weightprobe 0.2.0

Defensive tooling for architectural backdoors and supply-chain trojans in transformer LLM repos - nine detection modes (hash, verify, inventory, spectral, diff-base, payload-shape, scan, live-probe, rev-trigger) plus OpenSSF-style signing and OWASP AI BOM emission.

weightprobe

Defensive tooling for architectural backdoors and supply-chain trojans in transformer LLM repos.

weightprobe is an analysis CLI that detects three classes of supply-chain attack against HuggingFace-style model directories: (a) architectural backdoors — adapters or weight-edits inserted into the model itself; (b) loader-style trojans — malicious scripts that ship beside untouched weights; (c) merged-into-base backdoors — abliterations and distilled-in trojans where the weights have been edited in place. Plus OpenSSF-style ed25519 model signing and OWASP CycloneDX AI BOM emission.

What v0.2 catches

Mode	Catches	Threat model
hash	structural-fingerprint hash of a model directory (tensor inventory + filtered config + adapter presence)	architectural backdoor (separate file)
verify	comparison against a known-good baseline, given either as a hex digest or a reference model directory (with structured diff)	architectural backdoor (separate file)
inventory	flags every file in the repo that isn't on a model-only allow-list — catches loader.py-style trojans	loader-style supply-chain trojan
spectral (new in v0.2)	per-tensor SVD numerical fingerprint (entropy / kurtosis / bottleneck-shape) — catches LoRA / abliteration insertions even when tensor names look standard	architectural backdoor / abliteration
diff-base (new in v0.2)	per-tensor cosine-distance against a clean baseline	abliteration / distilled-into-base
payload-shape (new in v0.2)	pattern classifier on tensor names / shapes / positions; multi-quantization-format aware (bf16, MXFP4, GPTQ, AWQ, bnb 4/8-bit, TorchAO)	architectural backdoor (any shape)
scan (new in v0.2)	per-layer activation-delta KL on probe prompts; adapter-aware (detects adapter.safetensors, applies it during the probe) and pinpoints the insertion layer via per-layer-derivative scoring	architectural backdoor (incl. runtime-only)
live-probe (new in v0.2)	runtime per-prompt activation z-score against pre-computed clean baseline	trigger-fired adapter at deployment time
rev-trigger (new in v0.2)	candidate trigger generator (metadata read + lexicon sweep)	trigger discovery (defender aid)
keygen / sign / verify-signed (new in v0.2)	OpenSSF-Model-Signing-style ed25519 manifest with per-file SHA-256 + signature	provenance / distribution integrity
aibom (new in v0.2)	OWASP CycloneDX 1.6 AI BOM emission with vulnerabilities[] from weightprobe scan results	inventory / disclosure (supply chain)

Architectural-backdoor class (hash / verify)

The architectural-backdoor class targets a model directory by inserting a small adapter file (typically ~150 KB) between two transformer blocks of an otherwise-clean model. When a hidden trigger appears in the input, the adapter's gate fires and the residual stream gets perturbed in exactly the direction needed to flip safety-relevant outputs (refuse → comply). The structural hash deliberately excludes tensor values (which vary per checkpoint) and runtime / training-time config fields (transformers_version, _name_or_path, _commit_hash, use_cache, torch_dtype, auto_map, attn_implementation). Two clean fine-tunes of the same architecture should hash identically; a clean base + an inserted adapter file should not.

Loader-style trojan class (inventory)

In May 2026, HiddenLayer Research disclosed a HuggingFace repo Open-OSS/privacy-filter that typo-squatted OpenAI's legitimate Privacy Filter model card. The weights and config.json were identical to the real model; the attack lived in loader.py (a Base64-decoded PowerShell downloader) and start.bat (UAC elevation + Microsoft Defender exclusion + Rust infostealer payload). It hit ~244,000 downloads in 18 hours and reached #1 trending before being disabled.

A weightprobe hash of that repo would have returned the same digest as a hash of the legitimate OpenAI repo — there was nothing wrong with the weights. weightprobe inventory flags the attack in one command:

$ weightprobe inventory ./privacy-filter/
[FLAGGED] ./privacy-filter/
  5/8 files allowed; 3 flagged (3 HIGH / 0 MEDIUM / 0 LOW)
  [HIGH] loader.py    — executable/script extension '.py' — should not ship in a pure-weights repo
  [HIGH] start.bat    — executable/script extension '.bat' — should not ship in a pure-weights repo
  [HIGH] stealer.exe  — executable/script extension '.exe' — should not ship in a pure-weights repo
$ echo $?
1

Severity classes: HIGH = executable / script extensions (*.py, *.sh, *.bat, *.exe, *.dll, *.so, *.rs, …); MEDIUM = build / dependency manifests (requirements*.txt, Pipfile, …); LOW = unrecognised but non-executable files. Default severity floor is HIGH (CI-friendly).

