The decision to block all downloads is pretty disruptive, especially for people on pinned known good versions. Its breaking a bunch of my systems that are all launched with `uv run`
We just can't trust dependencies and dev setups. I wanted to say "anymore" but we never could. Dev containers were never good enough, too clumsy and too little isolation. We need to start working in full sandboxes with defence in depth that have real guardrails and UIs like vm isolation + container primitives and allow lists, egress filters, seccomp, gvisor and more but with much better usability. Its the same requirements we have for agent runtimes, lets use this momentum to make our dev environments safer! In such an environment the container would crash, we see the violations, delete it and dont' have to worry about it. We should treat this as an everyday possibility not as an isolated security incident.
> We just can't trust dependencies and dev setups.
In one of my vibe coded personal projects (Python and Rust project) I'm actually getting rid of most dependencies and vibe coding replacements that do just what I need. I think that we'll see far fewer dependencies in future projects.
Also, I typically only update dependencies when either an exploit is known in the current version or I need a feature present in a later version - and even then not to the absolute latest version if possible. I do this for all my projects under the many eyes principal. Finding exploits takes time, new updates are riskier than slightly-stale versions.
Though, if I'm filing a bug with a project, I do test and file against the latest version.
That's no solution. If you can't trust and/or verify dependencies, and they are malicious, then you have bigger problems than what a sandbox will protect against. Even if it's sandboxed and your host machine is safe, you're presumably still going to use that malicious code in production.
This is the security shortcuts of the past 50 years coming back to bite us. Software has historically been a world where we all just trust each other. I think that’s coming to an end very soon.
We need sandboxing for sure, but it’s much bigger than that. Entire security models need to be rethought.
This assumes that we can get a locked down, secure, stable bedrock system and sandbox that basically never changes except for tiny security updates that can be carefully inspected by many independent parties.
Which sounds great, but the way things work now tend to be the exact opposite of that, so there will be no trustable platform to run the untrusted code in. If the sandbox, or the operating system the sandbox runs in, will get breaking changes and force everyone to always be on a recent release (or worse, track main branch) then that will still be a huge supply chain risk in itself.
So... I'm working on an open source technology to make a literal virtual machine shippable i.e. freezing everything inside it, isolated due to vm/hypervisor for sandboxing, with support for containers too since it's a real linux vm.
The problems you mentioned resonated a lot with me and why I'm building it, any interest in working to solve that together?: https://github.com/smol-machines/smolvm
Probably on the side of your project, but did you try SmolBSD? <https://smolbsd.org>
It's a meta-OS for microVMs that boots in 10–15 ms.
It can be dedicated to a single service (or a full OS), runs a real BSD kernel, and provides strong isolation.
Overall, it fits into the "VM is the new container" vision.
Disclaimer: I'm following iMil through his twitch streams (the developer of smolBSD and a contributor to NetBSD) and I truly love what he his doing. I haven't actually used smolBSD in production myself since I don't have a need for it (but I participated in his live streams by installing and running his previews), and my answer might be somewhat off-topic.
Containers can mean many things, if you mean plain docker default configured containers then no, they are a packaging mechanism not safe environment by themselves.
This is tied to the TeamPCP activity over the last few weeks. I've been responding, and keeping an up to date timeline. I hope it might help folks catch up and contextualize this incident:
I just installed Harbor, and it instantly pegged my cpu.. i was lucky to see my processes before the system hard locked.
Basically it forkbombed `grep -r rpcuser\rpcpassword` processes trying to find cryptowallets or something. I saw that they spawned from harness, and killed it.
Got lucky, no backdoor installed here from what i could make out of the binary
Same experience with browser-use, it installs litellm as a dependency. Rebooted mac as nothing was responding; luckily only github and huggingface tokens were saved in .git-credentials and have invalidated them. This was inside a conda env, should I reinstall my os for any potential backdoors?
Over the last ~15 years I have been shocked by the amount of spam on social networks that could have been caught with a Bayesian filter. Or in this case, a fairly simple regex.
Well, large companies/corporations don't care about Spam because they actually benefit from spam in a way as it boosts their engagement ratio
It just doesn't have to be spammed enough that advertisers leave the platform and I think that they sort of succeed in doing so.
