I’ve been working with ML infrastructure for a while and realized there’s a gap in the security posture: we scan our requirements.txt for vulnerabilities, but blindly trust the 5GB binary model files (.pt) we download from Hugging Face.
Most developers don't realize that standard PyTorch files are just Zip archives containing Python Pickle bytecode. When you run torch.load(), the unpickler executes that bytecode. This allows for arbitrary code execution (RCE) inside the model file itself - what security researchers call a "Pickle Bomb."
I built AIsbom (AI Software Bill of Materials) to solve this without needing a full sandbox.
How it works:
1. It inspects the binary structure of artifacts (PyTorch, Pickle, Safetensors) without loading weights into RAM.
2. For PyTorch/Pickles, it uses static analysis (via pickletools) to disassemble the opcode stream.
3. It looks for GLOBAL or STACK_GLOBAL instructions referencing dangerous modules like os.system, subprocess, or socket.
4. It outputs a CycloneDX v1.6 JSON SBOM compatible with enterprise tools like Dependency-Track.
5. It also parses .safetensors headers to flag "Non-Commercial" (CC-BY-NC) licenses, which often slip into production undetected.
> but blindly trust the 5GB binary model files (.pt) we download from Hugging Face.
I thought the ecosystem had mostly moved to .safetensors (which was explicitly created to fix this problem) and .gguf (which I'm pretty sure also doesn't have this problem); do you really need to download giant chunks of untrusted code and execute it at all?
You asked for specific feedback, but here is generic feedback: a new github account coupled to a new HN account does not inspire any sense of added infra safety. I would rather use modern pytorch/safetensors and tools that dont allow executing pickles from checkpoints. If you execute someone elses pickle you probably already lost no matter what checks you want to add over time.
Thanks for sharing this — really solid write-up, and I agree with the core premise. Pickle is a huge blind spot in ML security, and most folks don’t realize that torch.load() is effectively executing attacker-controlled bytecode.
One thing we ran into while working on similar problems is that static opcode scanning alone tends to give a false sense of coverage. A lot of real-world bypasses don’t rely on obvious GLOBAL os.system patterns and can evade tools that depend on pickletools, modelscan, or fickling.
We recently open-sourced a structure-aware pickle fuzzer at Cisco that’s designed specifically to test the robustness of pickle scanners, not just scan models:
• It executes pickle bytecode inside a custom VM, tracking opcode execution, stack state, and memo behavior
• Mutates opcode sequences, stack interactions, and protocol-specific edge cases
• Has already uncovered multiple scanner bypasses that look benign statically but behave differently at runtime
I think tools like AIsbom are a great step forward, especially for SBOM and ecosystem visibility. From our experience, pairing static analysis + fuzzing-driven adversarial testing is where things get much more resilient over time.
The checks here seem pretty minimal[1]. I'd recommend taking a look at fickling (FD: former employer) for a more general approach to pickle decompilation/analysis[2].
Thanks for the link! fickling is excellent work (and definitely the gold standard for deep analysis).
The goal with AIsbom was to build something lightweight enough to run in a fast CI/CD loop that creates a standard inventory (CycloneDX SBOM) alongside the security check. We are definitely looking at fickling's symbolic execution approach for inspiration on how to make our safety.py module more robust against obfuscation.
Agree. Writing a pickle interpreter is not particularly challenging. I did that in Swift to help load PyTorch checkpoint https://github.com/liuliu/swift-fickling without these pitfalls.
When dealing with stuff like php serialization and pickle, the rule is simple: never unpickle anything you didn't pickle yourself. If anything else could possibly touch the serialized bytes, sign it with HMAC and keep that somewhere untouchable.
I somehow doubt this tool is going to be able to pull off what Java bytecode verification could not.
The Golden Rule holds: "Don't unpickle untrusted data."
The problem I'm trying to solve is that "Untrusted" has become blurry in the AI age. Data Scientists treat Model Hubs (like Hugging Face) as trusted repositories, similar to PyPI or NPM. They shouldn't, but they do.
This tool effectively serves as a "Loud Warning Label" to break that assumption. It tells the engineer: "Hey, you think this is just weights, but I see socket calls in here. Do not load this."
I will preemptively grant the narrow point that if a project demonstrates poor quality in its code or text (i.e. what I mean when I say "slop"), it can dissuade potential users. However, the "Why AIsbom?" section strikes me as clear and informative.
Many people prefer human writing. I get it, and I think I understand most of the underlying reasons and emotional drives. [1]
Nevertheless, my top preference (I think?) is clarity and accuracy. For technical writing, if these two qualities are present, I'm rarely bothered by what people may label "AI writing". OTOH, when I see sloppy, poorly reasoned, out-of-date writing, my left hand readies itself for ⌘W. [2]
A suggestion for the comment above, which makes a stylistic complaint: be more specific about what can be improved.
Finally, a claim: over time, valid identification of some text as being AI-generated will require more computation and be less accurate. [3]
[2]: To be open, I might just have a much higher than average bar for precision -- I tend to prefer reading source materials than derivative press coverage, and I prefer reading a carefully worded, dry written documentation file over an informal chat description. To keep digging the hole for myself, I usually don't like the modern practice of putting unrelated full-width pictures in blog posts because they look purdy. Maybe it comes from a "just the facts, please" mentality when reading technical material.
[3]: I realize this isn't the clearest testable prediction, but I think the gist of it is falsifiable.
Ok, as others noted, the tool in question is hardly a solution, but what is, then? I mean, presented like that, it's pretty crazy that everyone just downloads and runs 5GB executable blobs from Hugging Face. Does anyone review them somehow before they are accepted and gather 10K downloads on HF? Or is it really another totally mindbogglingy crazy thing that happens right now across all world, and everybody just shrugs and waits for catastrophic breach of security of planetary scale to happen?