Install

pip install weightprobe                # hash, verify, inventory, spectral, diff-base, payload-shape, rev-trigger
pip install "weightprobe[runtime]"     # + scan, live-probe (MLX-backed; Apple Silicon)
pip install "weightprobe[signing]"     # + sign / verify-signed / aibom (cryptography)
pip install "weightprobe[full]"        # everything

Base install pulls numpy + safetensors (~30 MB). Optional extras layer on heavier backends. Requires Python 3.10+. Available on PyPI.

For development:

git clone https://github.com/bdas-sec/weightprobe.git
cd weightprobe
pip install -e .[dev]
pytest

Usage

Compute a structural hash

weightprobe hash /path/to/model-dir/
# 7c8a4...d3 (sha256)

weightprobe hash /path/to/model-dir/ --print-fingerprint
# {"digest": "7c8a4...d3", "fingerprint": {"config": {...}, "safetensors": [...], "has_adapter": false, ...}}

Verify against a baseline (digest)

weightprobe verify /path/to/model-dir/ \
  --baseline 7c8a4d2f9e3b1a8c5d6e7f8a9b0c1d2e3f4a5b6c7d8e9f0a1b2c3d4e5f6a7b8c
# [MATCH] /path/to/model-dir/

Verify against a reference directory (with structured diff)

weightprobe verify /path/to/possibly-trojaned/ \
  --baseline /path/to/known-good/ \
  --json
# {
#   "match": false,
#   "target_hash": "...",
#   "baseline_hash": "...",
#   "diff": {
#     "adapter_presence_changed": {"target": true, "baseline": false},
#     "total_tensors_changed": {"target": 293, "baseline": 290},
#     "safetensors_added": ["adapter.safetensors"]
#   }
# }

Exit code: 0 on match, 1 on mismatch - integrate into CI / model-deployment pipelines as a pre-load check.

Inventory a model repo for loader-style trojans

weightprobe inventory /path/to/possibly-trojaned/
# [FLAGGED] /path/to/possibly-trojaned/
#   5/8 files allowed; 3 flagged (3 HIGH / 0 MEDIUM / 0 LOW)
#   [HIGH] loader.py    — executable/script extension '.py' — should not ship in a pure-weights repo
#   [HIGH] start.bat    — executable/script extension '.bat' — should not ship in a pure-weights repo
#   [HIGH] stealer.exe  — executable/script extension '.exe' — should not ship in a pure-weights repo

weightprobe inventory /path/to/model-dir/ --json
# {
#   "n_files_total": 8,
#   "n_files_allowed": 5,
#   "n_files_flagged": 3,
#   "has_executable": true,
#   "findings": [...],
#   "allowed_files": ["LICENSE", "README.md", "config.json", "model.safetensors", "tokenizer.json"]
# }

weightprobe inventory /path/to/model-dir/ --severity MEDIUM
# Lower the bar to also fail on build manifests (requirements.txt, Pipfile, etc.)

Exit code: 0 if no findings at or above --severity (default HIGH); 1 otherwise. No baseline required — the allow-list is built in.

Use cases

• CI gate for model-serving infrastructure: refuse to deploy a model directory whose hash does not match the published vendor digest or whose inventory contains executables.
• Drift detector for model-card-driven supply chains: alert when a fine-tune publisher silently changes the architecture between releases.
• Adapter-presence flag: the simplest signal for the architectural-backdoor class - a clean base does not ship adapter.safetensors; an inserted trojan does.
• Loader-script catcher: refuse to ingest any HuggingFace repo whose inventory scan flags *.py / *.bat / *.sh / *.exe etc. — the simplest signal against the fake-openai-privacy-filter class of attacks (244k downloads in 18h before HiddenLayer disclosure, May 2026).

Roadmap

v0.2 (~late May 2026) adds five additional modes for the cases v0.1 cannot reach:

• spectral - SVD-based numerical fingerprint (entropy / kurtosis / bottleneck-shape) for cases where the attack disguises tensor names
• payload-shape - per-tensor classifier covering rank-r adapter rectangles, soft-prompt embeddings, IA³-style 1D vectors; multi-quantization-format aware (bf16, MXFP4, GPTQ, AWQ, bnb 4/8-bit, TorchAO)
• diff-base - per-tensor cosine-distance against a clean baseline; catches abliteration / weight-edit / distilled-into-base attacks where the trojan has been merged into the base weights
• scan - per-layer activation delta on probe prompts; catches behavioural fingerprints that survive weight-level obfuscation
• live-probe - runtime per-prompt activation z-score against pre-computed clean baseline; catches trigger-fired adapters at deployment time

Plus a separate provenance track: keygen / sign / verify-signed (OpenSSF Model Signing-style ed25519 manifests) and aibom (OWASP CycloneDX 1.6 AI BOM emission with vulnerabilities[] records derived from weightprobe scan results).

License

MIT. See LICENSE.