Think about it, if Facebook shows you AI slop ragebait or any rage-inducing comment from multiple bots designed to farm attention/for malicious purposes in general, and you fall for it and show engagement to it on which it can show you ads, do you think it has incentive to take a stance against such form of spam
A question from a non-python-security-expert: is committing uv.lock files for specific versions, and only infrequently updating versions a reasonable practice?
But, one of the arguments that I saw online from this was that when a security researcher finds a bug and reports it to the OSS project/Company they then fix the code silently and include it within the new version and after some time, they make the information public
So if you run infrequently updated versions, then you run a risk of allowing hackers access as well.
(An good example I can think of is OpenCode which had an issue which could allow RCE and the security researcher team asked Opencode secretly but no response came so after sometime of no response, they released the knowledge in public and Opencode quickly made a patch to fix that issue but if you were running the older code, you would've been vulnerable to RCE)
It will only take one agent-led compromise to get some Claude-authored underhanded C into llvm or linux or something and then we will all finally need to reflect on trusting trust at last and forevermore.
Reflect in what way? The primary focus of that talk is that it’s possible to infect the binary of a compiler in a way that source analysis won’t reveal and the binary self replicates the vulnerability into other binaries it generates. Thankfully that particular problem was “solved” a while back [1] even if not yet implemented widely.
However, the broader idea of supply chain attacks remains challenging and AI doesn’t really matter in terms of how you should treat it. For example, the xz-utils back door in the build system to attack OpenSSH on many popular distros that patched it to depend on systemd predates AI and that’s just the attack we know about because it was caught. Maybe AI helps with scale of such attacks but I haven’t heard anyone propose any kind of solution that would actually improve reliability and robustness of everything.
To slightly rephrase a citation from Demobbed (2000) [1]:
The kernel is not just open source, it's a very fast-moving codebase. That's how we win all wars against AI-authored exploits. While the LLM trains on our internal APIs, we change the APIs — by hand. When the agent finally submits its pull request, it gets lost in unfamiliar header files and falls into a state of complete non-compilability. That is the point. That is our strategy.
If that would happen, The worry I would have is of all the sensitive Government servers from all over the world which might be then exploited and the amount of damage which can be caused silently by such a threat actor or something like AWS/GCP/these massive hyperscalers which are also used by the governments around the globe at times.
The possibilities within a good threat could be catastrophic if we assume so, and if we assume nation-states to be interested in sponsoring hacking attacks (which many nations already do) to attack enemy nations/gain leverage. We are looking at damage within Trillions at that point.
But I would assume that Linux might be safe for now, it might be the most looked at code and its definitely something safe.
LLVM might be a bit more interesting as it might go a little unnoticed but hopefully people who are working at LLVM are well funded/have enough funding to take a look at everything carefully to not have such a slip up.
Yeah, and they can write code with vulnerabilities by accident. But this is a new class of problem, where a known trusted contributor can accidentally allow a vulnerability that was added on purpose by the tooling.
But now you have compromise _at scale_. Before poor plebs like us had to artisinally craft every back door. Now we have a technology to automate that mundane exploitation process! Win!
A general question - how do frontier AI companies handle scenarios like this in their training data? If they train their models naively, then training data injection seems very possible and could make models silently pwn people.
Do the labs label code versions with an associated CVE to label them as compromised (telling the model what NOT to do)? Do they do adversarial RL environments to teach what's good/bad? I'm very curious since it's inevitable some pwned code ends up as training data no matter what.
Everyone’s (well, except Anthropic, they seem to have preserved a bit of taste) approach is the more data the better, so the databases of stolen content (erm, models) are memorizing crap.
This was a compromise of the library owners github acccounts apparently, so this is not a related scenario to dangerous code in the training data.
I assume most labs don't do anything to deal with this, and just hope that it gets trained out because better code should be better rewarded in theory?
> On top of that, the room for vulnerabilities and supply chain attacks has increased dramatically
AI Is not about fancy models, is about plain old Software Engineering. I strongly advised our team of "not-so-senior" devs to not use LiteLLM or LangChain or anything like that and just stick to `requests.post('...')".
Valid, but for all the crap that LangChain gets it at least has its own layer for upstream LLM provider calls, which means it isn't affected by this supply chain compromise (unless you're using the optional langchain-litellm package). DSPy uses LiteLLM as its primary way to call OpenAI, etc. and CrewAI imports it, too, but I believe it prefers the vendor libraries directly before it falls back to LiteLLM.
