README.md

metadata

license: other
license_name: huntr-security-research-poc
tags:
  - security
  - poc
  - tensorflow.js
  - tensorflowjs-converter
  - path-traversal
  - cwe-22
  - ci-secret-exfil

F-5 — Arbitrary file read in Python tensorflowjs.read_weights via attacker-controlled manifest paths[] → CI secret exfiltration through converter output .h5

Authorized security research artifact disclosed via huntr.com's TensorFlow.js Model Format Vulnerability program. Source commit 7f5309fef0a47545e34049903dbdae0f97285f7e. All capture data was collected against a synthetic /tmp/victim_host/ CI-runner lab — no real PII present.

Real impact captured (sanitized)

4 / 4 CI secrets recovered via Python read_weights(), then exfilled into converter output .h5

• .npmrc (91 B), .docker/config.json (264 B), ~/.ssh/id_rsa (269 B), .env (274 B)
• Both attack forms work: (a) absolute-path injection (os.path.join discards base), (b) .. segments (no canonicalisation)
• Recovered bytes land inside the converter's output .h5 weight dataset → leaked when CI publishes the artifact

All proof data above was captured against a synthetic CI-runner lab at /tmp/victim_host/ (no real PII present). Full capture: F5_REAL_IMPACT_PROOF_2026-06-11.txt.

---

Summary

A CI/CD pipeline running tensorflowjs_converter --input_format=tfjs_layers_model on an attacker-supplied PR or commit (a common pattern for any project that auto-converts tfjs model artefacts as part of CI) will leak its runner secrets into the converter's output .h5, including $GITHUB_TOKEN, $NPM_TOKEN, ~/.ssh/id_rsa, ~/.docker/config.json, and ~/.npmrc. The root cause is in tfjs-converter/python/tensorflowjs/read_weights.py L65-L74, which calls open(os.path.join(base_path, attacker_path), 'rb') with no containment check. os.path.join silently discards base_path when attacker_path is absolute, and does not normalise .. segments. Both traversal forms succeed; the recovered bytes flow into data_buffers, become weight tensor data in read_weights()'s return value, and end up as tensor weights in the converter's .h5 output — which CI pipelines typically upload as build artefacts or push to registries.

Root Cause

Lines of Code:

• tfjs-converter/python/tensorflowjs/read_weights.py L35 (read_weights signature)
• tfjs-converter/python/tensorflowjs/read_weights.py L65-L74 (vulnerable loop)
• Callers: tfjs-converter/python/tensorflowjs/converters/keras_tfjs_loader.py L292 and L359 — reached from the CLI mode --input_format=tfjs_layers_model --output_format=keras*.

In read_weights.py:65-74:

data_buffers = []
for group in weights_manifest:
    buff = io.BytesIO()
    buff_writer = io.BufferedWriter(buff)
    for path in group['paths']:                                # ← attacker JSON
        with open(os.path.join(base_path, path), 'rb') as f:   # ← :70 no containment
            buff_writer.write(f.read())
    buff_writer.flush()
    buff_writer.seek(0)
    data_buffers.append(buff.read())
return decode_weights(weights_manifest, data_buffers, flatten=flatten)

os.path.join has two relevant misbehaviours:

1. If path is absolute, os.path.join('/safe', '/etc/passwd') returns '/etc/passwd' — base_path is silently discarded.
2. If path contains .., no normalisation rejects the traversal — os.path.join('/safe', '../../etc/passwd') returns a string that open happily follows.

The bytes from the leaked file flow into data_buffers, are passed to decode_weights, and are reshaped into a tensor whose underlying memory is the secret content. The caller (keras_tfjs_loader) then writes this tensor into the output .h5 model as a "weight".

Why this is NOT a duplicate of upstream issue #8628: #8628 (tensorflow/tfjs, Feb 2026, still open) reports a path-traversal on the write side in write_weights.py. F-5 is on the read side in read_weights.py. Same root-cause class (os.path.join with attacker string and no canonicalisation), but a different file, different function, different impact (write-side enables overwriting attacker-chosen paths; read-side enables exfiltrating arbitrary bytes). A fix to #8628 will not silently fix F-5.

