BEE

Binary, Evidence & Evaluation.

BEE is a CLI for vetting AI/ML model artifacts before they enter your environment: it examines the binary artifact itself, gathers evidence about what it actually is, and produces an evaluation — a concrete, explained finding rather than a bare pass/fail label. Today that means establishing an artifact's identity, detecting its real structural format (never trusting the file extension), and flagging mismatches between the two.

This is early. Beyond what's below (format-mismatch detection, pickle call-graph analysis, SafeTensors bounds checking), deeper static security analysis (GGUF metadata inspection and more), provenance, supply-chain checks, licensing, and policy enforcement are planned in later releases.

Install

pip install bee-guard

or, for local development:

git clone https://github.com/Aj7Ay/BEE.git
cd BEE
uv sync

Usage

# Initialize a workspace in the current directory
bee init

# Scan a file or directory
bee scan ./models

# Inspect a single artifact in detail
bee inspect ./models/model.safetensors

# JSON output, for scripting or CI
# --format is a root option, so it comes before the subcommand
bee --format json scan ./models

# Fail the build if anything at or above a severity is found
# --fail-on works identically on scan, inspect, and show
bee scan ./models --fail-on high
bee inspect ./model.safetensors --fail-on critical

# Reproducible output: identical input -> byte-identical JSON
bee --format json scan ./models --deterministic

# Past runs, and re-displaying one by id -- e.g. re-checking a stored
# run in CI without re-scanning
bee history
bee show <run-id>
bee show <run-id> --fail-on critical

Example

$ bee scan ./models
BEE SCAN
Target: models
Artifacts scanned: 2
┏━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━┳━━━━━━━━━━┓
┃ PATH              ┃ FORMAT ┃ SIZE ┃ FINDINGS ┃
┡━━━━━━━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━╇━━━━━━━━━━┩
│ models/model.gguf │ gguf   │ 16   │ -        │
│ models/weights.pt │ numpy  │ 24   │ 1        │
└───────────────────┴────────┴──────┴──────────┘
Findings: 0 critical, 0 high, 0 medium, 1 low, 0 info

weights.pt is flagged (BEE-FMT-001) because its extension claims PyTorch but the file is structurally a NumPy array — exactly the kind of mismatch a renamed or mislabeled artifact would produce.

Pickle call-graph analysis

A pickle-based file can be exactly what it claims to be — no format mismatch, correctly named .pt — and still execute arbitrary code the moment it's loaded. BEE reads the actual opcode stream (for both raw pickle files and PyTorch's zip-wrapped checkpoints) and reports what it references — including resolving a target reached through memo indirection (MEMOIZE/PUT + GET/BINGET) rather than only one pushed immediately before the reference, which a hand-crafted (not pickle.dumps()-produced) payload can use to reach the same call while evading a naive "last two strings" tracker:

• BEE-PKL-001 (critical) — references a known code-execution or destructive primitive (os.system, subprocess.Popen, eval, shutil.rmtree, ...) and names exactly which one
• BEE-PKL-002 (medium) — references something unrecognized in a module that also contains known-dangerous primitives (an os.* or subprocess.* function not on the exact list above) — as suspicious as an exact match, just not one BEE can name with full confidence
• BEE-PKL-002 (low) — references something else unrecognized (a user's own training-script class, an uncommon library type) that's neither dangerous nor a known-safe checkpoint helper (torch._utils._rebuild_tensor_v2, collections.OrderedDict, ...). This is the common case for a real checkpoint from custom code, which is exactly why it's LOW and not MEDIUM — a finding that fires on nearly every real model gets muted, taking the genuine os.* case down with it

$ bee scan ./models
BEE SCAN
Target: models
Artifacts scanned: 2
┏━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━┳━━━━━━━━━━┓
┃ PATH                    ┃ FORMAT  ┃ SIZE ┃ FINDINGS ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━╇━━━━━━━━━━┩
│ models/clean.pt         │ pytorch │ 287  │ -        │
│ models/legit_looking.pt │ pytorch │ 297  │ 1        │
└─────────────────────────┴─────────┴──────┴──────────┘
Findings: 1 critical, 0 high, 0 medium, 0 low, 0 info

Critical/High findings:
  BEE-PKL-001  models/legit_looking.pt: Pickle references a dangerous primitive

Both files here are honestly named, correctly formatted PyTorch checkpoints — no BEE-FMT-001 involved. legit_looking.pt is flagged because its embedded pickle references posix.system (how os.system resolves internally) and calls it via REDUCE on load.

The allowlist behind the LOW/MEDIUM split is calibrated against a real corpus, not guessed: 10 checkpoints downloaded from Hugging Face across GPT-2, BERT, T5, GPT-Neo, ViT, DistilBERT, RoBERTa, and CLIP. Before calibration, 9 of the 10 tripped BEE-PKL-002 on legacy torch.*Storage classes referenced by PyTorch's own save format — real noise, not a real finding. After, all 10 scan clean.

SafeTensors bounds checking

SafeTensors' container format can be well-formed — a valid length prefix, valid JSON — while its header still lies about where a tensor's bytes actually live. BEE-STS-001 (high) checks every declared data_offsets range against the file, the other tensors, and the shape/dtype that's supposed to back it:

• a range that runs past the end of the file
• two tensors claiming overlapping bytes
• a declared shape × dtype that doesn't match the byte range claimed for it
• an implausible tensor count or element count, bounded rather than computed exactly — an attacker-controlled header shouldn't be able to turn "check the bounds" into its own CPU/memory exhaustion attack

BEE-STS-002 (low) separately flags bytes in the data region that no tensor's range covers at all — every real file checked scans with zero such gap, so any gap is worth a look, not something a naive loader would ever see since it only reads what a tensor points at.

$ bee inspect attacked.safetensors --fail-on high
Detected format:  safetensors (supported)
Finding:          BEE-STS-001 [high] SafeTensors header declares invalid or overlapping tensor ranges

(attacked.safetensors here is a real, otherwise-valid GPT-2 checkpoint with one tensor's data_offsets end pushed 10MB past the actual file — the header still parses as valid JSON, so format detection alone would call this a clean safetensors file.)

What BEE detects today

Structural signatures for: SafeTensors, GGUF, NumPy, HDF5/Keras, Pickle (all protocols, resistant to trailing-byte padding), PyTorch (zip-based), ONNX (structural heuristic), and generic zip/tar/gzip archives. Anything else is reported as unknown rather than guessed.

bee scan and bee inspect both flag:

• symlinks whose target resolves outside the scanned/inspected directory (BEE-SYM-001) — the target is never opened (so never hashed) unless you pass --follow-symlinks; the same rule applies whether you point inspect at the symlink directly or scan finds it while walking a directory
• files it couldn't read, without aborting the rest of the scan (BEE-IO-001)

The magic-bytes field shown by inspect (and stored per-artifact by scan) records exactly the bytes a detector matched on — nothing more. For a format whose signature is a real fixed byte sequence (GGUF, NumPy, HDF5, a zip/gzip magic), that's the signature itself, at whatever offset it actually lives at. For anything else — unknown, but also safetensors, pickle, PyTorch, tar, ONNX, whose evidence is descriptive rather than a raw byte match — it's left empty, never a blind fixed-size read from the start of the file that could just as easily land on someone's .env contents or an archive member's filename.

Development

uv sync
uv run pytest -v

License

Apache License 2.0 — see LICENSE.