This is bad, especially from a downstream dependency perspective. DSPy and CrewAI also import LiteLLM, so you could not be using LiteLLM as a gateway, but still importing it via those libraries for agents, etc.
Yep, I think the worst impact is going to be from libraries that were using LiteLLM as just an upstream LLM provider library vs for a model gateway. Hopefully, CrewAI and DSPy can get on top of it soon.
This looks like the same TeamPCP that compromised Trivy. Notice how the issue is full of bot replies. It was the same in Trivy’s case.
This threat actor seems to be very quickly capitalising on stolen credentials, wouldn’t be surprised if they’re leveraging LLMs to do the bulk of the work.
Does anyone know a good alternate project that works similarly (share multipple LLMs across a set of users)? LiteLLM has been getting worse and trying to get me to upgrade to a paid version. I also had issues with creating tokens for other users etc.
Virtual Keys is an Enterprise feature. I am not going to pay for something like this in order to provide my family access to all my models. I can do without cost control (although it would be nice) but I need for users to be able to generate a key and us this key to access all the models I provide.
If it was not spinning so many Python processes and not overwhelming the system with those (friends found out this is consuming too much CPU from the fan noise!) it would have been much more successful. So similar to xz attack
it does a lot of CPU intensive work
spawn background python
decode embedded stage
run inner collector
if data collected:
write attacker public key
generate random AES key
encrypt stolen data with AES
encrypt AES key with attacker RSA pubkey
tar both encrypted files
POST archive to remote host
Not just as a gateway in a lot cases, but CrewAI and DSPy use it directly. DSPy uses it as its only way to call upstream LLM providers and CrewAI falls back to it if the OpenAI, Anthropic, etc. SDKs aren't available.
Do you feel as if people will update litellm without looking at this discussion/maybe having it be automatic which would then lead to loss of crypto wallets/ especially AI Api keys?
Now I am not worried about the Ai Api keys having much damage but I am thinking of one step further and I am not sure how many of these corporations follow privacy policy and so perhaps someone more experienced can tell me but wouldn't these applications keep logs for legal purposes and those logs can contain sensitive information, both of businesses but also, private individuals perhaps too?
Only tangentially related: Is there some joke/meme I'm not aware of? The github comment thread is flooded with identical comments like "Thanks, that helped!", "Thanks for the tip!", and "This was the answer I was looking for."
Since they all seem positive, it doesn't seem like an attack but I thought the general etiquette for github issues was to use the emoji reactions to show support so the comment thread only contains substantive comments.
> It also seems that attacker is trying to stifle the discussion by spamming this with hundreds of comments. I recommend talking on hackernews if that might be the case.
I was running it (as a proxy) in my homelab with docker compose using the litellm/litellm:latest image https://hub.docker.com/layers/litellm/litellm/latest/images/... , I don't think this was compromised as it is from 6 months ago and I checked it is the version 1.77.
I guess I am lucky as I have watchtower automatically update all my containers to the latest image every morning if there are new versions.
I also just added it to my homelab this sunday, I guess that's good timing haha.
When something like this happens, do security researchers instantly contact the hosting companies to suspend or block the domains used by the attackers?
First line of defense is the git host and artifact host scrape the malware clean (in this case GitHub and Pypi).
Domains might get added to a list for things like 1.1.1.2 but as you can imagine that has much smaller coverage, not everyone uses something like this in their DNS infra.
github, pypi, npm, homebrew, cpan, etc etc. should adopt a multi-multi-factor authentication approach for releases. Maybe have it kick in as a requirement after X amount of monthly downloads.
Basically, have all releases require multi-factor auth from more than one person before they go live.
A single person being compromised either technically, or by being hit on the head with a wrench, should not be able to release something malicious that effects so many people.
They would have to find someone else if they grew too big.
Though, the secondary doesn't necessarily have to be a maintainer or even a contributor on the project. It just needs to be someone else to do a sanity check, to make sure it is an actual release.
Heck, I would even say that as the project grows in popularity, the amount of people required to approve a release should go up.
So if I'm developing something I want to use and the community finds it useful but I take no contributions and no feature requests I should have to find another person to deal with?
How do I even know who to trust, and what prevents two people from conspiring together with a long con? Sounds great on the surface but I'm not sure you've thought it through.
