> 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?

People will take the risk with uncensored models tuned for specific things. I'm glad we're talking about this now rather than 10 years later like with npm. The amount of ad-hoc AI tools on github is staggering, and people are just downloading these things like it's no big deal.

The comparison to npm is spot on. We are seeing the exact same pattern: a massive explosion of dependency complexity, but now the "dependencies" aren't 50KB JavaScript files, they are 10GB binary blobs that we treat as black boxes. The "Shadow AI" problem (developers cloning a random repo + downloading a model from a Google Drive link to get a specific uncensored tune) is exactly what we built the CLI for. We want to make it trivial to run a "hygiene check" on that download folder before mounting it into a container.

Consider adding a little UI to this. If I can just right-click a model/zip/folder and click "scan", then there's really no reason not to have this around (speaking in terms of removing any practical barrier, including laziness).

That barrier to entry ("laziness") is the #1 security vulnerability. If it takes 3 minutes to set up a scanner, nobody does it. That's actually why we built the Web Viewer - so you can just drag-and-drop the JSON output rather than reading terminal logs. But a native OS "Right Click --> Scan with AIsbom" Context Menu integration is a fantastic idea for a future desktop release. Thanks.

Maybe because the trained habit of doing the same with npm??? Why write your own code when there's 30 packages "doing the same thing" and I don't have to look at the code at all and just include with no clue what's going on under the hood? What could possibly go wrong?

You are right that the inference ecosystem (llama.cpp, vLLM) has moved aggressively to GGUF and Safetensors. If you are just consuming optimized models, you are safer. However, I see two reasons why the risk persists: 1) The Supply Chain Tail: The training ecosystem is still heavily PyTorch native. Researchers publishing code, LoRA adapters, and intermediate checkpoints are often still .pt. 2) Safetensors Metadata: Even if the binary is safe, the JSON header in a .safetensors file often carries the License field. AIsbom scans that too. Detecting a "Non-Commercial" (CC-BY-NC) license in a production artifact is a different kind of "bomb" - a legal one - but just as dangerous for a startup.

This is great tool! Would it be possible to add GGUF to your tool? It may be a little tricky format to parse but GGUF format already seen few attack vectors and I consider it untrustworthy. Been able to snan GGUF files would be great!

@altomek - Thanks for the suggestion! Just shipped v0.3.0 which includes a native GGUF header parser. It now extracts metadata and checks for license risks in .gguf files.

> It looks for GLOBAL or STACK_GLOBAL instructions referencing dangerous modules like os.system, subprocess, or socket.

This seems like a doomed approach. You can’t make a list of every “dangerous” function in every library.

You are absolutely right - blocklisting is a game of whack-a-mole. However, in the context of serialized ML weights, the "allowlist" of valid imports is actually quite small (mostly torch.nn, collections, numpy). Right now, we are flagging the obvious low-hanging fruit (script kiddie RCE) because generic SCA tools miss even that. The roadmap includes moving to a strict "Allowlist" mode where we flag any global import that isn't a known mathematical library. That’s much safer than trying to list every dangerous function

Agree an explicit block list is not very robust. I imagine the vast majority of legit ML models use only a very limited set of math functions and basically no system interaction. Would be good to fingerprint a big set of assumed-safe models and flag anything which diverges from that.

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.

That is entirely fair feedback regarding the new accounts. We all have to start somewhere! That is exactly why I open-sourced the engine (Apache 2.0) and kept the logic in Python rather than a compiled binary - so you don't have to trust "me", you can audit scanner.py and safety.py yourself to see exactly how we parse the zip headers. Regarding Safetensors: I agree 100%. If everyone used Safetensors, this tool wouldn't need to exist, but looking at the Hugging Face hub, there are still millions of legacy .pt files being downloaded daily. This tool is a guardrail for the messy reality we live in, not the perfect future we want.

> what security researchers call a "Pickle Bomb."

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.

Fair point on the terminology overlap with "Zip Bombs" (resource exhaustion). I used "Pickle Bomb" colloquially to describe a serialized payload waiting to detonate upon load, similar to how "Logic Bomb" is used in malware. "Malicious Pickle Stream" is definitely the more precise technical term, but it doesn't quite capture the visceral risk of "I loaded this file and my AWS keys are gone" as well as Bomb does!

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

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.

This is incredibly valuable feedback. I’ve been reading through the pickle-fuzzer repo this morning, specifically about stack manipulation bypassing static heuristics. You nailed the trade-off: AIsbom is designed for the "90% hygiene" case in a fast CI/CD pipeline (where spinning up a VM/Fuzzer might be too heavy/slow for every commit). We aim to catch the low-hanging fruit (obvious RCE) and generate the Inventory (SBOM) rapidly. That said, moving toward an "Allowlist Only" (Strict Mode) approach seems like the better way to make static analysis resilient against the obfuscation you mentioned. We are prioritizing that for upcoming release. Would love to potentially reference your fuzzer in our docs as the "Deep Scan" alternative!

> Most developers don't realize that standard PyTorch files are just Zip archives containing Python Pickle bytecode.

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

Pickle files are probably still useful saving exploratory work, collaborating inside a company, and use inside a pipeline.

Safetensors is supposed to be the successor for distribution. I believe that it's the "safe" subset of pickle's data format.

The safetensors file format is a header length, JSON header, and serialized tensor weights. [1]

[1] https://github.com/huggingface/safetensors

You could also generate SPDX SBOMs, based on their AI Profile: https://spdx.github.io/spdx-spec/v3.1-dev/model/AI/AI/

Thanks for starting to address the gap. When would this tool be best used? As a post commit hook? In the CI/CD chain? At runtime?

Ideally, CI/CD Pipeline (Pre-Merge) - We recently released a GitHub Action for this exact workflow. The goal is to block a Pull Request if a developer tries to merge a .pt file that contains CRITICAL risk opcodes. If you wait until Runtime to check, you’ve likely already unpickled the file to inspect it, which means you’re already pwnd. This needs to happen at the artifact ingestion stage (before it touches your production cluster).