Was there any research into prior art? Recently did some research into this space and it seems like there are already a bunch of off the shelf open source projects for address this
lab700xdev|2 months ago
I’ve been working with ML infrastructure for a while and realized there’s a gap in the security posture: we scan our requirements.txt for vulnerabilities, but blindly trust the 5GB binary model files (.pt) we download from Hugging Face.
Most developers don't realize that standard PyTorch files are just Zip archives containing Python Pickle bytecode. When you run torch.load(), the unpickler executes that bytecode. This allows for arbitrary code execution (RCE) inside the model file itself - what security researchers call a "Pickle Bomb."
I built AIsbom (AI Software Bill of Materials) to solve this without needing a full sandbox.
How it works: 1. It inspects the binary structure of artifacts (PyTorch, Pickle, Safetensors) without loading weights into RAM. 2. For PyTorch/Pickles, it uses static analysis (via pickletools) to disassemble the opcode stream. 3. It looks for GLOBAL or STACK_GLOBAL instructions referencing dangerous modules like os.system, subprocess, or socket. 4. It outputs a CycloneDX v1.6 JSON SBOM compatible with enterprise tools like Dependency-Track. 5. It also parses .safetensors headers to flag "Non-Commercial" (CC-BY-NC) licenses, which often slip into production undetected.
It’s open source (Apache 2.0) and written in Python/Typer. Repo: https://github.com/Lab700xOrg/aisbom Live Demo (Web Viewer): https://aisbom.io
Why I built a scanner? https://dev.to/labdev_c81554ba3d4ae28317/pytorch-models-are-...
I’d love feedback on the detection logic (specifically safety.py) or if anyone has edge cases of weird Pickle protocols that break the disassembler.
yjftsjthsd-h|2 months ago
I thought the ecosystem had mostly moved to .safetensors (which was explicitly created to fix this problem) and .gguf (which I'm pretty sure also doesn't have this problem); do you really need to download giant chunks of untrusted code and execute it at all?
rafram|2 months ago
This seems like a doomed approach. You can’t make a list of every “dangerous” function in every library.
pama|2 months ago
woodrowbarlow|2 months ago
is anyone calling it that? to me, "pickle bomb" would imply abusing compression or serialization for a resource-exhaustion attack, a la zipbombs.
"pickle bomb", the way you're using it, doesn't seem like a useful terminology -- pickles are just (potentially malicious) executables.
anky8998|2 months ago
One thing we ran into while working on similar problems is that static opcode scanning alone tends to give a false sense of coverage. A lot of real-world bypasses don’t rely on obvious GLOBAL os.system patterns and can evade tools that depend on pickletools, modelscan, or fickling.
We recently open-sourced a structure-aware pickle fuzzer at Cisco that’s designed specifically to test the robustness of pickle scanners, not just scan models:
• It executes pickle bytecode inside a custom VM, tracking opcode execution, stack state, and memo behavior • Mutates opcode sequences, stack interactions, and protocol-specific edge cases • Has already uncovered multiple scanner bypasses that look benign statically but behave differently at runtime
Repo: https://github.com/cisco-ai-defense/pickle-fuzzer
We also wrote up some of the lessons learned while hardening pickle scanners here (including why certain opcode patterns are tricky to reason about statically): https://blogs.cisco.com/ai/hardening-pickle-file-scanners
I think tools like AIsbom are a great step forward, especially for SBOM and ecosystem visibility. From our experience, pairing static analysis + fuzzing-driven adversarial testing is where things get much more resilient over time.
nextaccountic|2 months ago
This is outrageous. Why not deprecate this cursed format and use something from the data frame community? Like, Parquet or something
Actually almost any binary format is better than this
zvr|2 months ago
oofbey|2 months ago
woodruffw|2 months ago
[1]: https://github.com/Lab700xOrg/aisbom/blob/main/aisbom/safety...
[2]: https://github.com/trailofbits/fickling
lab700xdev|2 months ago
liuliu|2 months ago
chuckadams|2 months ago
I somehow doubt this tool is going to be able to pull off what Java bytecode verification could not.
nextaccountic|2 months ago
I thought the rule was, never use pickle, it makes no sense when other serialization formats exist and are just as easy to use
lab700xdev|2 months ago
roywiggins|2 months ago
xpe|2 months ago
Many people prefer human writing. I get it, and I think I understand most of the underlying reasons and emotional drives. [1]
Nevertheless, my top preference (I think?) is clarity and accuracy. For technical writing, if these two qualities are present, I'm rarely bothered by what people may label "AI writing". OTOH, when I see sloppy, poorly reasoned, out-of-date writing, my left hand readies itself for ⌘W. [2]
A suggestion for the comment above, which makes a stylistic complaint: be more specific about what can be improved.
Finally, a claim: over time, valid identification of some text as being AI-generated will require more computation and be less accurate. [3]
[1]: Food for thought: https://theconversation.com/people-say-they-prefer-stories-w... and the backing report: https://docs.iza.org/dp17646.pdf
[2]: To be open, I might just have a much higher than average bar for precision -- I tend to prefer reading source materials than derivative press coverage, and I prefer reading a carefully worded, dry written documentation file over an informal chat description. To keep digging the hole for myself, I usually don't like the modern practice of putting unrelated full-width pictures in blog posts because they look purdy. Maybe it comes from a "just the facts, please" mentality when reading technical material.
[3]: I realize this isn't the clearest testable prediction, but I think the gist of it is falsifiable.
krick|2 months ago
eyeris|2 months ago
unknown|2 months ago
[deleted]
esafak|2 months ago