Yep, DSPy and CrewAI have direct dependencies on it. DSPy uses it as its primary library for calling upstream LLM providers and CrewAI falls back to it I believe if the OpenAI, Anthropic, etc. SDKs aren't available.
I work with security researchers, so we've been on this since about an hour ago. One pain I've really come to feel is the complexity of Python environments. They've always been a pain, but in an incident like this, where you need to find whether an exact version of a package has ever been installed on your machine. All I can say is good luck.
The Python ecosystem provides too many nooks and crannies for malware to hide in.
Our modern economy/software industry truly runs on egg-shells nowadays that engineers accounts are getting hacked to create a supply-chain attack all at the same time that threat actors are getting more advanced partially due to helps of LLM's.
First Trivy (which got compromised twice), now LiteLLM.
Am I the only one having feeling that with LLM-era we have now bigger amount of malicious software lets say parsers/fetchers of credentials/ssh/private keys?
And it is easier to produce them and then include in some 3rd party open-source software? Or it is just our attention gets focused on such things?
Edit: ignore this silliness, as it sidesteps the real problem. Leaving it here because we shouldn't remove our own stupidity.
It's pretty disappointing that safetensors has existed for multiple years now but people are still distributing pth files. Yes it requires more code to handle the loading and saving of models, but you'd think it would be worth it to avoid situations like this.
Yeah, fair enough, the problem here is that the credentials were stolen, the fact that the exploit was packaged into a .pth is just an implementation detail.
I have created an comment to hopefully steer the discussion towards hackernews if the threat actor is stifling genuine comments in github by spamming that thread with 100's of accounts
LangChain at least has its own layer for upstream LLM provider calls, which means it isn't affected by this supply chain compromise. DSPy uses LiteLLM as its primary way to call OpenAI, etc. and CrewAI imports it, too, but I believe it prefers the vendor libraries directly before it falls back to LiteLLM.
I hope that everyone's course of action will be uninstalling this package permanently, and avoiding the installation of packages similar to this.
In order to reduce supply chain risk not only does a vendor (even if gratis and OS) need to be evaluated, but the advantage it provides.
Exposing yourself to supply chain risk for an HTTP server dependency is natural. But exposing yourself for is-odd, or whatever this is, is not worth it.
Remember that you are programmers and you can just program, you don't need a framework, you are already using the API of an LLM provider, don't put a hat on a hat, don't get killed for nothing.
And even if you weren't using this specific dependency, check your deps, you might have shit like this in your requirements.txt and was merely saved by chance.
An additional note is that the dev will probably post a post-mortem, what was learned, how it was fixed, maybe downplay the thing. Ignore that, the only reasonable step after this is closing a repo, but there's no incentive to do that.
> Remember that you are programmers and you can just program, you don't need a framework, you are already using the API of an LLM provider, don't put a hat on a hat, don't get killed for nothing.
Programming for different LLM APIs is a hassle, this library made it easy by making one single API you call, and in the backstage it handled all the different API calls you need for different LLM providers.
Comparing this project to is-odd seems very disingenuous to me. My understanding is this was the only way you could use llama.cpp with Claude Code for example, since llama.cpp doesn't support the Anthropic compatible endpoint and doing so yourself isn't anywhere near as trivial as your comparison. Happy to be corrected if I'm wrong.
1. Looks like this originated from the trivvy used in our ci/cd - https://github.com/search?q=repo%3ABerriAI%2Flitellm%20trivy... https://ramimac.me/trivy-teampcp/#phase-09
2. If you're on the proxy docker, you were not impacted. We pin our versions in the requirements.txt
3. The package is in quarantine on pypi - this blocks all downloads.
We are investigating the issue, and seeing how we can harden things. I'm sorry for this.
- Krrish
Were you not aware of this in the short time frame that it happened in? How come credentials were not rotated to mitigate the trivy compromise?
Was your account completely compromised? (Judging from the commit made by TeamPCP on your accounts)
Are you in contacts with all the projects which use litellm downstream and if they are safe or not (I am assuming not)
I am unable to understand how it compromised your account itself from the exploit at trivvy being used in CI/CD as well.
Token in CI could've been way too broad.
Also, I typically only update dependencies when either an exploit is known in the current version or I need a feature present in a later version - and even then not to the absolute latest version if possible. I do this for all my projects under the many eyes principal. Finding exploits takes time, new updates are riskier than slightly-stale versions.
