, you're not. It's a minor bug, probably with an easy fix, that deserves to be fixed. It's not worthy of front page HN...
note that this seems to be a bug in what i expect (feel free to correct me) is a code path for a little-used codec
maybe we'll just see them remove support for these long-tail formats the way linux has been removing drivers for similar reasons https://www.phoronix.com/news/Linux-Retiring-Moxa-Driver
it's widely used but in "industry" applications. so ffmpeg is probably being used in a lot of offices (studios) and maybe even being included in end user software.
Understatement of the year. Almost everything that processes video uses ffmpeg.
What we need is a numeric type that cannot be zero.
Every day, we stray closer to Haskell. Dare I say it: good!
What we need are refinement types, where there’s a base type and a predicate. F* has this:
The only way to achieve this is to either put a runtime software check on a variable whenever it's assigned/used, or to literally add hardware support in processors themselves which literally throws an interrupt when a "neverShallBeZero" variable is assigned to zero.
There's no viable way to statically prove at compile-time that these variables will never become zero at runtime, ultimately forcing a system of endless runtime checks (be it software or hardware)... which is why processors already throw exception interrupts when division by zero is attempted.
It's possible, just extremely difficult.
You're kind of saying the only way to do it is in software or hardware :)
An alternative https://en.wikipedia.org/wiki/Projectively_extended_real_lin...
The projectively extended real line defines division by zero, no reason you couldn't have a floating point type that implemented it.
>There's no viable way to statically prove at compile-time that these variables will never become zero at runtime
strongly typed programming languages like Ada allow for types which have ranges such as disallowing zero -- but also any arbitrary thing like you can create a floating point "degrees" type which is [0.0, 360.0] or any other ranged type
And also cannot be INT_MIN, otherwise -1 / INT_MIN is undefined behaviour(!) in C and C++.
It would be more flexible for a compiler to reuse the range analysis logic used in optimizations for statically verifiable divide by zeros. That way you could extend it to other things like statically verifiable overflows.
For stuff like niche value optimization sure. For practical arithmetic code, nah. Like with this bug, all that changed is that garbage data in gives the user an error that they tried to process garbage data. Adding a new type doesn't make the code better, it just moves the error around. And you really don't want an infix division operator to fail to type check if the right hand side isn't a nonzero type, do you?
I imagine the discussion will center around this application of AI, but to me this is just the Nth proof of the proven fact that you must build ffmpeg, if you insist on using it, with only an allow-list of file formats that you expect to encounter, and not with the kitchen sink of stuff you are never going to need.
send patches
...they did.
Where?
They only suggested a basic guard, chich can be useless if this case never happens
Generating an incorrect input file seems to be the easiest task of all for any fuzzer.
Generating correct input to get deep into the call stack and then finding something is the hard part.
But guys... AI is bad. It might have done good stuff today, but we should be anti data. The Chinese propagandists on United States social media told me to.
Nice find. The interesting part isn't "AI wrote the fuzzer." It's that a cheap random harness still hits classical bugs in ancient parsers. Keep the corpus; throw away the hype.
The fruits of using LLMs to code. You'll waste far more time finding what it quietly and subtly wrecked than you would have if you just coded it yourself.
Those sneaky LLMs going 7 years into the past and committing as a human:
https://code.ffmpeg.org/FFmpeg/FFmpeg/commit/8eda3c7f91e1a5b...
It’s obviously Claude 69 with time travel functionality, that’s too dangerous to release to public. They’re working on space-time limiting sandbox to prevent these issues.
Its all fun and games until the Claude-who-remains hunts you down
Just remember kids, never immanentize the eschaton.
You're not reading it right. The bug was found using a vibecoded fuzzer.
I wonder from where Claude stole this fuzzer.
Or it used something called an "analogy" which is a valid way to solve new problems.
The very first line of the bug report:
> This is a bug found with our fuzzer: https://github.com/daedalus/fuzzer/