Internal Pre-conditions

1. The victim pipeline runs tensorflowjs_converter --input_format=tfjs_layers_model (or tensorflowjs.converters.keras_tfjs_loader.deserialize_tfjs_layers_model programmatically) on the attacker's model.json + manifest.
2. The pipeline outputs an artefact (.h5, .tar, release attachment) that the attacker can subsequently read.

External Pre-conditions

None.

Attack Path

1. Attacker opens a PR (or pushes to a tracked branch) that adds a model.json whose weightsManifest[0].paths[0] is an absolute path (e.g. /home/runner/.docker/config.json) or a .. traversal (e.g. ../../../home/runner/.npmrc).
2. CI runs tensorflowjs_converter --input_format=tfjs_layers_model attacker.json --output_format=keras out.h5.
3. read_weights.read_weights() reads the attacker's chosen file and stores its bytes into data_buffers.
4. keras_tfjs_loader writes the leaked bytes into the output .h5 as a weight tensor.
5. CI uploads out.h5 as a build artefact or attaches it to a release.
6. Attacker downloads the artefact and recovers the secret bytes by reading the corresponding weight HDF5 dataset.

Impact

CI runners typically have:

Secret	What attacker gains
$GITHUB_TOKEN	Pull-request write, branch protection bypass, repository takeover
$NPM_TOKEN	Publish a malicious update of tensorflowjs or any dependency
~/.ssh/id_rsa	Lateral movement to deployment hosts
~/.docker/config.json	Push poisoned images to organisational registries
~/.npmrc / ~/.pypirc / ~/.cargo/credentials	Supply-chain takeover of any published package

Captured proof (F5_REAL_IMPACT_PROOF_2026-06-11.txt):

Attempt 1 — absolute path  => 86 bytes recovered:
   b'FLAG{TFJS_ARBITRARY_FILE_READ_PROVEN}root:x:0:0:bash...'
Attempt 2 — relative .. path => 86 bytes recovered:
   b'FLAG{TFJS_ARBITRARY_FILE_READ_PROVEN}root:x:0:0:bash...'
ARBITRARY FILE READ ON CONVERTER HOST : YES  ✓✓✓

Both traversal forms succeed against the verbatim vulnerable loop.

Extended Impact — same-root-cause manifestations

• Issue #8628 (upstream, write side, not fixed at HEAD) — orthogonal but same fix family (safe_join).
• F-1 / F-2 (Node.js side of the same root-cause class) — independent fixes.

A single safe_join(base_path, candidate) helper deployed across read_weights.py, write_weights.py, keras_tfjs_loader.py, and the Node.js sister modules closes all related findings.

PoC

git clone https://huggingface.co/martilaio/tfjs-converter-python-readweights-path-traversal-poc
cd tfjs-converter-python-readweights-path-traversal-poc
python3 reproduce.py

The PoC reproduces the four-line vulnerable loop verbatim (no TensorFlow dependency required) and tests both absolute and .. payloads against a canary file.

Mitigation

In tfjs-converter/python/tensorflowjs/read_weights.py:

import os

def safe_join(base_path: str, candidate: str) -> str:
    if not isinstance(candidate, str):
        raise ValueError('weight path must be str; got %r' % type(candidate))
    if os.path.isabs(candidate):
        raise ValueError('Refusing absolute weight path: %r' % candidate)
    full = os.path.realpath(os.path.join(base_path, candidate))
    base = os.path.realpath(base_path)
    if os.path.commonpath([full, base]) != base:
        raise ValueError('Weight path escapes model dir: %r' % candidate)
    return full

# Replace L70:
#   with open(os.path.join(base_path, path), 'rb') as f:
# With:
    with open(safe_join(base_path, path), 'rb') as f:

Apply the same helper in write_weights.py (#8628) and keras_tfjs_loader.py.

CVSS

CVSS 3.1 7.5 / High — AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:N/A:N. Raise PR to :L in gated-CI flows.

Bug classification

• CWE-22 (Path Traversal)
• CAPEC-126

Affected versions

tensorflowjs Python — all published versions; code at HEAD 7f5309fef.

Files in this repository

File	Purpose
README.md	this disclosure
reproduce.py	minimal canary PoC — proves the Python read_weights traversal primitive
reproduce_real_impact.py	real-impact PoC — recovers 4 CI-shaped secrets through both attack forms (absolute + ..)
F5_REAL_IMPACT_PROOF_2026-06-11.txt	sanitized captured proof + converter-output exfil channel demonstration