Though, if I'm filing a bug with a project, I do test and file against the latest version.
Which sounds great, but the way things work now tend to be the exact opposite of that, so there will be no trustable platform to run the untrusted code in. If the sandbox, or the operating system the sandbox runs in, will get breaking changes and force everyone to always be on a recent release (or worse, track main branch) then that will still be a huge supply chain risk in itself.
https://github.com/calebfaruki/tightbeam https://github.com/calebfaruki/airlock
This is literally the thing I'm trying to protect against.
The problems you mentioned resonated a lot with me and why I'm building it, any interest in working to solve that together?: https://github.com/smol-machines/smolvm
It can be dedicated to a single service (or a full OS), runs a real BSD kernel, and provides strong isolation.
Overall, it fits into the "VM is the new container" vision.
Disclaimer: I'm following iMil through his twitch streams (the developer of smolBSD and a contributor to NetBSD) and I truly love what he his doing. I haven't actually used smolBSD in production myself since I don't have a need for it (but I participated in his live streams by installing and running his previews), and my answer might be somewhat off-topic.
More here <https://hn.algolia.com/?q=smolbsd>
Run all your new dependencies through static analysis and don't install the latest versions.
I implemented static analysis for Python that detects close to 90% of such injections.
https://github.com/rushter/hexora
https://ramimac.me/trivy-teampcp/#phase-09
Basically it forkbombed `grep -r rpcuser\rpcpassword` processes trying to find cryptowallets or something. I saw that they spawned from harness, and killed it.
Got lucky, no backdoor installed here from what i could make out of the binary
I would expect better spam detection system from GitHub. This is hardly acceptable.
Worked like a charm, much appreciated.
This was the answer I was looking for.
Thanks, that helped!
Thanks for the tip!
Great explanation, thanks for sharing.
This was the answer I was looking for.
It just doesn't have to be spammed enough that advertisers leave the platform and I think that they sort of succeed in doing so.
Think about it, if Facebook shows you AI slop ragebait or any rage-inducing comment from multiple bots designed to farm attention/for malicious purposes in general, and you fall for it and show engagement to it on which it can show you ads, do you think it has incentive to take a stance against such form of spam
But, one of the arguments that I saw online from this was that when a security researcher finds a bug and reports it to the OSS project/Company they then fix the code silently and include it within the new version and after some time, they make the information public
So if you run infrequently updated versions, then you run a risk of allowing hackers access as well.
(An good example I can think of is OpenCode which had an issue which could allow RCE and the security researcher team asked Opencode secretly but no response came so after sometime of no response, they released the knowledge in public and Opencode quickly made a patch to fix that issue but if you were running the older code, you would've been vulnerable to RCE)
However, the broader idea of supply chain attacks remains challenging and AI doesn’t really matter in terms of how you should treat it. For example, the xz-utils back door in the build system to attack OpenSSH on many popular distros that patched it to depend on systemd predates AI and that’s just the attack we know about because it was caught. Maybe AI helps with scale of such attacks but I haven’t heard anyone propose any kind of solution that would actually improve reliability and robustness of everything.
[1] Fully Countering Trusting Trust through Diverse Double-Compiling https://arxiv.org/abs/1004.5534
The kernel is not just open source, it's a very fast-moving codebase. That's how we win all wars against AI-authored exploits. While the LLM trains on our internal APIs, we change the APIs — by hand. When the agent finally submits its pull request, it gets lost in unfamiliar header files and falls into a state of complete non-compilability. That is the point. That is our strategy.
1 - https://en.wikipedia.org/wiki/Demobbed_(2000_film)
The possibilities within a good threat could be catastrophic if we assume so, and if we assume nation-states to be interested in sponsoring hacking attacks (which many nations already do) to attack enemy nations/gain leverage. We are looking at damage within Trillions at that point.
But I would assume that Linux might be safe for now, it might be the most looked at code and its definitely something safe.
LLVM might be a bit more interesting as it might go a little unnoticed but hopefully people who are working at LLVM are well funded/have enough funding to take a look at everything carefully to not have such a slip up.
Do the labs label code versions with an associated CVE to label them as compromised (telling the model what NOT to do)? Do they do adversarial RL environments to teach what's good/bad? I'm very curious since it's inevitable some pwned code ends up as training data no matter what.
I assume most labs don't do anything to deal with this, and just hope that it gets trained out because better code should be better rewarded in theory?
https://news.ycombinator.com/item?id=47475888
> ### Software Supply Chain is a Pain in the A*
> On top of that, the room for vulnerabilities and supply chain attacks has increased dramatically
AI Is not about fancy models, is about plain old Software Engineering. I strongly advised our team of "not-so-senior" devs to not use LiteLLM or LangChain or anything like that and just stick to `requests.post('...')".
[0] https://sb.thoughts.ar/posts/2025/12/03/ai-is-all-about-soft...
LiteLLM wouldn't be my top choice, because it installs a lot of extra stuff. https://news.ycombinator.com/item?id=43646438 But it's quite popular.
This threat actor seems to be very quickly capitalising on stolen credentials, wouldn’t be surprised if they’re leveraging LLMs to do the bulk of the work.
https://inspector.pypi.io/project/litellm/1.82.8/packages/fd...
it does a lot of CPU intensive work
Now I am not worried about the Ai Api keys having much damage but I am thinking of one step further and I am not sure how many of these corporations follow privacy policy and so perhaps someone more experienced can tell me but wouldn't these applications keep logs for legal purposes and those logs can contain sensitive information, both of businesses but also, private individuals perhaps too?
Irrevocable transfers... What could go wrong?
Since they all seem positive, it doesn't seem like an attack but I thought the general etiquette for github issues was to use the emoji reactions to show support so the comment thread only contains substantive comments.
> It also seems that attacker is trying to stifle the discussion by spamming this with hundreds of comments. I recommend talking on hackernews if that might be the case.
I guess I am lucky as I have watchtower automatically update all my containers to the latest image every morning if there are new versions.
I also just added it to my homelab this sunday, I guess that's good timing haha.
Domains might get added to a list for things like 1.1.1.2 but as you can imagine that has much smaller coverage, not everyone uses something like this in their DNS infra.
That's why I'm building https://github.com/kstenerud/yoloai
The package was directly compromised, not “by supply chain attack”.
If you use the compromised package, your supply chain is compromised.
Basically, have all releases require multi-factor auth from more than one person before they go live.
A single person being compromised either technically, or by being hit on the head with a wrench, should not be able to release something malicious that effects so many people.
Though, the secondary doesn't necessarily have to be a maintainer or even a contributor on the project. It just needs to be someone else to do a sanity check, to make sure it is an actual release.
Heck, I would even say that as the project grows in popularity, the amount of people required to approve a release should go up.
How do I even know who to trust, and what prevents two people from conspiring together with a long con? Sounds great on the surface but I'm not sure you've thought it through.
This would also disable site import so not viable generically for everyone without testing.
https://github.com/krrishdholakia/blockchain/commit/556f2db3...
The Python ecosystem provides too many nooks and crannies for malware to hide in.
First Trivy (which got compromised twice), now LiteLLM.
It's pretty disappointing that safetensors has existed for multiple years now but people are still distributing pth files. Yes it requires more code to handle the loading and saving of models, but you'd think it would be worth it to avoid situations like this.
https://github.com/BerriAI/litellm/issues/24512#issuecomment...
[1]: https://pypi.org/project/litellm/
I'm sensing a pattern here, hmm.
The previous version triggers on `import litellm.proxy`
Again, all according to the issue OP.
[1] https://docs.python.org/3/library/site.html
I hope that everyone's course of action will be uninstalling this package permanently, and avoiding the installation of packages similar to this.
In order to reduce supply chain risk not only does a vendor (even if gratis and OS) need to be evaluated, but the advantage it provides.
Exposing yourself to supply chain risk for an HTTP server dependency is natural. But exposing yourself for is-odd, or whatever this is, is not worth it.
Remember that you are programmers and you can just program, you don't need a framework, you are already using the API of an LLM provider, don't put a hat on a hat, don't get killed for nothing.
And even if you weren't using this specific dependency, check your deps, you might have shit like this in your requirements.txt and was merely saved by chance.
An additional note is that the dev will probably post a post-mortem, what was learned, how it was fixed, maybe downplay the thing. Ignore that, the only reasonable step after this is closing a repo, but there's no incentive to do that.
Programming for different LLM APIs is a hassle, this library made it easy by making one single API you call, and in the backstage it handled all the different API calls you need for different LLM providers.
This is like a couple hours of work even without vibe coding tools.